European Psychologist

Volume 24 / Number 1 / 2019

Volume 24 / Number 1 / 2019

European Psychologist

European Psychologist

Editor-in-Chief Peter Frensch Managing Editor Kristen Lavallee Associate Editors Ulrike Ehlert Alexandra Freund Katariina Salmela-Aro

Official Organ of the European Federation of Psychologists’ Associations (EFPA)

Special Issue Adjustment to Chronic Illness Guest Editors Maria João Figueiras and David Dias Neto

Behavioral sciences for the next generation of health care providers

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“This latest edition continues the editors’ outstanding success in bridging the exciting worlds of the physiological and behavioral-social contributions to quality health care.”

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Olle Jane Z. Sahler / John E. Carr / Julia B. Frank / João V. Nunes (Editors)

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A clearly structured, compact text on the behavioral sciences for medical and other health professions, including practical features such as chapter review questions and an annotated practice exam.

pulsive and related disorders, and theories of social relations and interprofessional collaboration as well as a new appendix on psychological testing as well as integrating the new classification of the DSM-5.

This edition of this easy-to-use text presents succinct information about the wide variety of social and psychological sciences that interact with the biological sciences in contributing to health and illness. Based around but expanding on the Integrated Sciences Model, and focusing on the Institute of Medicine’s key themes for medical training, this latest edition includes new chapters on pain, obsessive com-

Each chapter begins with guidance questions and ends with current recommended readings and review questions. A complete 335 questionand-answer multiple choice-type review section not only allows readers to assess how well they have learned the material, but also highlights important points and adds additional specific information to supplement the text.

European Psychologist

Volume 24/ Number 1 /2019 OfďŹ cial Organ of the European Federation of Psychologists Associations (EFPA)

Editor-in-Chief

Peter A. Frensch, Institute of Psychology, Humboldt-University of Berlin, Rudower Chaussee 18, 12489 Berlin, Germany, Tel. +49 30 2093 4922, Fax +49 30 2093 4910, peter.frensch@psychologie.hu-berlin.de

Managing Editor

Kristen Lavallee, editorep-psych@hu-berlin.de

Founding Editor / Past Editor-in-Chief

Kurt Pawlik, Hamburg, Germany (Founding Editor) / Alexander Grob, Basel, Switzerland (Past Editor-in-Chief)

Associate Editors

Ulrike Ehlert, Institute of Psychology, University of Zurich, Binzmühlestrasse 14 / Box 26, 8050 Zurich, Switzerland, Tel. +41 44 635 7350, u.ehlert@psychologie.uzh.ch Alexandra Freund, Institute of Psychology, University of Zurich, Binzmühlestrasse 14 / Box 26, 8050 Zurich, Switzerland, Tel. +41 44 635 7200, freund@psychologie.uzh.ch Katariina Salmela-Aro, University of Helsinki, P.O. Box 4, 00014 University of Helsinki, Finland, Tel. +358 50 415-5283, katariina.salmela-aro@helsinki.fi

EFPA News and Views Editor

Eleni Karayianni, Department of Psychology, University of Cyprus, P.O. Box 20537, Nicosia, Cyprus, Tel. +357 2289 2022, Fax +357 2289 5075, eleni.karayianni@efpa.eu

Editorial Board

Louise Arseneault, UK Dermot Barnes-Holmes, Belgium Claudi Bockting, The Netherlands Gisela Böhm, Norway Mark G. Borg, Malta Serge Brédart, Belgium Catherine Bungener, France Torkil Clemmensen, Denmark Cesare Cornoldi, Italy István Czigler, Hungary Géry d’Ydewalle, Belgium Michael Eysenck, UK Rocio Fernandez-Ballesteros, Spain Dieter Ferring, Luxembourg Magne Arve Flaten, Norway Marta Fulop, Hungary Danute Gailiene, Lithuania

Alexander Grob, Switzerland John Gruzelier, UK Sami Gülgöz, Turkey Vera Hoorens, Belgium Paul Jimenez, Austria Remo Job, Italy Katja Kokko, Finland Günter Krampen, Germany Anton Kühberger, Austria Todd Lubart, France Ingrid Lunt, UK Petr Macek, Czech Republic Mike Martin, Switzerland Teresa McIntyre, USA Judi Mesman, The Netherlands Susana Padeliadu, Greece Ståle Pallesen, Norway

Georgia Panayiotou, Cyprus Sabina Pauen, Germany Marco Perugini, Italy Martin Pinquart, Germany José M. Prieto, Spain Jörg Rieskamp, Switzerland Sandro Rubichi, Italy Ingrid Schoon, UK Rainer Silbereisen, Germany Katya Stoycheva, Bulgaria Jan Strelau, Poland Tiia Tulviste, Estonia Jacques Vauclair, France Dieter Wolke, UK Rita Zukauskiene, Lithuania

The Editorial Board of the European Psychologist comprises scientists chosen by the Editor-in-Chief from recommendations sent by the member association of EFPA and other related professional associations, as well as individual experts from particular fields. The associations contributing to the current editorial board are: Berufsverband Österreichischer Psychologen/innen; Belgian Psychological Society; Cyprus Psychologists’ Association; Unie Psychologickych Asociaci, Czech Republic; Dansk Psykologforening; Union of Estonian Psychologists; Finnish Psychological Association; Fédération Française des Psychologues et de Psychologie; Sociéte Française de Psychologie; Berufsverband Deutscher Psychologinnen und Psychologen; Magyar Pszichológiai Társaság; Psychological Society of Ireland; Associazione Italiana di Pscicologia; Lithuanian Psychological Association; Société Luxembourgeoise de Psychologie; Malta Chamber of Psychologists; Norsk Psykologforening; Österreichische Gesellschaft für Psychologie; Colegio Oficial de Psicologos; Swiss Psychological Society; Turkish Psychological Association; European Association for Research on Learning and Instruction; European Association of Experimental Social Psychology; European Association of Personality Psychology; European Association of Psychological Assessment; European Health Psychology Society.

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European Psychologist (2019), 24(1)

2019 Hogrefe Publishing

Contents Editorial

Challenges in ‘‘Tailoring’’ Adjustment: New Ways of Improving the Response to Chronic Conditions Maria João Figueiras and David Dias Neto

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Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Developing Behavior Change Interventions for Self-Management in Chronic Illness: An Integrative Overview Vera Araújo-Soares, Nelli Hankonen, Justin Presseau, Angela Rodrigues, and Falko F. Sniehotta

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EFPA News and Views

2019 Hogrefe Publishing

Treating Illness Distress in Chronic Illness: Integrating Mental Health Approaches With Illness Self-Management Joanna L. Hudson and Rona Moss-Morris

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Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions Jane C. Walsh and Jenny M. Groarke

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Best Practices and Recommendations for Digital Interventions to Improve Engagement and Adherence in Chronic Illness Sufferers Maria Karekla, Orestis Kasinopoulos, David Dias Neto, David Daniel Ebert, Tom Van Daele, Tine Nordgreen, Stefan Höfer, Svein Oeverland, and Kit Lisbeth Jensen

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Association Between Health Literacy, eHealth Literacy, and Health Outcomes Among Patients With Long-Term Conditions: A Systematic Review Efrat Neter and Esther Brainin

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Supporting Adherence to Medicines for Long-Term Conditions: A Perceptions and Practicalities Approach Based on an Extended Common-Sense Model Rob Horne, Vanessa Cooper, Vari Wileman, and Amy Chan

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Meeting Calendar

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European Psychologist (2019), 24(1)

Editorial Challenges in “Tailoring” Adjustment New Ways of Improving the Response to Chronic Conditions Maria João Figueiras1 and David Dias Neto2,3 1

ISEIT, Research Education and Community Intervention (RECI), Instituto Piaget, Almada, Portugal

2

Psychology and Health Standing Committee of the European Federation of Psychology Associations, Brussels, Belgium

3

APPsyCI – Applied Psychology Research Center Capabilities & Inclusion, ISPA – Instituto Universitário, Lisbon, Portugal

Given the increase in life expectancy and improvements in health care, chronic illnesses have become a major concern for Europe and other Western regions. Chronic illness also poses specific challenges since the focus is not only in seeking a cure – which is often inexistent – but in managing symptom and reducing disability. Considering this focus, patients are asked to be literate and proactive in their treatments which make psychological processes like adjustment to be particularly relevant. This special issue includes reviews on cutting-edge research areas of adjustment to chronic illness. Firstly, we will consider behavior change interventions and the importance of considering mental health issues on the treatment. Secondly, we will review the implications of using technology and the importance of considering practice guidelines. Finally, we will consider the role of adherence and health literacy in mediating the impact of the proposed interventions. In this Editorial, we provide an overview of these areas and will argue for the importance of considering the dimensions of adjustment in tailoring treatment. Given the importance of fostering individual self-management, psychological variables and their progression throughout the condition must be taken into account in tailoring treatment. Only then can the goal of providing the best care, for a particular person in a given time can be achieved.

Introduction The experience of living with chronic illnesses carry important psychological and social consequences, since prolonged illnesses can significantly disrupt the lives of patients and their relatives (Golics, Basra, Salek, & Finlay, 2013; McAndrew et al., 2008; Stanton, & Revenson, 2012). The different nature of chronic versus acute illness means that health and well-being outcomes will not only depend on the course and severity of the illness but also Ó 2019 Hogrefe Publishing

on the adjustment process to the condition. By adjustment process, we mean all the cognitive, affective, and behavioral changes conducted by the individual to adapt to the manifestations and consequences of the illness. It is a chronological process influenced by the characteristics of the individual, the illness, and the context. In general terms, adjustment implies a change in attitude, behavior, or both by an individual based on some recognized need particularly to account for the current environment or changing, atypical, or unexpected conditions. A well-adjusted person is one who satisfies needs in a healthy, beneficial manner and demonstrates appropriate social and psychological responses to situations and demands (VandenBos & American Psychological Association, 2015).

The adjustment process also has several implications with respect to treatment. Given the nature of chronic illnesses, treatment is not focused on the cure but in ameliorating symptoms and reducing disability. In this sense, the involvement of the individual in its treatment and consequently the need of self-management gains an increased importance. This self-management involves coping strategies of the patient, illness and treatment perception, the influence of formal and informal carers, healthcare utilization, adherence to medication or lifestyle recommendations, and perceived quality of life (McBain, Shipley, & Newman, 2015; Stanton, Revenson, & Tennen, 2007).

The same authors argued that in the context of chronic disease, examination of indicators of adjustment in research can enrich its understanding, as well as the integration of environmental variables and the translation of protective factors for adaptive outcomes into interventions.

In this Editorial, we will argue that the consideration of factors, such as the ability to engage in the use of technology to monitor and control illness, the perception of the condition and its treatment, and interventions for behavior change, represents an opportunity to increase the potential of adjustment. Further, the consideration of these factors European Psychologist (2019), 24(1), 1–6 https://doi.org/10.1027/1016-9040/a000348

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provides indicators of relevant variables to tailor interventions and foster self-management for better health outcomes. The existing evidence indicates the need to consider the complexity of factors involved, as well as new challenges, questions, and thoughts about this process.

The aim of this special issue is to present a review of core themes which contribute to update and expand our understanding of adjustment to chronic illness.

Back to the Concept of Adjustment The impact of chronic illnesses in terms of suffering and disability makes the adjustment process particularly relevant and involving several life domains. Many of these conditions can be treated and managed through behavior change interventions, which provide individuals with the skills to have control over and improve their health (Tougas, Hayden, McGrath, Huguet, & Rozario, 2015). It has been acknowledged that due to changing contextual factors, adaptation to chronic illness is not linear (Stanton & Revenson, 2012; Stanton, Revenson, & Tennen, 2007).

For instance, disease progression may involve symptomatic and asymptomatic periods, cancer recurrence, or repeat myocardial infarction which requires constant readjustment, being seen as a process rather than an outcome.

The consideration of adjustment as a process can focus both on the relation between the individual and the health condition, and the chronological evolution of such relation.

These two perspectives are not incompatible.

In the first level, illness and treatment common-sense models are key determinants because they provide targets for intervention and predict variables that will affect the adjustment process. Beliefs about illness and treatment need to “make sense” in order to promote adherence and adjustment (Hekler et al., 2008; Horne et al., 2013; Leventhal, Brissette, & Leventhal, 2003; Howard Leventhal, Leventhal, & Breland, 2011; Mann, Ponieman, Leventhal, & Halm, 2009; McAndrew, Mora, Quigley, Leventhal, & Leventhal, 2014). These models should be considered to promote self-efficacy to engage in self-management behaviors in a coherent fashion, promoting the link between tailored action plans and individual’s level of competence to build self-confidence and trust in new resources to manage illness. For instance, the Common-Sense Model (CSM) of self-regulation is a complex, multilevel framework depicting the process of self-regulation of health and illness, and it has been instrumental in understanding how individuals self-regulate chronic illness (McAndrew et al., 2008). Both the prolonged nature of the condition and the need for the patients to be active on their treatment highlight the importance of the psychological reaction of the individual to the European Psychologist (2019), 24(1), 1–6

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condition. Heterogeneity in adjustment is apparent between individuals and throughout the course of the disease trajectory (Hoyt & Stanton, 2012) requiring individuals to understand transition and to adjust and modify their behaviors and attitudes according to the circumstances (Kralik & van Loon, 2009). This idiosyncratic reaction and its evolution throughout time can be thought within the concept of adjustment. Another factor to be considered is self-monitoring, as the patient undertaking self-measurement and interpretation of vital signs, symptoms, behavior, or psychological well-being; and/or self-adjustment of medication, treatment, lifestyle, or help-seeking behavior as a result of self-awareness and/or self-interpretation (McBain et al., 2015).

In the second level, the chronological dimension of adjustment implies both the consideration of how the illness progresses, as a transition process, and the resources available to minimize their effects. There have been references to models of stress and coping (Walker, Jackson, & Littlejohn, 2004), self-regulation (McAndrew et al., 2008), and social cognition models (Vallis et al., 2003), as frameworks to understand the process of adjustment in relation to the illness trajectory. Over time, as people learn to live with chronic illness, there can be an increased understanding and elaboration in the meanings people attach to their illness and other aspects of their lives (Kralik & van Loon, 2009). Considering these two levels of adjustment requires the use of integrative approaches. One can be to explore illness and treatment perceptions, and the way patients are able to cope with it. Another is to identify the determinants of behavior change according to the context in order to promote self-management. Additionally, the operationalization of theoretical models which include this combination of factors provides a clearer picture of salient issues for adjustment and self-management in chronic patients.

Interventions for Behavior Change in Chronic Illness More recently, the shift from a medic to a multidisciplinary approach for interventions has considerably improved their impact in modifying the determinants of behavior from a wider perspective (Hollands et al., 2017).

Although major theories of behavior change do not include explicit predictions about behavior change in the context of chronic illness, the basic tenets of several health behavior models suggest that the onset of chronic illness should motivate lifestyle changes (Newsom et al., 2012), while other aspects of health behavior models suggest that these lifestyle changes after the diagnosis of a chronic disease may be difficult to make. Further, with the aging of the

Ó 2019 Hogrefe Publishing

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population, research is focusing on ways to enhance individuals’ abilities to self-manage chronic disease. To help individuals better self-manage, healthcare providers must understand clients’ attitudes toward chronic disease, health behavior change, barriers to change, and the role social support plays in the self-management process (Sell, Amella, Mueller, Andrews, & Wachs, 2016). A recent review has indicated that interventions that include self-monitoring can lead to significant reductions in specific areas of healthcare usage, but this is dependent on the characteristics of the chronic illness (McBain et al., 2015).

This gives further support to the need to guide intervention principles in line with existing good evidence but adapted to the context and target groups, providing a better translation of findings. Further, the development of interventions for behavior change in chronic illness seems to benefit from a multidisciplinary approach, allowing flexibility for implementation, adjustment to the stage of the illness and to the context where it occurs. This is supported

by Araújo-Soares and colleagues’ (2019) contribution in the present issue, as well as the need to consider the possibility of adapting an existing intervention with a good evidence based, using integrative approaches. In the same sense, the contribution of Hudson and Moss-Morris (2019) calls for the need to deliver CBT treatments for anxiety and depression which can synergistically target illness distress for patients with chronic conditions. Both contributions for this special issue highlight the need to consider integrative approaches and synergies in order to promote self-management for the wide range of challenges posed by adjustment to chronic illness.

Advances in Technology and Resources In the context of an aging society, it is essential to understand the role of technology in providing new resources and challenges for living with a chronic illness. One source of potential disease self-management and healthcare navigation support is eHealth technology. There is evidence that patients with chronic conditions are increasingly using eHealth technology to support their self-management, and using online resources as a primary source of information, reporting increased health-related knowledge and a greater sense of empowerment to improve their health (Zulman et al., 2015). The results of a recent review looking at the intersection of technology and chronic disease self-management support, and the e-health enhanced chronic care model, showed that eHealth tools make important contributions to chronic care but some aspects require modifications, such as eHealth education as being critical for self-care, the need to consider eHealth within the context of community and the need to assure productive Ó 2019 Hogrefe Publishing

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technology-based interactions between the patient and provider (Gee, Greenwood, Paterniti, Ward, & Miller, 2015).

The potential for new mobile technologies to facilitate health behavior change adds new challenges for health care, raising questions concerning the uptake, acceptability and efficacy of mHealth apps. In recent years, the growth of “electronic health” (eHealth) and “mobile health” (mhealth) interventions have been used in disease prevention and self-management, enabling the collection of data previously based on self-report (Naughton et al., 2016).

This opens the potential of technology to implement user-friendly interventions adapted to context and time, with a possibility of improving self-management and the ability to monitor changes over time. Some of the problems affecting self-management through digital interventions are a consequence of the chronic nature of the conditions. The use of digital resources may contribute to promote adherence to a treatment plan or lifestyle changes (Doughty, 2011). However, one should consider the availability of information versus credibility of sources as well as the updating of contents to promote use and engagement.

There is a shift of adherence from traditional formats to implement treatment to the engagement with digital media, in which engagement is seen as a multidimensional construct and a dynamic process (Perski, Blandford, West, & Michie, 2017). Personalization seems to increase engagement and success with the intervention, providing the rights of the users such as anonymity, confidentiality, trust, and safety are assured. Although digital interventions are based upon technology, the use of persuasive designs in which a sense of human contact is considered may contribute to build trust and improve self-management. Further, as stated by Walsh and Goarke in their contribution for this special issue (2019), the advent of mHealth can benefit behavioral science research and theory, particularly the advantage of providing objective measurements for health behavior may promote self-management and better health outcomes. However, the speed of growth of eHealth and mHealth technology may increase usability of health apps.

This calls for a multidisciplinary approach from technologists, medics, and health psychologists, as well as the need to consider guidelines for equal access and use, and for the quality of electronic devices for health monitoring. In that sense, Karekla and colleagues’ (2019) contribution argued the need to consider user’s characteristics in digital intervention development, and the implementation of research informed and consensual recommendations. Recent research recognized the need for alignment of system tools as well as the urgent need of bridging gaps, as the synergies between these domains have enormous potential for tackling long-term conditions (Gammon, Berntsen, Koricho, Sygna, & Ruland, 2015). The use of technology for selfmanagement in chronic conditions allows personalization European Psychologist (2019), 24(1), 1–6

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which in turn promotes user engagement, greater adherence, and better adjustment.

Adjustment, Literacy, and Adherence The ability to use and correctly interpret health information is important for people in general and in particular for those with chronic conditions, who have to manage complex medical needs, and are likely to take multiple prescription drugs. Previous research has focused on health literacy as a safety issue for patients in relation to misunderstanding instructions on prescription medication labels. The results indicated that lower literacy was associated with misunderstanding the instructions on prescription medication labels (Davis et al., 2006) While medication management is an important aspect of health literacy, the consequences of low health literacy can have a wider impact. These may include failure to recognize signs and symptoms of illness, negligence to preventive care or self-management, and unwillingness to talk with medical providers out of fear or shame. Among people with long-term conditions, this can be critical. For instance, oral or written instructions for medication management may be insufficient or confusing.

According to existing research, pharmaceutical prescriptions are essential to the treatment of most chronic illnesses, yet only half are taken as prescribed (Barber, Parsons, Clifford, Darracott, & Horne, 2004; Horne et al., 2013), and adherence rates are poor. Although there are several determinants of adherence, health literacy seems to be associated with adherence. However, this association seems to be inconsistent. According to a recent meta-analysis, there were very small correlations, between health literacy and adherence, mainly for non-medication regimens.

The same study argued that moderator analyses revealed greater intervention efficacy when health literacy and adherence were assessed using subjective measures compared to objective measures (Miller, 2017). This gives further support to address individual beliefs and perceptions of patients concerning illness and treatment and the extent health literacy may influence their understanding and motivation to adhere in the context of chronic conditions. Furthermore, one should consider that with culturally diverse patients, providers will have to go well beyond scores on health literacy tests to accurately anticipate patient understanding, adherence, and health maintenance (Shaw, Armin, Torres, Orzech, & Vivian, 2012). The same authors argued that the health literacy scores may be affected by culturally shaped models of health and illness, and consequently, it is important to expand our understanding of literacy to include the strengths that patients bring to chronic disease self-management.

However, in their review contribution to this special issue, Neter and Brainin (2019) found an inconsistent European Psychologist (2019), 24(1), 1–6

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association between health literacy and health outcomes.

This result calls for the need to use general health literacy instruments that can enable cross-context comparisons (Mackert, Champlin, Su, & Guadagno, 2015) and to further explore this association in longitudinal studies given the nature of chronic conditions. The opportunity to expand the study about the influence of e-health literacy for adjustment and self-management may also contribute to explore barriers which in turn may endorse a more pragmatic approach to adherence in chronic conditions. In that sense, Horne, Cooper, Wileman, and Yan Chan (2019) contribution to this special issue outline the key features of the Perceptions and Practicalities Approach as a framework to address how the understanding of the illness and treatment impacts on the patient’s motivation and ability to follow the agreed treatment recommendations. This contribution gives further support to the patient-centered approach, and how the patient’ resources may contribute to tailor interventions and promote better adjustment in chronic conditions.

Final Considerations Adjustment to chronic illness has fostered a large body of research and interventions. Chronic illnesses, given their long-term nature and prognosis, imply a greater attention to adjustment. Treatment itself provides another layer of relevant processes. Given the chronic nature of the conditions, treatment implies a closer contact with healthcare providers. Furthermore, given the nature of these illnesses, an important dimension of treatment is self-management.

Self-management implies a greater importance given to health literacy skills, interface with technology, and patient’s motivation and ability to adhere and adjust to the condition (Chapman et al., 2017; Horne et al., 2013; Mackert et al., 2015; McBain et al., 2015; Perski et al., 2017; Simco et al., 2015; Zulman et al., 2015).

The attention given to each particular facet of chronic illness and its treatment is well addressed in each paper of this special issue, contributing for an integrative overview of chronic illness. Some of the challenges raised are direct consequences of the characteristics of chronic illnesses, as well as the development of technology as a resource for self-management. Mental health issues are a concern given the long-term experience of pain or loss of function. Adherence becomes an issue given the need to maintain a treatment that often produces partial results, and as a process to engage in new ways of monitoring adjustment and outcomes. In that sense, digital interventions must be tailored to the patient and their needs, including the consideration of sociodemographics and user-related characteristics. This will allow to improve effectiveness in the management of Ó 2019 Hogrefe Publishing

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chronic illness. Health literacy becomes important as a tool to improve self-management and health at a lower cost, shifting from framing it as a problem to a solution to reduce costs and improve health (Pleasant, Cabe, Patel, Cosenza, & Carmona, 2015).

Understanding adjustment to chronic illness helps to improve treatment and the conditions that foster self-management, but it reinforces the complexity of the interaction between personal, illness, and chronological factors. Therefore, it is less likely that the best treatment is the same for every person in every disease phase. Understanding the adjustment process will add to the literature on the relevant factors that are used in tailoring interventions. Perhaps it is not only the type of illness or the level of health literacy that must be taken into account, but also the stage of the condition and the motivation to engage in new resources, or illness-awareness that must be considered. This combination of factors requires a multidisciplinary approach addressing the need to “tailor” the interventions as technology develops, considering “what makes sense” to the patients, for the complex endeavor of self-management in the context of chronic illness.

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European Psychologist, 24, 49–67. https://doi.org/10.1027/ 1016-9040/a000349 Kralik, D., & van Loon, A. M. (2009). Editorial: Transition and chronic illness experience. Journal of Nursing and Healthcare of Chronic Illness, 1, 113–115. https://doi.org/10.1111/j.17529824.2009.01021.x Leventhal, H., Brissette, I., & Leventhal, E. A. (2003). The commonsense model of self-regulation of health and illness. In L. D. Cameron & H. Leventhal (Eds.), The self-regulation of health and illness behavior (pp. 42–65). London, UK: Routledge.

Leventhal, H., Leventhal, E. A., & Breland, J. Y. (2011). Cognitive science speaks to the “common-sense” of chronic illness management. Annals of Behavioral Medicine, 41, 152–163. https://doi.org/10.1007/s12160-010-9246-9 Mackert, M., Champlin, S., Su, Z., & Guadagno, M. (2015). The many health literacies: Advancing research or fragmentation?

Health Communication, 30, 1161–1165. https://doi.org/ 10.1080/10410236.2015.1037422 Mann, D. M., Ponieman, D., Leventhal, H., & Halm, E. A. (2009).

Predictors of adherence to diabetes medications: The role of disease and medication beliefs. Journal of Behavioral Medicine, 32, 278–284. https://doi.org/10.1007/s10865-009-9202-y McAndrew, L. M., Mora, P. A., Quigley, K. S., Leventhal, E. A., & Leventhal, H. (2014). Using the common sense model of selfregulation to understand the relationship between symptom reporting and trait negative affect. International Journal of Behavioral Medicine, 2, 989–994. https://doi.org/10.1007/ s12529-013-9372-4

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McAndrew, L. M., Musumeci-Szabó, T. J., Mora, P. A., Vileikyte, L., Burns, E., Halm, E. A., . . . Leventhal, H. (2008). Using the common sense model to design interventions for the prevention and management of chronic illness threats: From description to process. British Journal of Health Psychology, 13, 195–204. https://doi.org/10.1348/135910708X295604 McBain, H., Shipley, M., & Newman, S. (2015). The impact of selfmonitoring in chronic illness on healthcare utilisation: A systematic review of reviews. BMC Health Services Research, 15, 1–10. https://doi.org/10.1186/s12913-015-1221-5 Miller, T. (2017). HHS public access. Patient Education and Counseling, 99, 1079–1086. https://doi.org/10.1016/j. pec.2016.01.020.Health Naughton, F., Hopewell, S., Lathia, N., Schalbroeck, R., Brown, C., Mascolo, C., . . . Sutton, S. (2016). A context-sensing mobile phone app (Q sense) for smoking cessation: A mixed-methods study. JMIR MHealth and UHealth, 4, e106. https://doi.org/ 10.2196/mhealth.5787 Neter, E., & Brainin, E. (2019). Association between health literacy, eHealth literacy and health outcomes among patients with long term conditions: A systematic review. European Psychologist, 24, 68–81. https://doi.org/10.1027/1016-9040/a000350 Newsom, J. T., Huguet, N., Mccarthy, M. J., Ramage-morin, P., Kaplan, M. S., Bernier, J., . . . Oderkirk, J. (2012). Health behavior change following chronic illness in middle and later life. The Journals of Gerontology, 67, 279–288. https://doi.org/ 10.1093/geronb/gbr103 Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behavior change interventions: A systematic review using principles from critical interpretive synthesis. Translational Behavioral Medicine, 7, 254–267. https://doi.org/10.1007/s13142-016-0453-1 Pleasant, A., Cabe, J., Patel, K., Cosenza, J., & Carmona, R. (2015).

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SAGE Open, 6, 3. https://doi.org/10.1177/2158244016665661 Shaw, S. J., Armin, J., Torres, C. H., Orzech, K. M., & Vivian, J. (2012). Chronic disease self-management and health literacy in four ethnic groups. Journal of Health Communication, 17(Suppl. 3), 67–81. https://doi.org/10.1080/10810730.2012.712623 Simco, R., McCusker, J., Sewitch, M., Cole, M. G., Yaffe, M., Lavoie, K. L., . . . Belzile, E. (2015). Adherence to a telephone-supported depression self-care intervention for adults with chronic physical illnesses. SAGE Open, 5, 1. https://doi.org/10.1177/ 2158244015572486 Stanton, A. L., & Revenson, T. (2012). Adjustment to chronic disease: Progress and promise in research. In H. S. Friedman (Ed.), Oxford handbook of health psychology (pp. 244–272). New York, NY: Oxford University Press. https://doi.org/10.1093/ oxfordhb/9780195342819.013.0011 Stanton, A. L., Revenson, T. A., & Tennen, H. (2007). Health psychology: Psychological adjustment to chronic disease.

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diabetes. Diabetes Care, 26, 1468–1474. https://doi.org/ 10.2337/DIACARE.26.5.1468 VandenBos, G. R., & American Psychological Association. (2015).

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Maria João Figueiras Instituto Piaget – RECI (Research Education and Community Intervention) Quinta da Arreinela de Cima 2800-305 Almada Portugal maria.j.santos@almada.ipiaget.pt David Dias Neto Instituto Superior de Psicologia Aplicada Lisbon Portugal d.neto@campus.ul.pt

Maria João Figueiras is a clinical psychologist (PhD, Health Psychology) awarded by Kings College and an Associate Professor at the Piaget Institute (Almada). She is President of the Portuguese Association of Health and Behavioural Sciences, full member of the European Health Psychology Society and coordinated as PI several research projects funded by FCT in cooperation with other entities. Her field of research is in the area of Health Psychology, namely illness and treatment perceptions, adherence and chronic illness.

David Dias Neto (PhD) is a Lecturer at Instituto Superior de Psicologia Aplicada – Instituto Universitário, Lisbon, Portugal. He has published in the areas of process research in psychotherapy and clinical and health psychology. He is the current president of the clinical and health psychology division of the Portuguese Order of Psychologists. He also works as a psychotherapist in private practice.

Ó 2019 Hogrefe Publishing

Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Developing Behavior Change Interventions for Self-Management in Chronic Illness An Integrative Overview Vera Araújo-Soares,1,2 Nelli Hankonen,3 Justin Presseau,4,5,6 Angela Rodrigues,1,7 and Falko F. Sniehotta1,7 1

Institute of Health & Society, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

2

School of Psychology, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

3

Faculty of Social Sciences, University of Tampere, Finland

4

Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada

5

School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Canada

6

School of Psychology, University of Ottawa, Canada

7

Fuse. The UK Clinical Research Collaboration Centre for Translational Research in Public Health

Abstract: More people than ever are living longer with chronic conditions such as obesity, type 2 diabetes, and heart disease. Behavior change for effective self-management can improve health outcomes and quality of life in people living with such chronic illnesses. The science of developing behavior change interventions with impact for patients aims to optimize the reach, effectiveness, adoption, implementation, and maintenance of interventions and rigorous evaluation of outcomes and processes of behavior change. The development of new services and technologies offers opportunities to enhance the scope of delivery of interventions to support behavior change and self-management at scale.

Herein, we review key contemporary approaches to intervention development, provide a critical overview, and integrate these approaches into a pragmatic, user-friendly framework to rigorously guide decision-making in behavior change intervention development. Moreover, we highlight novel emerging methods for rapid and agile intervention development. On-going progress in the science of intervention development is needed to remain in step with such new developments and to continue to leverage behavioral science’s capacity to contribute to optimizing interventions, modify behavior, and facilitate self-management in individuals living with chronic illness.

Keywords: Behavior change, intervention development, complex interventions

Life expectancy continues to increase worldwide, with the global average life expectancy having increased by 5 years between 2000 and 2015 (World Health Organization, 2014a). However, non-communicable conditions such as cardiovascular disease, respiratory disease, cancer, and diabetes have also increased since 2000 in every region of the world and are now the most prevalent causes of mortality and morbidity (World Health Organization, 2014a, 2014b). Chronic non-communicable conditions share behavioral risk factors such as tobacco smoking, poor diet, and physical inactivity (Lim et al., 2012). These conditions are also associated with an increased risk of undermining mental health (Moussavi et al., 2007). Multimorbidity is also prevalent and health behaviors can benefit patients by positively impacting on more than one condition

(Barnett et al., 2012). Self-management is thus a complex endeavor, involving adherence to treatment, change to multiple health behaviors, and regular contact with healthcare providers (Department of Health, 2012; SchulmanGreen et al., 2012).

Interventions addressing risk factors and supporting behavior change for the effective self-management of chronic conditions can make a considerable difference to health and well-being and reduce the costs of delivering health care to an aging population living longer with chronic conditions (OECD/EU, 2016). In the US, 157 million people are predicted to live with chronic conditions by 2020. Population aging raises capacity concerns for healthcare systems, in their current configurations, to cope with the increasing burden of chronic conditions

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(Bodenheimer, Chen, & Bennett, 2009; NHS England, 2016). There is consensus for the need for interventions to support individuals and populations by targeting the prevention and self-management of chronic disease (Boon et al., 2014) and for the key role of behavior change interventions in this process (Hardeman, Sutton, Michie, & Kinmonth, 2004).

What Is a Health Behavior Change Intervention?

Interventions are coordinated sets of activities and techniques introduced at a given time and place to change the behavior of individuals, communities, and/or populations through a hypothesized or known mechanism (NICE, 2007, 2014). The health of populations and the individuals within them is influenced by a complex system of determinants, from individual lifestyle factor to community influences, through living, working, and social conditions (Dahlgren & Whitehead, 2006). Health behavior change interventions can be targeted at a combination of levels: policy (e.g., laws and regulation), community (e.g., neighborhoods), macro-environments (e.g., foot outlets or transport links), micro-environmental (e.g., choice architecture in shops), institutional (e.g., schools and employers), interpersonal (families and social networks), and/or intrapersonal (e.g., weight loss program or therapy) level (AraújoSoares & Sniehotta, 2017; Hollands et al., 2017; McLeroy, Bibeau, Steckler, & Glanz, 1988).

Health behavior change interventions are usually complex (Craig et al., 2008). What makes an intervention complex is the number and complexity of its interacting components, the behaviors involved, the organizational group, and individual levels targeted and the outcomes as well as the degree of flexibility or tailoring permitted. The TIDieR checklist (Hoffmann et al., 2014) was developed to improve the completeness of reporting, and ultimately the replicability, of interventions by describing: (a) a rationale or theory describing the goals of the intervention elements, (b) the content in terms of behavior change methods (Adams, Giles, McColl, & Sniehotta, 2014; Hollands et al., 2017; Kok et al., 2016; Michie, Richardson, Johnston, Abraham, Francis, Hardeman, et al., 2013), materials, and procedures, (c) provider(s) (including qualification and training needed), (d) modes of delivery (e.g., provided face-to-face or through a digital platform) to individuals or groups (Dombrowski, O’Carroll, & Williams, 2016), (e) location and required infrastructure, (f) timing and dose, and (g) any planned mechanisms for tailoring or adaptation of the intervention to needs/features of the recipient(s). An extension of the TIDieR guideline for reporting population European Psychologist (2019), 24(1), 7–25

health and policy interventions has recently been published (Campbell et al., 2018). Interventions also often include additional components to build and sustain rapport and engagement through interpersonal styles (Hagger & Hardcastle, 2014) or features such as gamification in digital interventions (Cugelman, 2013). Health behavior change intervention development is the process of deciding the optimal combination of these features and the transparent reporting of these decisions.

What Makes a Good Health Behavior Change Intervention?

“Primum non nocere” (eng. “first, do no harm”). The principle of non-maleficence is the single most important criterion for any health intervention (Craig et al., 2008; Michie, Atkins, & West, 2014). In addition, a good intervention should be designed for impact, should be evaluable, should not increase social inequalities, and should have a demonstrable benefit over existing interventions and services.

The impact of interventions on the health of the target audience can be illustrated through the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) model (Glasgow, Vogt, & Boles, 1999). Reach refers to the proportion of the intended target population that can actually be and is ultimately reached with an intervention; Effectiveness refers to the beneficial and unintended effect the intervention achieves on key outcomes under realworld conditions, including cost-effectiveness; Adoption refers to the uptake of the intervention by the staff, settings, and organizations; Implementation refers to the degree to which the intervention can/will be delivered consistently and with fidelity over time and setting; and Maintenance refers to the sustainability of intervention effectiveness in individuals and settings over time. To achieve this, interventions should be based on the best available evidencebased theory and direct evidence to optimize impact and to model whether and how the intervention is likely to create benefit (Bartholomew Eldredge et al., 2016; Craig et al., 2008; Wight, Wimbush, Jepson, & Doi, 2016). Optimizing RE-AIM is aided by maximizing the acceptability and feasibility of intervention procedures and materials (Lancaster, 2015). This is best achieved through the active involvement of key stakeholders in all stages, from development through to evaluation of acceptability and feasibility in initial pilot/ feasibility studies as well as subsequent efficacy/effectiveness, implementation and maintenance evaluations (Craig et al., 2008; O’Brien et al., 2016).

A prerequisite of a good intervention is its “evaluability,” that is, whether its effect can be robustly evaluated. Interventions with a clear definition, elaborated logic model,

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and defined primary and intermediate targets are easier to evaluate, which in turn facilitate understanding if, how and for whom an intervention works, facilitating optimization and thereby contributing to the accumulation of knowledge (Leviton, Khan, Rog, Dawkins, & Cotton, 2010; Ogilvie et al., 2011; Windsor, 2015).

Good interventions should not increase social inequalities in health (Lorenc, Petticrew, Welch, & Tugwell, 2013). Health and healthy life expectancy are strongly related to socioeconomic status (OECD/EU, 2016). To avoid intervention-generated inequalities, intervention design should be sensitive to PROGRESS indicators (Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital (T. Evans & Brown, 2003; O’Neill et al., 2014). Intervention developers need to consider uptake, usage, and level of individual agency required to minimize the potential of generating inequalities (Adams, Mytton, White, & Monsivais, 2016).

Finally, good interventions should create incremental benefit over already existing interventions and services.

Interventions have high utility if they address gaps in provision, increase the potential to be implemented and sustained, reduce costs and/or address barriers compared with previous and existing interventions. In particular, scalable interventions, that is, effective interventions which have a far reach and modest costs, address the need for solutions which have few resource and geographic barriers and can be provided to large numbers of individuals and communities (Milat, King, Bauman, & Redman, 2013).

The health research landscape is not short of behavioral interventions. In light of this, a thorough environmental scan analysis is needed to identify gaps in provision to ensure that new interventions have a fair chance to make a positive contribution to health and well-being. Understanding usual care and competing interventions in a given setting enables strategic decision-making about potential incremental benefit of a new intervention. Increasingly, the boundaries of usual care are no longer physical or geographical. As interventions can take years to be developed and fully evaluated, this analysis of the health intervention market should also consider pilot studies and evaluation studies underway, for example, by analyzing trial registries and grey literature (Adams, Hillier-Brown, et al., 2016).

The Process of Intervention Development There is a range of frameworks that can inform the development of health behavior change interventions such as the MRC guidance for the development and evaluation of

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complex interventions (Craig et al., 2008), Intervention mapping (IM; Bartholomew Eldredge et al., 2016), Theory Informed Implementation Intervention (S. D. French et al., 2012), PRECEDE-PROCEDE (Green & Kreuter, 2005), the Person-Based Approach (Yardley, Morrison, Bradbury, & Muller, 2015), the 6SQuID approach in quality intervention development (Wight et al., 2016), evidenceguided co-design (O’Brien et al., 2016), the Knowledgeto-Action (KTA) cycle (Graham et al., 2006), the ORBIT model (Czajkowski et al., 2015), the Experimental Medicine Model (Sheeran, Klein, & Rothman, 2017), Multiphase optimization strategy (MOST; Collins, Murphy, & Strecher, 2007), and the Behavior Change Wheel (Michie, van Stralen, & West, 2011; see Appendix A for a summary of frameworks and their purpose). While each has a different focus and approach, they converge on a core set of key steps that include: analyzing the problem and developing an intervention objective, causal modeling, defining intervention features, developing a logic model of change, developing materials and interface, and empirical optimization followed by outcome and process evaluation and implementation. Intervention development is iterative, recursive, and cyclical rather than linear. Developers may need to go back and forth between steps to achieve the optimal intervention definition paired with most appropriate logic model of change within available resources.

Intervention development should ideally be led by an interdisciplinary Planning and Development Group representing relevant expertise (e.g., clinical care, psychology, policy, sociology, health economics, epidemiology, service design) and key stakeholders (e.g., citizens, patients, carers, healthcare professionals, deliverers, commissioners, policymakers, funders) to understand the context for intervening and to make strategic decisions that reflect scientific evidence and the preferences and views of those for whom the intervention is developed and those whose input is needed to adopt and implement the intervention (Bartholomew Eldredge et al., 2016; Witteman et al., 2017). To document the sequence of decisions involved in intervention development, workbooks can help to record intervention development steps, crucial decisions, and the process and information informing these decisions (Bartholomew Eldredge et al., 2016); Appendix B contains a comprehensive list of Key Considerations for the Reporting of Intervention Development). Next, we address each key step in detail:

A. Analyzing the Problem and Developing an Intervention Objective The development of a behavior change intervention rests on a foundation of a thorough analysis of the problem that

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the intervention developers aim to solve and a clear definition of intervention objectives. PRECEDE/PROCEED was conceived in the 1970s to guide policymakers and intervention planners in analyzing the likely costs and benefits of health programs. It consists of two main parts: PRECEDE describes an “educational diagnosis” and is an acronym for Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation. PROCEED refers to an “ecological diagnosis” and stands for Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development (Green & Kreuter, 2005). It provides the first framework for analyzing how health and quality of life relate to behavior, physiology, and environmental factors and for the identification of predisposing, reinforcing, and enabling factors for behaviors, which can be tackled with interventions.

Many intervention development frameworks include a Needs Assessment, which involves assessing the health problem and its likely behavioral, social, and environmental causes. This initial stage involves the identification and definition of the sequence of behaviors needed to modify health outcomes thereby identifying intermediate outcomes relevant for the hypothesized mechanisms of the intervention (Bartholomew Eldredge et al., 2016), that is, “who needs to do what differently, when, where, how?” (S. D. French et al., 2012). The person-based approach to intervention development (Yardley et al., 2015) aims to ground the development of behavior change interventions in an understanding of the perspective and psychosocial context of the people who will use them. Behaviors targeted for change are embedded in a network of multiple behaviors, some of which may facilitate or conflict with each other (Presseau, Tait, Johnston, Francis, & Sniehotta, 2013).

Understanding how a target health behavior fits alongside other behaviors, and the essential preparatory behaviors required, can help to identify the most viable behavioral targets for an intervention that may extend beyond the single behavioral outcome of the intervention. Target behaviors need to be defined in context and in very specific terms, ideally in terms of Target(s), Action, Context(s), Time(s) and actors (Fishbein, 1967; Francis & Presseau, 2018), including the inter-relationships between behaviors and actors. Considerations about changeability guide the prioritization and selection of target behaviors and targeted antecedents of behavior, for example, which changes are achievable based on current evidence and theory, and how much impact would such changes have for the key outcomes (Bartholomew Eldredge et al., 2016; Czajkowski et al., 2015; Sheeran et al., 2017; Wight et al., 2016).

Key stakeholders should contribute from the beginning to defining the initial problem, rather than the intervention development being a researcher-driven top-down design task. Stakeholder involvement helps to bridge between European Psychologist (2019), 24(1), 7–25

the evidence and the local context and ensures ownership, acceptability, and widespread support for the intervention essential for implementation (O’Brien et al., 2016). In some instances, intervention priorities are driven by users or patient organization. Such priorities can be robustly surfaced, for example, involving James Lind Alliance (2017) methods that bring clinicians, patients, and carers together to use a formal methodological approach to generate research priorities that are important to patients across a range of settings.

B. Defining the Scientific Core of the Intervention Health behavior change interventions are guided by a logic model or a theory of change that combines the intervention techniques used to target causal mechanisms into a comprehensive and testable set of assumptions (Moore et al., 2015). Three steps go hand in hand and are best described as one iterative process:(i) causal modeling of the problem, (ii) defining intervention features, and (iii) formulating a logic model of change for the intervention (Bartholomew Eldredge et al., 2016; Moore et al., 2015; Wight et al., 2016).

Decisions need to be made on method(s) and mode(s) of delivery, behavior change technique(s), provider(s), location (s), timing, dose, personalization and hypothesized causal mechanisms to optimize reach, (cost-) effectiveness, adoption, implementation, and maintenance. These design decisions should be recorded and made explicit to clarify the contribution that all new interventions make to previous evidence. The process should be led by a participatory planning group representing stakeholders such as users and commissioners of the intervention and the research team to iteratively build a hypothesis of change and make design decisions based on scientific evidence and the needs of the target audience. This ensures the relevance of the developed solution and creates co-ownership as a result of coproduction. (i) Causal Modeling The identification of causal and contextual factors affecting self-management behaviors is a key step in intervention development. Behavior is the result of a complex ecologic system of influences which range from proximal individual, cognitive, and emotional factors to social and community influence up to more distal factors such as care delivery systems (e.g., access to specialist medical care), living and working conditions (employment, environment, education, and housing), and socioeconomic, cultural, and environmental conditions (e.g., legislation; Dahlgren & Whitehead, 2006). Modifiable factors that have a strong relationship to

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the target behavior are potential targets for interventions (Michie, van Stralen, et al., 2011; Wight et al., 2016).

Behavior change approaches tend to operate on the assumption that interventions affect behavior by modifying social, environmental, and/or cognitive predictors of the target behavior. Interventions are then thought to operate through a sequential causal model beginning from predictors of behavior, to behavior, to physiological changes and eventually leading to health outcome(s) (Hardeman et al., 2005). IM (Bartholomew Eldredge et al., 2016) proposes to work backward from the targeted health problems (and that impact on quality of life), to the behavior and environmental factors that shape these health problems, and finally to the predictors of the causal behavioral and environmental risk factors. Predictors are rated by relevance and changeability to determine their priority for inclusion in the intervention (Bartholomew Eldredge et al., 2016; Yardley et al., 2015).

Literature reviews are recommended to synthesize evidence of the causes and predictors of the target behavior (Bartholomew Eldredge et al., 2016; Craig et al., 2008), ideally, with systematic searches (Craig et al 2008). In reviewing existing evidence, tensions between strength and rigor and applicability of evidence can occur. Decisions about evidence reviews should be strategically driven to address key uncertainties. While usually systematic reviews of studies with low risk of bias are preferable, the most relevant evidence informing an intervention might be supplemented by grey literature such as local government reports or hospital records (Adams, Hillier-Brown, et al., 2016; O’Brien et al., 2016; Rodrigues, Sniehotta, Birch-Machin, Olivier, & Araujo-Soares, 2017). Reviews may highlight the degree to which results are likely to be transferable to the present context but often additional empirical research is needed to identify the most important predictors and to test their sensitivity to contextual features of communities, services, or geographies.

Theory has a central role in this process. Intervention development is often based on operationalizing the principles from a single theory and selecting intervention techniques with the potential to modify the theoretical predictors of behavior. This approach can be useful when there is insufficient resource to consider collecting further empirical data and given the inherently evidenced-based nature of a theory, in that it has been successfully applied to different behaviors and/or in different contexts (D. P. French, Darker, Eves, & Sniehotta, 2013). However, this approach is limited when the observed prospective relationships considered for the selection of intermediate intervention targets are not strong enough for interventions changing behavioral predictors to achieve changes in behavior (Sniehotta, Presseau, & Araújo-Soares, 2014).

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When no appropriate theory can be identified, or when more than one may seem relevant, intervention developers can use the Theoretical Domains Framework (TDF) to organize evidence about key barriers and enablers and link back to relevant theories (Francis, O’Connor, & Curran, 2012; Heslehurst et al., 2014). The TDF is a simple tool developed through review and consensus methods to describe the most common explanatory constructs in behavioral theories organized into 14 domains: knowledge, skills, social influences, memory, attention and decision processes, social/professional role and identity, reinforcement, beliefs about capabilities, beliefs about consequences, optimism, intention, goals, behavioral regulation, emotion, environmental context and resources (Cane, O’Connor, & Michie, 2012; Michie et al., 2005). The TDF can be used to inform both qualitative and quantitative studies with the aim to understand key predictors of behavior and to identify the most relevant theoretical approach (Beenstock et al., 2012; Laine, Araújo-Soares, Haukkala, & Hankonen, 2017; Presseau, Schwalm, et al., 2017).

Additional empirical studies can increase understanding of the key influences of the behavior in the target group.

For example, a survey identifying the most important correlates of physical activity behavior and intention could help in selecting the key barriers and enablers to target with an intervention (Hankonen, Heino, Kujala, et al., 2017; Presseau, Schwalm, et al., 2017; Sniehotta, Schwarzer, Scholz, & Schüz, 2005). Qualitative interviews or n-of-1 studies can provide an individualized assessment of barriers and needs (McDonald et al., 2017; Rodrigues, Sniehotta, Birch-Machin, & Araujo-Soares, 2017; Yardley et al., 2015). A key weakness of approaches based on correlation is the lack of causation and the problem of attenuation, that is, large changes in predictors are needed to achieve modest changes in behavior (Sniehotta, 2009).

Where multiple behaviors are targeted, a process of testing multiple theories across multiple behaviors can be used to identify the most consistently predictive constructs within their theories across behaviors, then theorize and test how such theories and their constructs can be combined, for example, into a dual process model (Presseau, Johnston, et al., 2014) to inform a logic model (Presseau, Hawthorne, et al., 2014). This approach combines the strength of preexisting theory (and its tested mediating and moderating mechanisms) with the empirical comparison of theory across behaviors to facilitate the selection of behavior(s) and theory upon which to further develop the intervention. Theory is used to address uncertainties and may include theoretical ideas that are not directly related to behavior, for example, theories of persuasion (Petty & Cacioppo, 1986) or of symptom recognition (Petersen, van den Berg, Janssens, & van den Bergh, 2011). Figure 1 provides two examples of intervention development.

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(ii) Defining Intervention Features Intervention techniques (e.g., to change behavior, cognitions, perceptions, or environmental variables) are selected based on evidence of their effectiveness in changing the identified causal and contextual factors influencing the target behavior. Intervention development approaches differ in how they approach the analysis of causal factors focussing on intervention targets or techniques (Michie et al., 2014; Sheeran et al., 2017; Webb, Michie, & Sniehotta, 2010). Target-based approaches identify modifiable predictors of behavior, whereas technique-based approaches focus on intervention techniques themselves and contextual modifications which directly influence behavior (Webb et al., 2010).

As highlighted in the knowledge creation funnel within the KTA cycle (Graham et al., 2006), use of review evidence sets the foundation and prevents repeating previously unsuccessful behavior change techniques or withholding intervention strategies with demonstrated effectiveness in changing behavior. In some cases, evidence synthesis may identify that a suitable intervention already exists that could be retrofitted (i.e., transformed for use in a novel context and or in a novel population) rather than re-invented. But systematic reviews of randomised controlled trials (RCTs) of interventions with similar aims do not always provide sufficient answers. For example, in the development of the “Let’s Move It” intervention to change physical activity and sedentary behaviors in vocational school, a systematic review (Hynynen et al., 2016) informed the designers about what works in getting older adolescents more active, but it was not sufficient. A range of other relevant sources of evidence contributed to its development including existing evidence regarding the setting (schoolbased health promotion), evidence about the target behavior using a range of methods and research on similar interventions in other age groups and populations contributed to inform the intervention design.

Different levels of evidence answer different questions.

While systematic reviews of RCTs of behavior change interventions provide the strongest evidence for effectiveness, they often say little about reach, adoption, and implementation outside of a research study or about longer-term maintenance (Dombrowski et al., 2012). Likewise evidence from rigorous studies conducted in very different settings or in communities with different features may be applicable to the local needs when retrofitted. Evidence synthesis should be strategic and sequential, developing an iterative understanding of how to optimize the intervention (Michie et al., 2014). Where previous health behavior change interventions had heterogeneous effects, it is often possible to code behavior change techniques and other intervention features such as modes of delivery (Abraham & Michie, 2008; Adams et al., 2014; Kok et al., 2016; Michie, Ashford, European Psychologist (2019), 24(1), 7–25

et al., 2011; Michie, Richardson, Johnston, Abraham, Francis, & Hardeman, 2013) and to explore whether such features are associated with intervention effectiveness (Dombrowski et al., 2012). Such an intervention features review-based approach begins by identifying intervention techniques and other TIDIER features (Hoffmann et al., 2014) of interventions for a given health behavior in a systematic review of trials. TIDIER features, including behavior change techniques and other intervention techniques can then be coded within interventions in the review to test which techniques and combinations of these are associated with greater effectiveness in other settings. Even though trials of interventions make causal statements of effectiveness, the evaluation of intervention techniques within the review is correlational and should be treated with due care.

Nevertheless, this approach can help to combine evidence of intervention strategies that have been found to be effective in other settings and/or using theory to inform the selection of intervention techniques.

In addition to review-based identification of effective intervention features, some approaches promote an experimental method for intervention development to establish causal evidence for the hypothesized change by identifying the potential modifiable causal factors and assessing whether changes in the target behavior occur as a result of manipulating the predictive factor(s) (Sheeran et al., 2017). The emphasis is on understanding the mechanisms of change and using experimental designs to robustly clarify how to change these and integrating this knowledge into applied research. Environmental interventions targeting point-of-choice decisions such as stairs versus escalator use (Ryan, Lyon, Webb, Eves, & Ryan, 2011) and on-thespot opportunities to register for organ donation (Li et al., 2017), nudges (Hollands et al., 2013; Marteau, Ogilvie, Roland, Suhrcke, & Kelly, 2011) or point of sale decisions (Dolan et al., 2012) are more likely to be informed by experimental than by correlational considerations.

Some intervention techniques may be effective when tested in an RCT but not widely acceptable by facilitators or target audience alike, while other intervention techniques might be highly acceptable but show smaller effect sizes. Acceptability can be defined as a “multi-faceted construct that reflects the extent to which people delivering or receiving a healthcare intervention consider it to be appropriate, based on anticipated or experienced cognitive and emotional responses to the intervention” (Sekhon, Cartwright, & Francis, 2017, p. 4). Engaging stakeholders in the development process from early on will increase the potential for acceptability. Intervention principles that are theoretically sound and in line with good evidence, might still not be seen as acceptable without adaptation to context and audience. For example, some might not be willing to engage in planning interventions unless key modifications

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The Interventions Supporting Long-term Adherence and Decreasing cardiovascular events (ISLAND) multi centre trial and theory-based process evaluation involved an intervention to support on-going medication adherence and attendance to cardiac rehabilitation following a myocardial infarction (MI) (Ivers et al., 2017).

Intervention development considered existing Cochrane review evidence for both these behaviors (Karmali et al., 2014; Nieuwlaat et al., 2014), key trials of behaviorchange interventions (Sniehotta, Scholz, & Schwarzer, 2006), cost-effectiveness data (Ito et al., 2012) and pilot trial data (Schwalm et al., 2015). Development also involved conducting qualitative interviews based on the Theoretical Domains Framework with patients post-MI to identify potential barriers and enablers, as well as quantitative analyses based on the Health Action Process Approach to identify motivational and volitional correlates of behavior (Presseau, Schwalm, et al., 2017). These sources of evidence informed the basis for developing a logic model, behavior change techniques and modes of delivery of the intervention (Ivers et al., 2017). An interdisciplinary team was assembled involving partnering with a design firm, patients, a patient stakeholder organization, primary and secondary healthcare providers, and researchers (health psychologists, statisticians, health services researchers, health economists, implementation scientists, and human factors engineers) from the start and throughout to ensure that it could be implemented at scale within healthcare systems. An explicit user-centred design process was used to iteratively develop materials including developing personas, prototype materials, two design cycles, piloting materials using think aloud and semi-structured interviews (Witteman et al., 2017).

The Let’s Move It (LMI) intervention aimed to increase physical activity and decrease excessive sedentary behavior among adolescents – especially those with insufficient PA levels (Hankonen et al., 2016), physical activity can prevent or delay onset of several lifestyle-related chronic diseses such as type 2 diabetes or heart disease. The aim of the intervention development was to create a feasible, acceptable, effective and costeffective school-based intervention that could later be scaled up. Intervention development considered existing review evidence for these behaviors and school-based health promotion interventions, but also carried out a systematic review of the target group, behaviors, and context (Hynynen et al., 2016). Development also involved conducting qualitative analysis of interviews to better understand the role of PA in daily life of Finnish vocational students, as well as analysis of personal stories on key inci dents related to PA change over childhood and adolescence. Further, quantitative analyses informed by the Therectical Domain Framework (Francis et al., 2012) aimed to identify the key correlates of these behaviors (Hankonen, Heino, Kujala, et al., 2017). As some parts of the intervention were to be delivered by teachers, a mixed-methods study to examine acceptability of potential intervention strategies was conducted among teachers (Laine et al., 2017). We conducted e.g., scenario work with a group of experts and stakeholders, and with a student panel, did practical small trials of e.g., discussion exercises with students in order to get rapid feedback of alternative practical strategies within the student program. This resulted in the first version of the intervention, the acceptability). and feasibility was investigated in a randomised feasibility trial (Hankonen, Heino, Kujala, et al., 2017). An enhanced version of the intervention was then developed based on this feedback (Hankonen, Heino, Kujala, et al., 2017). An advertisement agency designed the materials and the visual look of the intervention, in close collaboration with the research team, including testing with end-users and a close linkage with theory. An interdisciplinary team involving researchers (disciplines including social and health psychology, statistics, exercise physiology and measurement, sports science, implementation science, sociology), health promotion organisations, teachers, students, school health specialists, etc. was assembled from the start and they convened regularly throughout the intervention development process.

Figure 1. Intervention development examples.

are implemented to increase acceptability and feasibility (Witteman et al., 2017). Anticipated acceptability of candidate features can be empirically examined to inform decisions, for example, teachers’ views on potential strategies

to reduce student sitting in schools was examined using a mixed-methods approach (Laine et al., 2017). This example also illustrates that in addition to the main target group (students), the environmental agents or “providers” (teachers)

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that deliver the intervention are also the target of a “secondary” intervention, hence, their views and behaviors should also be understood. In implementation science the environmental agents are the target of the intervention. (iii) Developing a Logic Model of Change The MRC framework for the development and evaluation of complex interventions highlights that interventions should be theory-based (Craig et al., 2008). A common misconception is equating “theory” with “hypothesis.” A scientific theory has been empirically demonstrated to explain behavior. If, while designing an intervention, the team concludes that there is a need to target a combination of constructs from different theories that have never been tested together, what will actually happen is that a specific scientific hypothesis (that can lead to a new theory if successful) is being tested, not a theory.

It is useful to create a program’s scientific hypothesis in terms of the evidence-based mechanisms associated with behavior and behavior change. In contrast to formal scientific theories, program theories are practical, concrete working models and hypotheses of interventions, and are specific to each program or intervention. They (1) specify the intervention components, the intervention’s expected outcomes, and the methods for assessing those outcomes, often in the form of a logic model, and (2) offer an intervention’s “hypotheses” (the rationale and assumptions about mechanisms that link processes and inputs to (both intended and unintended) outcomes, as well as conditions/context necessary for effectiveness; Davidoff, Dixon-Woods, Leviton, & Michie, 2015).

This hypothesis of change may be based on or informed by scientific theories, but the main requirement is to formalize the hypothesized causal assumptions, detail the planned implementation and theorized mechanisms of impact within a set of relevant contexts (Craig et al., 2008). Theory can also identify specific issues that create barriers to intervention success (e.g., competing goals in time-limited GP sessions; Presseau, Sniehotta, Francis, & Campbell, 2009). Rather than using a single theory to guide intervention development, it is often sensible to use theory to address the uncertainties in the process and to create a map of assumptions/hypothesis linking theories and evidence.

According to UK MRC Guidance, modeling an intervention before evaluation provides the insights that are key to informing the design of both the intervention and its evaluation. Modeling may take the form of a pretrial economic evaluation testing if the set of assumptions used to develop the interventions are sufficient to provide a good chance of successful impact. Mapping links between outcomes, determinants, change objectives, and intervention techniques reflect this process of creating the logic of intervention

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(Bartholomew Eldredge et al., 2016). For example, in a school-based intervention to prevent obesity, performance objectives (e.g., Communicate healthy behavior messages to parents and seek their support) are mapped against personal (e.g., self-efficacy) and external, environmental predictors (e.g., family support), and thus created actionable change objectives (e.g., confidence to seek parental support and social reinforcement from parents/family for interest in healthy lifestyles. These change objectives become the target of intervention techniques (Lloyd, Logan, Greaves, & Wyatt, 2011).

This process should also involve the explicit elaboration of a “dark” logic model, that is, a careful elaboration of potential pathways through which the intervention may lead to negative or harmful consequences (Bonell, Jamal, Melendez-Torres, & Cummins, 2014). This extends beyond identifying potential harms by clearly outlining the mechanisms through which such harms may take place.

The Behavior Change Wheel (Michie, van Stralen, et al., 2011) is a particularly useful recent tool to integrate theory and evidence and to bring together stakeholders in making intervention design decisions. It is a meta-model of the intervention development process based on a comprehensive review and synthesis of existing methodological and theoretical approaches from various disciplines. The Behavior Change Wheel links policy categories (guidelines, environmental/social planning, communication/marketing, fiscal measures, regulation, service provision and legislation) with intervention functions (restrictions, education, persuasion, incentivization, coercion, training, enablement, modeling, and environmental restructuring) and commonly theorized sources of behavior; Capability (physical and psychological), Opportunity (social and physical) and Motivation (automatic and reflective), known as the COM-B model (Michie, van Stralen, et al., 2011).

C. Development of Material and Interface Design decisions about the look and feel of an intervention can promote their sustained use and are thus highly dependent on the mode of deliver, target audience and behavior.

In a digital intervention, the graphics used, decisions about gamification and devices used to deploy the intervention influence the overall success of a behavior change intervention. This calls for multidisciplinary work to incorporate theories and methods from other disciplines. Health behavior change theories are not sufficient for informing all decisions about the design of an intervention, and other disciplines have a key role in optimizing design decisions. The use of community-based participatory research (TeufelShone, Siyuja, Watahomigie, & Irwin, 2006) such as

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consensus conferences (Berry, Chan, Bell, & Walker, 2012) or co-design workshops (O’Brien et al., 2016) and user-centered design (Cafazzo, Casselman, Hamming, Katzman, & Palmert, 2012) help to make the intervention attractive, clear and relevant to the user.

Producing final program materials such as posters and videos may involve creative consultants, artists or graphic designers. IM suggests writing design documents to guide the creation and reviewing of the materials: They can help in ensuring that behavioral science insights and intervention strategies are adequately transferred into actual material production.

D. Empirical Optimization Once the intervention program is designed and materials developed into a ‘beta’ version, there is the need for refinement and optimization. Building in time for this extra step will increase future acceptability and feasibility of the intervention. There are rigorous methods that can be used to get extra information to proceed with empirical optimization/ refinement of the intervention prior to wider scale evaluation, such as the Multiphase Optimization Strategy (MOST).

Qualitative and/or quantitative methods can facilitate optimization/refinement.

MOST is a framework for robust empirical optimization and evaluation of behavior change interventions (Collins et al., 2007; Collins, Nahum-Shani, & Almirall, 2014).

MOST proposes three phases: preparation (i.e., develop theoretical model and highlight uncertainties about most effective intervention features), optimization (i.e., component selection using empirical testing), and evaluation (i.e., definitive RCT). At the optimization phase intervention developers gather empirical information on each intervention feature by conducting a randomized experiment (e.g., factorial design, fractional factorial design, SMART designs). The results from this formal testing inform decision-making process in terms of feature selection and formation of the optimized intervention. The framework proposes an iterative process stating that if an optimized intervention is shown to be effective through a formal test, it can be made available to the public. The key element in MOST is the processes by which a multicomponent behavior change intervention and its components are optimized before a definitive trial or potentially while the intervention is in use (e.g., optimization of an existing app).

Qualitative methods provide a complementary approach to support the development and refinement of an initially drafted intervention. Developers should aim to understand and incorporate the perspectives of those who will use the intervention by undertaking iterative qualitative research.

This is important for digital interventions (Baek, Cagiltay,

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Boling, & Frick, 2008) but also for traditional methods of delivery. An example on how this can be translated in practice is by eliciting and analyzing service users’ reactions to the intervention and its elements. It might also be important to conduct consultation with topic experts (e.g., computer scientists) and other stakeholders (e.g., healthcare practitioners) of the intervention to accommodate their views and expertise (Presseau, Mutsaers, et al., 2017; Rodrigues, Sniehotta, Birch-Machin, Olivier, et al., 2017). This can be achieved using research methods such as focus groups, individual semi-structured interviews coupled with a think-aloud process. Mixed methods can also be used to refine an intervention coupling both qualitative with quantitative forms of collecting information that can inform refinement.

E. Evaluating the Intervention Developing interventions that test explicit hypotheses could allow for synergy between knowledge generated via the implementation and evaluation of interventions and theories, allowing for their test and evolution. In the pilot and feasibility stage the feasibility and acceptability of the intervention and evaluation procedures is tested and if needed optimized and additional information needed to design the evaluation is collected (Eldridge et al., 2016; Lancaster, 2015). Once a viable intervention and evaluation protocol has been achieved, a full-scale evaluation of whether the intervention has its intended effects on the main outcome should take place assuming resources are available to do so.

The study design should be chosen based on what is fit for purpose – based on question, circumstances, and specific characteristics of the study (e.g., expected effect size and likelihood of biases). Considering the range of experimental and non-experimental approaches should lead to more appropriate methodological choices (Shadish, Cook, & Campbell, 2002). UK MRC guidance strongly encourages considering randomization, due to it being the most robust method of preventing selection bias (i.e., intervention recipients systematically differing from those who do not). In case a conventional individually-randomized parallel group design is not appropriate, evaluators should consider other experimental designs, for example, cluster-randomized trials, stepped wedge designs (Li et al., 2017), preference trials and randomized consent designs, or n-of-1 designs (Craig et al., 2008; Shadish et al., 2002). Even when an experimental approach may not be feasible, for example, the intervention is irreversible, robust nonexperimental alternatives should be considered. In any case, intervention evaluators should be conscious of the need to avoid underpowered trials to prevent producing research waste (Ioannidis et al., 2014).

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F. Process Evaluation In addition to a formal outcome evaluation, an important part of intervention development and evaluation involves understanding how and for whom an intervention works or does not. Process evaluation is key to explore the functioning of a complex intervention and it involves examining fidelity, mechanisms of impact, and contextual factors (Moore et al., 2015). A process evaluation can involve the use of various qualitative and/or quantitative methods to increase understanding of outcomes, how these are achieved and how can interventions be improved (Moore et al., 2015). For instance, a process evaluation can include self-completed questionnaires (E. H. Evans et al., 2015), semi-structured interviews (Sainsbury et al., 2017), data-driven interviews (Leslie et al., 2016), and non-participant observations to understand the functioning of the different features of an intervention (Hardeman et al., 2008). It should be noted that process evaluation can be conducted at various stages of intervention development and evaluation, serving a different function in each: in the feasibility and pilot study phase it may, for example, shed light on intermediate processes and acceptability of implementation procedures (Hankonen, Heino, Hynynen, et al., 2017), in the effectiveness evaluation trial, fidelity, impact mechanisms and context (Presseau et al., 2016), and finally in the post-evaluation implementation, its function may be to investigate the routine uptake or normalization into new context (May & Finch, 2009; Moore et al., 2015).

For example, in the feasibility study of the “Let’s Move It” intervention to promote physical activity in vocational school youth, the identification of activities most and the least frequently taken up by the participants enabled an improvement or removal and replacement of such suboptimal program components (Hankonen, Heino, Hynynen, et al., 2017).

integrated as a key consideration at each stage of an intervention’s development and evaluation process. Intervention co-creation provides some ownership to those involved with its implementation but does not guarantee that others will use it. The field of Implementation Science has emerged to robustly develop and evaluate interventions to support real-world implementation process itself. The “actors” whose behavior is targeted thus shifts from patients and citizens, to those who deliver the intervention in routine settings (e.g., doctors, nurses, teachers), and the same rigorous process of intervention design advocated above for patient/citizen-focused interventions should form the basis of an implementation intervention, including development, piloting and evaluation. Just as mere information provision is unlikely to support someone to quit smoking or eat more healthily, so too is the provision of information to a healthcare provider about an effective health behavior change intervention unlikely to be sufficient to change routine practice. Instead, change in healthcare provider behavior should be assessed and informed by behavior change theory qualitatively, quantitatively, determinants reviewed, pilot testing, and robust randomized evaluation conducted.

Indeed, Cochrane reviews of strategies for supporting healthcare professional behavior exist (e.g., Ivers et al., 2012), and there is a movement toward clarifying behavior change techniques targeting change in healthcare provider behaviors alongside those focused on patients (Presseau et al., 2015). Such implementation research is best achieved in collaboration with those with the infrastructure within which to implement the intervention (e.g., health services, schools). There remains much opportunity to apply principles of behavior change intervention development and evaluation to changing the behavior of those who deliver interventions routinely.

G. Implementation: Real-World Application

Conclusion: Reflections and Challenges

Once a health behavior change intervention is evaluated and demonstrated to be effective, this evaluation contributes to the wider evidence in favor of the intervention.

As replicated evidence mounts and is synthesized in favour of the intervention, there can be greater confidence in promoting its implementation and routine use as part of a new standard of care in health services, community services, schools, the workplace and/or online (Peters, Adam, Alonge, Agyepong, & Tran, 2013). Demonstrating that an intervention is effective does not guarantee that it will be adopted or implemented beyond the scope of the project that developed and evaluated it. As suggested within RE-AIM, real-world implementation issues should be

Methods for behavior change intervention development have progressed considerably over the last four decades and made a significant contribution to the translation of health behavior science into public health and health care.

Guidance for the outcome and process evaluation of complex interventions has increased both the quality of interventions as well as their reporting (Hoffmann et al., 2014). Moving away from an academically dominated approach toward a multidisciplinary process with meaningful involvement of stakeholders and users working toward codesign and joint ownership while maintaining commitment to evidence-based practice and scientific theory, has considerably increased the potential for impact in the real

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world. This further underscores that reach, implementation, adoption, and maintenance – not just effectiveness – must be optimized to create maximal impact. Intervening is increasingly seen from a complex systems perspective with a view to modifying the behavioral as well as the wider social and environmental determinants of behavior and recent developments reflect this emphasis on environmental interventions and context (Aunger & Curtis, 2016; Dolan et al., 2012; Hollands et al., 2017).

Policy and practice partners often require solutions in a timely fashion and at limited budgets. Scientific methods are usually conceived to reach optimal solutions but impact might depend on creating the optimal solution in a given context of time and budget. Increasing chances of acceptability and feasibility by involving key stakeholders from the start, we can design interventions that have the highest likelihood of delivery to time and budget. These stakeholders ideally include policymakers and other agents who are gatekeepers to long-term implementation and dissemination. By partnering early and over the long term the seeds for incremental evaluation will be sow. This will increase flexibility and allow for immediate response to identified needs while also contributing to science over the longer term. Hence, involving them early on enables longsighted planning for real-world impact.

Intervention development frequently involves a systematic review, extensive patient and public involvement and additional original mixed method research before conducting a feasibility study and subsequently for a definitive study evaluating the effectiveness. While defensibly robust, this best practice approach can be time consuming, which may be appropriate in many settings. However, in domains characterized by very rapid innovation cycles, such as mobile phone apps for public health, more efficient approaches are needed and can be considered. One option rarely raised in this literature is the option not to develop an intervention but to adapt or retrofit an existing one. Such an approach is sensible where evidence synthesis or a scoping review suggests that an existing intervention has a good evidence base. An example of an adapted intervention is the “Waste the Waist,” (Gillison et al., 2012) which was based on an intervention used in Australia (Absetz et al., 2007; Laatikainen et al., 2007).

We suggest that intervention developers should avoid following formal methods in a linear “cookbook” fashion.

Instead, we advocate for transparency of reporting of strategic decisions inspired by an iterative value of information approach where at each stage of the development the opportunity costs for conducting additional research or seeking further evidence is weighted against the likely improvement to the interventions resulting from it – informed by a strong multidisciplinary conceptual model.

This allows some flexibility and adjusts the process to the

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available time and resource. It is important to highlight which design decisions are based on evidence but also to acknowledge those decisions made in the process of intervention development that could not be based on available evidence.

Finally, it is possible to use methods of empirical optimisation such as MOST (Collins et al., 2007), sequential multiple assignment randomized trial (SMART; Cheung, Chakraborty, & Davidson, 2015) or built in n-of-1 trials (McDonald et al., 2017) to empirically optimize interventions while being used, a possibility that benefits particularly from digital intervention platforms. Developing realworld interventions is an opportunity to create impact from behavioral science and to contribute to addressing some of the most pressing issues of our time.

Acknowledgments Angela Rodrigues and Falko F. Sniehotta are funded by Fuse, the Centre for Translational Research in Public Health, a UK Clinical Research Collaboration Public Health Research Centre of Excellence based on funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council.

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Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35, 565–576. https://doi.org/10.1002/ejsp.258 Teufel-Shone, N. I., Siyuja, T., Watahomigie, H. J., & Irwin, S. (2006). Community-based participatory research: Conducting a formative assessment of factors that influence youth wellness in the Hualapai community. American Journal of Public Health, 96, 1623–1628.

Webb, T. L., Michie, S., & Sniehotta, F. F. (2010). Using theories of behavior change to inform interventions for addictive behaviors.

Addiction, 105, 1879–1892. https://doi.org/10.1111/j.13600443.2010.03028.x Wight, D., Wimbush, E., Jepson, R., & Doi, L. (2016). Six steps in quality intervention development (6SQuID). Journal of Epidemiology and Community Health, 70(5), 520–525. https://doi.org/ 10.1136/jech-2015-205952 Windsor, R. A. (2015). Evaluation of health promotion and disease prevention programs: Improving population health through evidence-based practice. New York, NY: Oxford University Press.

Witteman, H. O., Presseau, J., Nicholas Angl, E., Jokhio, I., Schwalm, J. D., Grimshaw, J. M., . . . Ivers, N. M. (2017). Negotiating tensions between theory and design in the development of mailings for people recovering from acute coronary

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syndrome. JMIR Human Factors, 4, e6. https://doi.org/10.2196/ humanfactors.6502 World Health Organization. (2014a). Global Health Estimates: Deaths by cause, age, sex and country, 2000–2012. Retrieved from http://www.who.int/healthinfo/global_burden_disease/ en/ World Health Organization. (2014b). Global status report on noncommunicable diseases 2014. In WHO.. Geneva, Switzerland: WHO. Yardley, L., Morrison, L., Bradbury, K., & Muller, I. (2015). The person-based approach to intervention development: Application to Digital health-related behavior change interventions (Eds.), Journal of Medical Internet Research, 17, e30. https:// doi.org/10.2196/jmir.4055

Justin Presseau, PhD, is a Scientist and Health Psychologist at the Ottawa Hospital Research Institute and Assistant Professor in the School of Epidemiology, Public Health and Preventive Medicine at the University of Ottawa. His research draws upon theories and approaches from health psychology and behavioral medicine to develop and evaluate interventions focused on changing healthcare professional behaviors and health behaviors of patients and the public.

Received May 20, 2017 Revision received February 21, 2018 Accepted April 19, 2018 Published online August 16, 2018

Angela M. Rodrigues, PhD, is Research Associate in the Faculty of Medical Sciences, Institute of Health & Society, at Newcastle University and in Fuse, the UK Centre for Excellence for Translational Research in Public Health.

Vera Araújo-Soares Institute of Health & Society Faculty of Medical Sciences Newcastle University Baddiley-Clarke Building Richardson Road Newcastle upon Tyne NE2 4AX UK vera.araujo-soares@newcastle.ac.uk

Vera Araújo-Soares, PhD, is a Senior Lecturer in Health Psychology in the Faculty of Medical Sciences, Institute of Health & Society and the School of Psychology, Newcastle University, UK. Her research focuses on the development and assessment of evidence-based interventions for the promotion of health behaviors, prevention and self-management of chronic conditions. She is President Elect of the European Health Psychology Society.

Falko F. Sniehotta, PhD, is Director of the NIHR Policy Research Unit Behavioural Science and Professor of Behavioral Medicine and Health Psychology in the Faculty of Medical Sciences, Institute of Health & Society, at Newcastle University and in Fuse, the UK Centre for Excellence for Translational Research in Public Health. His research focuses on the development and evaluation of complex behavioral interventions for individuals and populations. He is past president of the European Health Psychology Society.

Nelli Hankonen, PhD, is Assistant Professor (Social Psychology) in the Faculty of Social Sciences at the University of Helsinki. Her research focuses on changing motivation and behavior in the area of well-being and health and in mechanisms of change in complex interventions.

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Appendix A Intervention Development and Evaluation Frameworks and Purpose Frameworks

Purpose

MRC Framework for the Development of Complex Interventions (Craig et al., 2008)

To provide guidance on the process of development, evaluation and implementation of a target intervention.

Intervention Mapping (Bartholomew Eldredge et al., 2016)

To describe the iterative path (six phases) for designing, implementing and evaluating an intervention.

To support the development of an intervention designed to change clinical behaviour based on a theoretical framework.

The model aims to explain health-related behaviours and environments, and to design and evaluate the intervention.

This tool details how to design and select interventions according to a behaviour analysis, mechanisms of action, and the interventions required to change those mechanisms. This tool is also used to link influences on behaviour to potential intervention functions and policy categories.

To design interventions based on rigorous, in-depth understanding of the psychosocial context of users, and derived from iterative in-depth qualitative research.

To provide a pragmatic and systematic six-step guide to intervention development, maximising its likely effectiveness.

To describe a systematic, sequential approach to integrate scientific evidence, expert knowledge, and stakeholder involvement in the co-design and development of an intervention.

A conceptual framework to integrate the roles of knowledge creation and knowledge application, contributing to sustainable, evidence-based interventions.

To provide guidance on the process of treatment development by suggesting the use of a progressive, transdisciplinary framework to facilitate the translation of basic behavioural science findings to clinical application.

To detail the process involved in designing interventions to gain more cumulative science of health behaviour change.

To provide a guide to the optimization and evaluation of multicomponent behavioural interventions.

Steps for developing a theory-informed implementation intervention (S. D. French et al., 2012) PRECEDE-PROCEEDE (Green & Kreuter, 2005)

The Behaviour Change Wheel (Michie, Atkins, & West, 2014)

The Person-Based Approach to Intervention Development (Yardley, Morrison, Bradbury & Muller, 2015) 6SQuID: 6 steps in quality intervention development (Wight, Wimbush, Jepson, & Doi, 2016) Evidence-guided co-design (O’Brien et al., 2016)

Knowledge-to-Action (KTA) cycle (Graham et al., 2006)

ORBIT model (Czajkowski et al., 2015)

EM Model (Sheeran, Klein, & Rothman, 2017)

Multiphase optimization strategy (MOST; Collins, Murphy, & Strecher, 2007) Social Marketing (e.g., Lefebvre, 2011)

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The systematic application of marketing concepts and techniques to achieve behaviour change.

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Appendix B Key Considerations for the Reporting of Intervention Development Preparatory work: Describe the team and planned development process a. Describe the expertise of the core team and advisory stakeholder team involved in development/design process (in different phases): multi-disciplinarity, prior experience b. Describe time used (and available) for intervention development process (e.g. length of design period, frequency of design meetings, etc.) c. Describe other resources available d. Describe possible funder/commissioner demands/limitations/requests for the intervention or the development process (e.g. future use, use of technology, limited financial resources, quick timeline for development) e. Describe original general aims and intended use/scalability of the future intervention Step 1: Analyse the problem and develop an intervention objective a. Describe how the planning group worked to define the health problem, health behaviors, target health behaviors b. Describe potential market analysis, segmentation, and possible subsequent resulting decisions c. Describe the decision process leading to prioritisation and selection of target group(s) and behavior change targets d. Describe how preparatory behaviors and networks of other behaviors were identified and prioritised Step 2: Define the scientific core of the intervention (i) Understand causal/contextual factors (Causal Modelling) a. Describe formal (behavioral) theories used in understanding the predictors of the target health behavior b. Describe how key uncertainties were identified to select aim of evidence synthesis c. Describe literature search and review process d. Describe the rationale/aims and the process of (possible) original empirical research e. Describe rating of influencing factors (psychological, social, predictors/mechanisms) for changeability and relevance (ii) Develop a logic/theoretical model a. Describe the process of developing the logic model (if possible, include early and later versions of the logic model)

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b. Describe key explicit criteria (e.g. acceptability, cost-effectiveness) in making decisions for logic model c. Describe whether and which other similar existing interventions were used in developing the logic model, or whether an existing intervention was used as core basis and retrofitted to account for new context d. Describe key uncertainties left in the causal chain or logic model and the possible “weak links” the development team thinks there may remain e. Assess evaluability potential of such an intervention f. Develop a dark logic model that describes considerations made around potential unintended consequences and steps made to avoid it (iii) Define intervention features a. Describe decision processes (including considered alternative options) leading to decisions about i. program components/activities ii. intermediate targets iii. behavior change techniques or methods to target predictors/mechanisms e.g. to what extent various combinations of BCTs were explicitly considered and left out iv. dose/intensity/frequency/duration of intervention v. delivery channel(s) vi. providers (expertise/background/training) vii. location/infrastructure b. Describe whether and how anticipated acceptability of intervention among target participants and/ or providers and/or commissioners was investigated c. Describe the decision processes related to room for local adaptation and necessity of fidelity for various components Step 3: Design/Develop intervention materials a. Describe how protocol was written b. Describe key principles in designing materials (e.g. design documents) c. Describe how stakeholder input was obtained for key decisions (e.g., scenario-based work) d. Describe whether and how small-scale pre-testing of intervention components (e.g. group exercises, key messages) was conducted, to make decisions about program content e. Describe decisions leading to personalization and tailoring (how and why) f. Describe the process of developing procedures to ensure fidelity

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Step 4: Conduct an empirical optimization a. Describe key (research) questions for empirical optimisation b. Describe empirical design used in testing the intervention (or its components), including data collection methods, sample, etc. c. Describe data analysis methods d. Describe whether and how qualitative and quantitative methods were mixed e. Describe how judgments and optimization decisions were made based on empirical testing Step 5: Design and undertake intervention evaluation a. Describe the plan for evaluation of effectiveness b. Describe rationale (e.g. resources available, funder interests) leading to decisions regarding evaluation c. Describe the plan for evaluating processes d. Describe the intended use of information gained (e.g. for potential adaptations)

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Step 6: Design implementation and undertake implementation evaluation a. Describe how decisions related to implementation (specific plans on how the intervention will be used in routine practice) were done, e.g., was the implementation informed by a theoretical framework or a model b. Describe the implementation intervention development process c. Describe reach and allowed adaptations d. Describe the plan for evaluation of implementation e. Describe rationale (e.g. resources available, funder interests) leading to decisions regarding evaluation f. Describe the plan for evaluating processes of implementation g. Describe the intended use of information gained (e.g. for potential adaptations)

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Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Treating Illness Distress in Chronic Illness Integrating Mental Health Approaches With Illness Self-Management Joanna L. Hudson and Rona Moss-Morris iHealth Psychology Section, Psychology Department, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK

Abstract: Cognitive-behavioral therapy (CBT) is an evidence-based treatment for depression and anxiety recommended for those with and without physical long-term conditions (LTCs). However, the cognitive-behavioral mechanisms targeted in CBT protocols are based on empirical cognitive-behavioral models of depression and anxiety. In these models, emotions are conceptualized as primary mental health disorders rather than a reaction to the challenges of living with a LTC commonly referred to as illness distress. This raises important clinical questions with theoretical implications. These include: Is the experience of illness distress conceptually distinct from primary mental health diagnoses of anxiety and mood disorder? Are there unique cognitive-behavioral mechanisms related to illness self-management, which should be incorporated into CBT for illness distress? How can illness self-management interventions be embedded within existing CBT protocols for depression and anxiety? To address these questions, we distinguish between primary mental health disorders and illness distress conceptually and explore the impact of this on tailored treatment planning and engagement. Second, we review how health psychology theoretical models can help to inform modifications of existing cognitive-behavioral treatments for anxiety and depression to better support the needs of individuals experiencing illness distress. Third, we provide examples of how to embed processes important for illness self-management including, illness cognitions and adherence, alongside existing CBT techniques. The mechanisms and intervention techniques discussed may help to inform the development of integrated CBT treatments for illness distress for future hypothesis testing in comparative effectiveness trials.

Keywords: illness distress, depression, anxiety, common-sense model of self-regulation, stress and coping model

Background Common mental health disorders, including depression and anxiety, are 2–3 times more likely to occur in people with physical long-term conditions (LTCs) compared with the general population (Naylor et al., 2012). People with comorbid mental and physical health conditions have poorer health outcomes compared with either condition alone (Moussavi et al., 2007). This is associated with increased LTC healthcare costs by an average of 60% (up to £13 billion annually in England; Naylor et al., 2012). In response to these findings, the need to implement integrated mental and physical health care is recognized (Naylor et al., 2016). Collaborative care is a promising integrated care delivery framework recommended by UK national clinical guidelines for the management of moderate to severe depression in LTCs (National Institute for Health and Clinical Excellence, 2009).

Collaborative care is a health service delivery model originally developed to improve the management of depression

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by non-mental health specialists in primary care (Gunn, Diggens, Hegarty, & Blashki, 2006). It includes four core components of care delivery (Gunn et al., 2006): (i) a multi-professional approach to care, (ii) access to evidence-based treatment protocols (e.g., manualized cognitive-behavioral therapy (CBT), pharmacotherapy dosing guidelines), (iii) proactive case management, and (iv) enhanced methods of multi-disciplinary communication (e.g., shared note systems).

This coordinated approach provides clear organizational frameworks for implementing integrated care. However, the latest evidence found only modest effects of collaborative care on depression outcomes in people with LTCs compared with usual care (Panagioti, Bower, Kontopantelis, et al., 2016). Collaborative care is a complex and multifaceted intervention. Several factors likely moderate its effectiveness. Indeed, evidence suggests collaborative care

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is more effective when treatment protocols include psychological interventions compared with protocols reliant on pharmacotherapy only (Coventry et al., 2014).

Given that collaborative care is a recommended framework for implementing integrated care and preliminary findings suggest psychological interventions bolster its effectiveness, it is important to refine our understanding of the type of psychological interventions that are likely most effective for managing depression and anxiety. Current psychotherapies recommended for depression in LTCs (National Collaborating Centre for Mental Health, 2010) are based on theoretical models of primary mental health disorders. They do not incorporate CBT management protocols specific to the challenges of having a LTC. Preliminary evidence collected as part of the UK Improving Access to Psychological Therapy (IAPT) initiative among people with LTCs showed significantly greater benefits across mood and quality of life outcomes for implementation sites which integrated CBT approaches with LTC self-management skills compared with sites who used standard CBT treatments for primary mental health disorders alone (de Lusignan, Jones, McCrae, Cookson, & Chan, 2016). In addition, a randomized controlled trial compared tailored diabetes-specific psychological therapy to a standard depression treatment for adults with diabetes and comorbid depression (Nobis et al., 2015). It showed considerably larger effects on depression outcomes for the tailored diabetes and depression treatment arm compared with the standard depression treatment arm (Cohen’s d = 0.89). However, there is no agreed theoretical framework or manualized treatment protocol, which sufficiently integrates comorbid mental and LTC self-management needs.

The goals of this article are threefold. First, the conceptual distinctions between the terms depression, anxiety, and distress are discussed. The relevance of these distinctions is highlighted by outlining how they can influence the detection and tailored treatment of negative emotions in LTCs. Second, theoretical and empirical research from the health psychology field is briefly reviewed. The aim is to highlight processes that promote successful LTC selfmanagement for integration into CBT treatment protocols for depression and anxiety. Third, a discussion of how to embed LTC self-management skills alongside existing cognitive-behavioral intervention skills is provided.

Conceptualizing Depression, Anxiety, and Distress in the Context of LTCs The conceptualization of depression and anxiety in LTCs has implications for its identification and management. Ó 2019 Hogrefe Publishing

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Psychiatrically defined diagnostic criteria are commonly used (World Health Organization, 1996). This approach applies categorical thresholds to core lists of somatic (e.g., sleep, energy) and affective (e.g., low mood) symptoms to distinguish those who have a mood or anxiety disorder versus those that do not. Subcategories within the anxiety and mood disorders provide diagnoses such as panic disorder or depressive disorder (American Psychiatric Association, 2013). This pragmatic method to identification allows the allocation of limited mental health resources to those highest in clinical need (Goldberg, 2000). However, symptoms common to both depression and anxiety often co-occur yet fail to meet diagnostic criteria for either condition alone (Das-Munshi et al., 2008). Subthreshold symptoms of depression and anxiety in people with LTCs are particularly common (Geraghty et al., 2016; Katon & Roy-Byrne, 1991).

To address these concerns, a Mixed Anxiety and Depressive Disorder (MADD) diagnostic category was developed (World Health Organization, 1996). The Diagnostic and Statistical Manual of Mental Disorders also has “Mood Disorder Due to a General Medical Condition” classified within the affective disorders (American Psychiatric Association, 2013).

However, formally diagnosing depression and anxiety in LTCs may be viewed as pathologizing the experience of negative emotions which occur in response to an objectively challenging illness. Both patients and practitioners have voiced a preference to normalize the experience of distress in LTCs (Coventry et al., 2011). The terms distress, stress, or illness distress are commonly used to describe negative emotional responses to chronic illness (Esbitt, Tanenbaum, & Gonzalez, 2013; Leventhal, Halm, Horowitz, Leventhal, & Ozakinci, 2004; Steptoe & Ayers, 2004). Distress is defined as a negative emotional reaction to an adverse event/stressor (Snoek, Bremmer, & Hermanns, 2015; Steptoe & Ayers, 2004). Rather than being a single stressor having a LTC can be seen to generate a wide range of stressors.

Illness stressors include either acute events (e.g., diagnosis) or chronic illness self-management challenges (e.g., treatment adherence; Moss-Morris, 2013). Illness distress is considered multidimensional and may include: depression, anxiety, anger, guilt, and shame (Browne, Ventura, Mosely, & Speight, 2013; Kreider, 2017; Steptoe & Ayers, 2004).

Experiencing distress in response to acute challenging events is often considered adaptive (Lazarus, 1991). Negative emotions signal that there is an environmental threat that requires attention. However, defining what constitutes normal illness distress compared with a response that requires clinical intervention relies, to an extent, on clinical judgment (Lazarus, 1991). Considering the severity of illness distress, its duration, and consequential impact on function is important. However, the concept of illness distress is criticized for its lack of specificity compared European Psychologist (2019), 24(1), 26–37

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with diagnostic classifications (Steptoe & Ayers, 2004).

Illness-specific measures of distress are emerging with psychometrically defined cutoffs to identify individuals who would benefit from clinical intervention (Ma et al., 2014; Polonsky et al., 2005; Snoek et al., 2015). In LTC contexts where illness-specific measures of distress are lacking the use of composite scores of depression and anxiety with a priori defined cutoffs may offer a useful substitute (Chilcot et al., 2018; Kroenke et al., 2016).

From here on in, the term primary mental health disorder will be referred to as a condition that predated the onset of a LTC or appeared unrelated to the LTC (i.e., two co-occurring but not necessary interlinked illnesses; Mc Sharry, Bishop, Moss-Morris, & Kendrick, 2013). This includes the terms primary depressive disorder and/or anxiety disorders. In contrast, the term illness distress will be used to refer to negative emotional states that are, at least in part, a clear consequence of LTC stressors. These may or may not reach diagnostic thresholds for a specific mental health disorder.

These conceptual distinctions have two important treatment implications. First, evidence is emerging to suggest that primary mental health disorders and illness distress explain unique variance in health outcomes (Barefoot & Williams, 2010; Snoek et al., 2015). These findings may indicate that the mechanisms, which trigger and sustain primary mental health disorders, differ from those, which trigger and sustain illness distress. If this is the case, then illness distress likely requires modified versions of existing psychotherapies. For example, adapting CBT to integrate the symptom and self-management needs of people with LTCs. Second, the label assigned to a person’s negative emotion is important for treatment engagement. Qualitative findings suggest disengagement from psychotherapy is related to healthcare professionals labeling negative emotions with a term that does not accurately reflect the challenge of struggling to adjust to having a LTC (Hind et al., 2014; Knowles, Chew-Graham, Adeyemi, Coupe, & Coventry, 2015).

In the next section, theoretical models from the health psychology literature are briefly reviewed. These theories can help to inform the selection of LTC-specific self-management techniques as potential candidate intervention strategies for embedding alongside existing CBT treatments to address illness distress.

Theoretical Models of Coping and Self-Management Lazarus and Folkman’s (1984) model of stress and coping suggests emotional responses to challenging events are shaped by two core processes: appraisal and coping.

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Appraisal occurs at two levels. Primary appraisal consists of an evaluation of the personal significance of the event.

It may be appraised as having the potential for harm, loss, or challenge. Alternatively, it may be appraised as a benign occurrence. When the event is appraised as having personal significance, secondary appraisal follows. During secondary appraisal, coping resources for managing both the event (i.e., source of distress) and its emotional consequences are evaluated. The process of primary and secondary appraisal informs the type of coping responses implemented.

Coping consists of cognitive and behavioral strategies aimed at relieving the source of distress using problemfocused coping while also managing the emotional response to the event using emotion-focused coping. LTCs can be viewed as consisting of a series of potential stressors such as lifestyle change or adapting to disability. Appraising these illness-specific events as posing a threat, harm, or loss will likely lead to emotional distress unless secondary appraisals of coping efficacy and available coping resources can lessen these negative appraisals. Greater perceived control over the event/stressor is associated with increased problem-focused coping (e.g., problem-solving, seeking illness information). Less perceived control is associated with emotion-focused coping (e.g., avoidance, cognitive restructuring (Folkman & Greer, 2000).

The stress and coping theoretical model helps to differentiate illness distress from primary psychopathology.

It also emphasizes the importance of perceptions of control and available resources for guiding the type of coping strategies implemented. However, it does not elaborate on specific illness cognitions and behaviors that are potentially important for managing illness distress. Here, the common-sense self-regulatory model (CS-SRM) may be more helpful (Leventhal, Phillips, & Burns, 2016). The CS-SRM is essentially a cognitive-behavioral model.

Patients’ illness and treatment cognitions guide their choice of illness self-management behaviors (i.e., problem-focused behaviors). In addition, illness and treatment cognitions affect a person’s emotional response to illness and consequent emotion-focused coping strategies.

The CS-SRM suggests illness cognitions are generated by appraising illness-related information according to five domains (Leventhal et al., 2016): identity – assigning an illness label to specific physical signs and symptoms (e.g., diabetes to high blood sugar), timeline – generating an understanding of illness and/or symptom duration (e.g., acute vs. chronic), cause – assigning meaning to the aetiology of illness, consequences – appraising the impact of illness on current and future functioning, and control – evaluating the availability of personal resources and skills to manage the illness. These same five domains also generate treatment cognitions (Leventhal et al., 2016): identity – linking specific treatments (including lifestyle Ó 2019 Hogrefe Publishing

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change) as appropriate strategies to apply when specific signs and symptoms occur, timeline – anticipating a timescale for observing improvements in symptoms and their duration of effect, cause – interpreting treatment side effects as representing harm or evidence of efficacy/potency, consequences – experiencing real or perceived treatment side effects, and control – appraising treatment efficacy.

In line with the CS-SRM, a person’s illness and treatment cognitions guide a person’s choice of coping behaviors. This includes both illness self-management behaviors which overlap to some extent with the concept of problemfocused behaviors and more emotion-focused coping to deal with emotional responses to illness (Hagger, Koch, Chatzisarantis, & Orbell, 2017). Illness self-management behaviors include sustaining treatment adherence, lifestyle change, navigating complex health systems and consultations, and implementing appropriate action plans to manage symptoms, altered function, negative emotions, or a combination of these health outcomes which change over time (Leventhal et al., 2004, 2016). Illness and treatment cognitions and self-management behaviors are continually appraised (self-regulated) for their efficacy and updated in light of new information (e.g., symptom exacerbation, consultation with medical professionals; Leventhal et al., 2004).

Substantial empirical support exists for the relationships between illness and treatment cognitions and problemfocused illness self-management behaviors (Hagger et al., 2017; Richardson, Schüz, Sanderson, Scott, & Schüz, 2016). When common-sense cognitive representations of illness differ from the medical understanding of the condition, poor illness self-management may occur (Leventhal et al., 2016). For example, individuals with asthma show lower levels of adherence to preventative steroidal inhalers when they perceive asthma to be an acute rather than chronic condition because of its sporadic symptom presentation (Kaptein, Klok, Moss-Morris, & Brand, 2010). Even when individuals have medically accurate illness cognitions and are motivated to engage in appropriate illness selfmanagement behaviors a lack of self-efficacy to perform a specific illness self-management task and/or concrete action plan may prevent behavior change (Leventhal, Leventhal, & Breland, 2011). Interventions to improve illness self-management therefore focus on supporting individuals to become coherent self-managers. This involves guiding individuals to develop accurate illness and treatment cognitions and linking these cognitions to defined procedural action plans (Horowitz, Rein, & Leventhal, 2004; Leventhal et al., 2004; Petrie, Cameron, Ellis, Buick, & Weinman, 2002; Petrie, Perry, Broadbent, & Weinman, 2012). Where self-efficacy is an issue realistic grading of actions plans tailored to an individual’s level of perceived Ó 2019 Hogrefe Publishing

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competence helps build confidence in self-management (Hibbard & Gilburt, 2014).

A particular challenge to bear in mind when supporting illness self-management behaviors is the lack of immediate positive reinforcement relative to substantial behavioral efforts (Leventhal et al., 2004). For example, lifestyle changes and adherence to complex medication regimens with negative side effects may be juxtaposed with the absence of immediate reward. In operant terms, side effects may be viewed as a form of “punishment” for engaging in the behavior. Conversely, nonadherence/avoidance of treatment acts as a negative reinforcer by removing aversive side effects. This strengthens the unhelpful nonadherent behavior. Thus, sustaining engagement in illness self-management tasks may require an individual to have a coherent understanding that successful illness self-management may not always lead to immediate and/or observable gains in health outcomes or a return to the “normal” self (Leventhal et al., 2004, 2016). To support this process, alternative criteria for evaluating successful and meaningful adherence are needed. SMART goal setting may be a useful intervention technique to apply (Doran, 1981). Indeed, goal setting is recommended by UK clinical guidelines to support behavioral change (National Institute for Health and Care Excellence, 2014). SMART goal setting provides a formal structure to allow the generation of illness self-management goals. For example, goals are concretely defined using the SMART acronym (Specific, Measurable, Achievable, Relevant, and Timely). Explicitly defining illness self-management goals using objective and measurable criteria while setting a realistic and achievable timeframe for their implementation allows a more objective assessment of illness self-management as opposed to relying on subjective and nonspecific symptom cues.

Further support for sustaining behavioral change may be achieved by providing opportunities to generate habitual illness self-management routines (Leventhal et al., 2011; Phillips, Leventhal, & Leventhal, 2013). Self-management habits are formed by generating cues (triggers) for adaptive illness self-management tasks (e.g., placing medication next to toothbrush; Leventhal et al., 2011). This promotes a switch from conscious deliberative illness self-management behaviors to automatic actions, thus freeing cognitive resources for other tasks.

Evidence is emerging to suggest pessimistic illness and treatment cognitions and low levels of treatment adherence are associated with elevated symptoms of depression and anxiety (Hudson, Bundy, Coventry, & Dickens, 2014; Katon, 2011; Richardson et al., 2016). When illness distress is present, implementing treatment interventions which target the processes outlined in the stress and coping model and CS-SRM would likely be beneficial. This would include: identifying illness stressors, exploring a person’s illness and European Psychologist (2019), 24(1), 26–37

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treatment cognitions to identify inaccurate appraisals for targeting and establishing what components of the stressors are controllable, so that procedural support can be implemented for behavior change. However, problem-focused coping directed toward managing the external illness stressors is likely a necessary but not sufficient intervention to address illness distress. Indeed, interventions which have provided intensive self-management support have typically failed to demonstrate synergistic improvements in mental health outcomes (Detweiler-Bedell, Friedman, Leventhal, Miller, & Leventhal, 2008). A person’s resources may be deployed toward problem-focused illness self-management thus limiting resource for emotion-regulation (DetweilerBedell et al., 2008). It may be that a person initially experiences illness distress but then goes on to develop a primary mental health disorder which requires a more explicit emotion-focused intervention (Moorey & Greer, 2012). Indeed, this situation may arise when the degree of control over the illness-related stressors is limited. In the final section, the potential for integrating the problem-focused illness self-management processes reviewed above alongside the processes amenable to emotionfocused coping using existing CBT techniques (Beck, 1976) are discussed.

Integrating LTC Self-Management Processes Alongside CBT Models of Depression and Anxiety In primary mental health disorders, the selection and sequencing of CBT techniques are informed by evidence-based knowledge of the perpetuating/maintaining processes that are present across (e.g., transdiagnostic processes) and within (e.g., disorder-specific processes) disorders (Tarrier & Johnson, 2007). Knowledge of transdiagnostic processes is advantageous when robust evidence to support the use of a particular CBT treatment protocol is lacking or when individuals present with comorbid depression and anxiety (Dudley, Kuyken, & Padesky, 2011). Having an awareness of these core maintaining processes is salient in the illness distress context because of its hypothesized multidimensional nature. As such, this generic transdiagnostic framework can be used to select CBT techniques known to effectively target transdiagnostic processes. However, this needs to occur alongside illness-specific selfmanagement intervention techniques. Indeed, this may improve the acceptability and efficiency of treatment delivery and bolster health outcomes.

Table 1 provides diabetes and chronic obstructive pulmonary disease (COPD) specific examples of integrating

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unhelpful illness responses to stressors alongside the key transdiagnostic processes common across primary mental health disorders (Dudley et al., 2011). The transdiagnostic factors include: behavioral avoidance, experiential avoidance, heightened attention, and repetitive negative thinking (rumination and/or worry). The remainder of this article focuses on how each of these transdiagnostic processes can be addressed to reduce illness-related distress. The aim is to demonstrate how illness self-management techniques may be integrated alongside evidence-based CBT techniques which map to and effectively target the transdiagnostic processes listed. It is important to highlight that the CBT techniques discussed draw on key summary texts and competency frameworks (Clark & Beck, 2011; McCracken, 2011; Roth & Pilling, 2007). Providing an extensive summary of their application and comparative effectiveness is beyond the scope of this article.

Behavioral Avoidance Patterns of behavioral withdrawal are observed in depression decreasing opportunities for pleasurable experiences and positive reinforcement (Jacobson, Martell, & Dimidjian, 2001). Withdrawal is hypothesized to occur because a person experiences aversive and punishing environmental events (Jacobson et al., 2001). This is likely occurring in the diabetes case example (Table 1). The person has withdrawn from his/her aversive insulin treatment and is no longer adhering to lifestyle changes. Consequently, this decreases opportunities for gaining diabetes mastery and its associated positive reinforcement. Validated self-report measures of treatment adherence are available to identify and explore the nature and extent of a person’s medication nonadherence (Horne, Hankins, & Jenkins, 2001).

Behavioral activation is an evidence-based CBT technique which targets withdrawal by encouraging individuals to gradually reengage with necessary routines and schedule opportunities for positive reinforcement (Jacobson et al., 2001). The application of these techniques in the illness distress context may benefit from drawing on problemfocused illness self-management processes to ensure the scheduling of tasks are congruent with LTC needs. In the diabetes example, scheduling routine tasks could include diabetes self-management tasks. Thus, the initial goal may be first to schedule a necessary yet achievable baseline diabetes-related behavior. SMART goal setting may be used (e.g., introducing a short 10-minute walk twice a day to reduce weight). This will allow a degree of illness mastery to be regained while also providing clear criteria to monitor the attainment of illness self-management goals. To establish this first baseline goal, it may be helpful to explore a

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Transdiagnostic mechanisms

Behavioural avoidance

Illness specific case example: Type 2 diabetes

Illness specific case example: COPD

Stressor: Progression onto insulin treatment

Stressor: Exacerbation in breathlessness upon exertion

– Diabetes specific behavioural avoidance/withdrawal in response to

– COPD specific behavioural avoidance in response to symptoms of

– –

Experiential avoidance – Denial – Distraction – Suppression

progression onto insulin.

Insulin may be operantly defined as a punisher. Thus a person may choose to avoid this punishing treatment regimen and not commence their insulin treatment schedule.

Opportunities to achieve illness mastery are diminished which impairs motivation and adherence to other illness self-management and/or pleasurable tasks are reduced (e.g., lifestyle change, seeing friends).

– Denying the negative consequences of insulin non-adherence. Thus, – – – – –

a person has an inaccurate illness representation – specifically the consequences domain from CS-SRM. Example cognition: “Kidney failure won’t happen to me, I only have Type 2 diabetes and not the serious kind of diabetes.”

Justifying non-adherence to a low perceived treatment efficacy of insulin. Thus, a person has an inaccurate treatment representation – specifically the treatment control domain from the CS-SRM. Example cognition: “All the other treatments for diabetes haven’t worked so why should insulin? I give up.”

Distraction from thoughts about the consequences of non-adherence. Thus, a person likely has low perceptions of control.

Example cognition: “I’m not going to read any information the Doctor provides – it’s just scare mongering and there’s nothing I can do.

I’ve tried.”

breathlessness.

– Exercise is avoided because of fear of symptom exacerbation.

– A person’s sedentary lifestyle results in physical deconditioning and breathlessness symptoms worsen on exertion.

– Not wanting to discuss COPD and consequently a person experiences increased somatic symptoms (suppression).

J. L. Hudson & R. Moss-Morris, Illness Distress in Long-Term Conditions

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Table 1. Transdiagnostic mechanisms common across primary mental health disorders and examples of how these may present in the context of illness distress

Self-focussed attention

– Focus on symptoms associated with COPD exacerbation (e.g., breathless-

gerated illness representation, specifically in the consequences domain of the CS-SRM. Example cognition: “Diabetes means always leading a life I do not enjoy.”

ness, tightness in chest). Thus, a person may have an inaccurate symptom representation (identity domain of the CS-SRM) through the misattribution of symptoms of anxiety to COPD.

– Focus on perceived injustice of illness and intent on curative model

– Focus on worst case scenario (e.g., death from symptom exacerbation) and

– Rumination/Worry

– –

of illness.

Thus, the person has a preoccupation on the cause, consequence and treatment control of diabetes from CS-SRM. Example cognitions: “Why do I have diabetes and not my siblings? I need to find a cure, without this I cannot move on with my life. Diabetes affects everything.”

using an inappropriate coping procedure in response to thought.

– Thus, the person has a catastrophic illness representation specifically in the identity, consequences and treatment control domains from CS-SRM.

– Example cognitions: “Exercise leads to death. I will rest to protect myself”.

Notes. COPD = chronic obstructive pulmonary disease; CS-SRM = common-sense self-regulatory model.

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– Focus on negative effects of diabetes. Thus, patient has an exag-

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person’s common-sense treatment cognitions. For example, their perceived benefits and barriers of engaging in walking, their treatment/lifestyle outcome expectations and how they are defining this, and any treatment concerns. Once basic behavioral goals are met, the next step is to collaboratively generate revised and suitably tailored SMART goals.

If a treatment goal is to work toward integrating insulin treatments into routine behaviors, then problem-solving (D’Zurilla & Goldfried, 1971) may be applied. During this process, gaps in declarative knowledge (e.g., illness and treatment cognitions) or procedural skills (e.g., administering insulin injections) can be identified (Leventhal et al., 2011) and graded action plans implemented. An assessment of a person’s illness and treatment cognitions may be facilitated/guided by self-report questionnaires. A commonly used questionnaire used to explore illness cognitions is the Illness Perceptions Questionnaire-Revised (MossMorris et al., 2002). Likewise, an assessment of person’s treatment cognitions may be guided by the constructs/ dimensions of the Beliefs about Medicines Questionnaire (Horne & Weinman, 1999).

Exploring treatment outcome expectancies when commencing a new treatment/self-management routine such as extensive lifestyle change will allow appropriate reward contingencies to be developed (National Institute for Health and Care Excellence, 2014). For example, a person expecting to observe a dramatic weight loss within one week of lifestyle change may need guidance to develop a more realistic timeframe for experiencing reward. Agreeing alternative sources of positive reinforcement for engaging in lifestyle behaviors may help reduce distress during this challenging period of habitual change. Involving social support networks for additional support may also be beneficial (National Institute for Health and Care Excellence, 2014).

Escape/avoidance behaviors commonly occur when a person interprets external environmental stimuli or internal bodily cues (e.g., symptoms) as a threat to survival (Wells, 1997). Because a person repeatedly escapes/avoids their feared situation, they fail to be provided with opportunities to learn the feared event does not occur (i.e., habituate to the feared environmental stimuli). In the COPD case example (Table 1), avoidance of exercise is likely occurring because of a fear of exacerbating breathlessness symptoms and the perceived life-threatening consequences of this (Livermore, Sharpe, & McKenzie, 2010).

Graded exposure targets avoidance mechanisms by gradually exposing individuals to their feared situation (Wells, 1997). Graded exposure may usefully be applied in the illness distress context. However, some adaptations may be needed to ensure symptoms common to illness distress, in this case breathlessness, are simultaneously managed from a problem-focused illness self-management perspective. Indeed, if breathlessness symptoms are not European Psychologist (2019), 24(1), 26–37

self-managed the outcome may be fatal (Livermore et al., 2010).

It therefore may be useful to first explore a patient’s illness and treatment cognitions to identify what is motivating their avoidance behaviors and provide a treatment rationale for graded exposure. This may involve explaining the longterm benefits of exercise for COPD (e.g., strengthens muscles, improves breathlessness, sustained independence) and the short-term effects of avoidance (e.g., temporary relief from feared situation) using psychoeducation (Bolton et al., 2013). Once the rationale for treatment is established, a therapist can work collaboratively with the physical healthcare team. In the COPD context, this may include pulmonary rehabilitation (Livermore et al., 2010). Prior to engaging with graded exposure therapy, a person’s symptom to illness label attribution (from the identity domain of the CS-SRM) may need exploring. A person may be erroneously attributing symptoms of breathlessness to a critical and life-threatening exacerbation in their health status as opposed to a normal response to exertion. Incorporating objective markers of symptoms where possible may help to develop accurate identity illness cognition domains (McAndrew et al., 2008). Re-engaging in feared behaviors may also be facilitated by providing action plans/coping procedures to assist with symptom experiences (Moorey & Greer, 2012). For example, in the case of COPD and breathlessness on exertion, this may involve applying the pursed lip breathing technique (Roberts, Stern, Schreuder, & Watson, 2009). Once symptom management action plans are established, graded exposure to the feared event (e.g., exercise) can occur. Thus, engagement with this intervention technique may be considerably longer in the illness distress context.

Experiential Avoidance Experiential avoidance can be defined as a process whereby a person takes active steps to prevent themselves from remaining in contact with unpleasant thoughts, feelings, or physical sensations (Hayes, Wilson, Gifford, Follette, & Strosahl, 1996). A person may implement cognitive avoidance strategies whereby they deny the objective and threatening long-term consequences of illness and/or implement distraction techniques. Alternatively, a person may attempt to suppress their outward expression of emotions to others (Peters, Overall, & Jamieson, 2014). Denial and distraction are demonstrated in the diabetes example (Table 1). An erroneous reframing of the possible negative consequences of diabetes and the need for insulin (treatment control cognition) is used to lessen feelings of threat. When confronted with information or images that threaten his/her erroneous beliefs, attention is allocated elsewhere. A person using Ó 2019 Hogrefe Publishing

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denial and/or distraction may not necessarily present with symptoms of illness distress as their coping strategies precariously sustain mood in the face of threatening illnessrelated information. Indeed, experiential avoidance may be a useful strategy to apply short term to allow the mobilization of coping resources. In these instances, it may be useful to explore a person’s illness and treatment cognitions using self-report measures such as the Illness Perception Questionnaire-Revised (Moss-Morris et al., 2002). In contrast, emotional suppression is illustrated in the COPD example.

The example is internally experiencing negative emotions but not expressing these to others. The use of this strategy may be particularly challenging to identify but the Beliefs about Emotions Scale (Rimes & Chalder, 2010) may help a therapist to gain a better understanding of a person’s coping techniques.

Written emotional expression may be a useful intervention technique for experiential avoidance (Pennebaker, 1997). Emotional expression provides a person with the opportunity to process emotional needs and can better inform therapeutic targets for action moving forward (Moorey & Greer, 2012). For example, it may enable the identification of erroneous illness and treatment cognitions and unhelpful problem-focused coping behaviors in response to these techniques. These can be addressed using cognitive-reappraisal techniques (Beck, 1976) alongside providing behavioral support for illness self-management which may also provide a behavioral experiment to challenge beliefs. However, a person may well hold objectively valid beliefs and emotional responses. Third wave CBT techniques including mindfulness and acceptance may be beneficial in these contexts (McCracken, 2011). It may be challenging to engage a person using experiential avoidance as a coping strategy. Remaining contextually focused on their illness while encouraging individuals to reflect on the costs and benefits of their current coping approaches may prove beneficial (Moorey & Greer, 2012).

Self-Focused Attention Attention toward the self commonly occurs in depression.

Typically, the focus remains on the inconsistency between the current and desired self (Pyszczynski, Hamilton, Herring, & Greenberg, 1989). In the context of chronic illness, as the diabetes example illustrates, selective abstraction may occur (Beck, 1976) whereby a person focuses on the negative impact of illness and discounts positive events.

The process of self-focused attention may be targeted using cognitive-reappraisal techniques (Beck, 1976). In the diabetes example, a person would be encouraged to generate a more balanced view of their diabetes by acknowledging the aspects of life diabetes does affect (e.g., diet and lifestyle); while counterbalancing this with aspects of life, Ó 2019 Hogrefe Publishing

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it does not impinge on (e.g., time with Grandchildren after school). A purely behavioral approach to targeting selffocused attention is to monitor the contexts in which it occurs and then generate an instrumental action plan for use in these contexts (Jacobson et al., 2001). This may include using mindfulness-based approaches (McCracken, 2011) or scheduling positive events with opportunities for mastery.

Hypervigilance toward environmental threats occurs in anxiety (Wells, 1997). As the COPD example illustrates, this may involve self-monitoring of internal bodily symptoms (e. g., breathlessness) to detect symptoms which pose a threat to health. Anxiety CBT protocols target hypervigilance through the generation of behavioral experiments (Wells, 1997). An individual is encouraged to become self-focused during sessions with a therapist. This attentional control task guides the individual to discover the impact of selffocused attention on symptom detection and intensification. However, in the context of chronic illness, a degree of self-monitoring is needed to regulate physical health.

An acceptable baseline for self-monitoring symptoms should be established in collaboration with the physical health team. Likewise, procedural action plans should be generated to allow a person to become skilled in managing symptoms when detected, which will likely enhance their sense of illness mastery and ultimately decrease anxiety.

Rumination and Worry Perseverative patterns of negative thinking occur commonly in primary mental health disorders and may include content focused on loss and/or worry about uncertainty (Dudley et al., 2011; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). Engaging in rumination and worry is motivated by a wish to problem-solve; however, paradoxically, it thwarts problem-solving abilities (Nolen-Hoeksema et al., 2008). In the diabetes example, the person is focused on the perceived injustice of their diabetes and is intent on finding a medical cure. Signs of “all or nothing” thinking are present in the diabetes case example (Beck, 1976).

The COPD example is showing catastrophic thinking patterns about the consequences of exercising (e.g., death) and is applying behavioral managements strategies to gain greater control and certainty over their future health (e.g., rest and symptom monitoring).

CBT intervention techniques target rumination and worry by scheduling positive and reinforcing events to block repetitive thinking cycles. These draw on mindfulnessbased principles to then subsequently work toward supporting effective problem-solving skills, instrumental behaviors, and reappraisal of unhelpful cognitions (Nolen-Hoeksema et al., 2008). In the diabetes example, once behavioral European Psychologist (2019), 24(1), 26–37

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strategies have been employed to thwart engagement in rumination, examining the benefits of remaining focused on the injustice of diabetes relative to achieving long-term goals may be explored in an empathic and supportive manner (Moorey & Greer, 2012). The person may be supported in shifting his/her focus from factors outside of his/her control to factors within his/her control, which provide opportunities for pleasure and mastery. In the COPD example, catastrophic patterns of thinking are present (e.g., death from exacerbation of breathlessness). The objectivity of these thoughts may be explored using graded exposure techniques discussed above (Wells, 1997). Alternatively, if the worrisome thoughts are objectively truthful, then emotion-focused coping strategies for managing the threat of uncertainty may be implemented by drawing on mindfulness and acceptance approaches (McCracken, 2011).

Conclusion The delivery of integrated mental and physical health care is a priority on international policy agendas. However, robust manualized CBT treatments capable of synergistically targeting mental and physical health needs are lacking. This paper has outlined factors to consider when developing and implementing integrated CBT. First, it may be useful to conceptually distinguish between primary mental health disorders and illness distress. The potential need for these distinctions was explored in relation to its impact on the detection and tailored management of negative emotions in LTC contexts. For individuals experiencing illness distress, a CBT manual which remains contextually anchored to their experience of living with a LTC may ultimately promote engagement with care and improve health outcomes. To inform the content of an integrated CBT manual, the health psychology literature was briefly reviewed. This literature highlighted the importance of accurate illness and treatment appraisals and behavioral management strategies for sustaining effective problemfocused coping in response to illness stressors. How to embed these processes alongside existing evidence-based CBT intervention techniques was explored. Transdiagnostic processes associated with symptoms of depression and anxiety were identified. How these processes may present in the context of illness distress was explored followed by a discussion of how CBT intervention techniques which map to and effectively target these transdiagnostic processes could be adapted to incorporate LTC-specific knowledge and problem-focused self-management strategies.

The suggestions in this review now require further hypothesis testing in robust clinical trials with embedded mechanism and efficacy evaluations.

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Nobis, S., Lehr, D., Ebert, D. D., Baumeister, H., Snoek, F., Riper, H., & Berking, M. (2015). Efficacy of a Web-based intervention with mobile phone support in treating depressive symptoms in adults with type 1 and type 2 diabetes: A randomized controlled trial. Diabetes Care, 38, 776–783. https://doi.org/ 10.2337/dc14-1728 Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008).

Rethinking rumination. Perspectives on Psychological Science, 3, 400–424. https://doi.org/10.1111/j.1745-6924.2008.00088.x Panagioti, M., Bower, P., Kontopantelis, E., Lovell, K., Gilbody, S., Waheed, W., . . . Coventry, P. A. (2016). Association between chronic physical conditions and the effectiveness of collaborative care for depression: An individual participant data metaanalysis. JAMA psychiatry, 73, 978–989. https://doi.org/ 10.1001/jamapsychiatry.2016.1794 Pennebaker, J. W. (1997). Writing about emotional experiences as a therapeutic process. Psychological Science, 8, 162–166.

Peters, B. J., Overall, N. C., & Jamieson, J. P. (2014). Physiological and cognitive consequences of suppressing and expressing emotion in dyadic interactions. International Journal of Psychophysiology, 94, 100–107. https://doi.org/10.1016/j. ijpsycho.2014.07.015 Petrie, K. J., Cameron, L. D., Ellis, C. J., Buick, D., & Weinman, J. (2002). Changing illness perceptions after myocardial infarction: An early intervention randomized controlled trial. Psychosomatic Medicine, 64, 580–586.

Petrie, K. J., Perry, K., Broadbent, E., & Weinman, J. (2012). A text message programme designed to modify patients’ illness and treatment beliefs improves self-reported adherence to asthma preventer medication. British Journal of Health Psychology, 17, 74–84. https://doi.org/10.1111/j.2044-8287.2011.02033.x

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Phillips, L. A., Leventhal, H., & Leventhal, E. A. (2013). Assessing theoretical predictors. Psychology & Health, 28, 1135–1151. https://doi.org/10.1080/08870446.2013.793798 Polonsky, W. H., Fisher, L., Earles, J., Dudl, R. J., Lees, J., Mullan, J., & Jackson, R. A. (2005). Assessing psychosocial distress in diabetes development of the diabetes distress scale.

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Psycho-Oncology, 26, 724–737. https://doi.org/10.1002/pon.4213 Rimes, K. A., & Chalder, T. (2010). The Beliefs about Emotions Scale: Validity, reliability and sensitivity to change. Journal of Psychosomatic Research, 68, 285–292. https://doi.org/ 10.1016/j.jpsychores.2009.09.014 Roberts, S. E., Stern, M., Schreuder, F. M., & Watson, T. (2009).

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The Lancet. Diabetes & Endocrinology, 3, 450–460. https://doi. org/10.1016/S2213-8587(15)00135-7 Steptoe, A., & Ayers, S. (2004). Stress, health and illness. In S. Sutton, A. Baum, & M. Johnston (Eds.), The Sage handbook of health psychology (pp. 169–196). London, UK: Sage.

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Geneva, Switzerland: WHO Received June 12, 2017 Revision received October 27, 2017 Accepted February 13, 2018 Published online February 11, 2019 Rona Moss-Morris Health Psychology Section Institute of Psychiatry, Psychology, and Neuroscience King’s College London 5th Floor Bermondsey Wing Guy’s Campus, London Bridge London, SE19RT UK rona.moss-morris@kcl.ac.uk

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J. L. Hudson & R. Moss-Morris, Illness Distress in Long-Term Conditions

Joanna L. Hudson is postdoctoral researcher at Health Psychology Section at the Institute of Psychiatry, Psychology, and Neuroscience, King’s College, London. After completing her Masters in Health Psychology, she worked as a psychological well-being practitioner as part of the Improving Access to Psychological Therapies (IAPT) initiative. Within this role, Joanna Hudson became interested in how to adapt cognitive-behavioral therapy for people with long-term conditions. After completing her PhD, Joanna moved to work with Professor Moss-Morris to develop theoretically informed interventions for the management of distress in long-term conditions.

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Rona Moss-Morris is Professor of Psychology as Applied to Medicine and is Head of the Health Psychology Section at the Institute of Psychiatry, Psychology, and Neuroscience, King’s College London. She has been researching psychological factors that affect symptom experience and adjusting to chronic conditions for the past 20 years. This research has been used to design cognitive-behavioral interventions, including Web-based interventions, for a range of patient groups. Randomized controlled trials to test the clinical and cost effectiveness of these interventions form a key component of her research.

European Psychologist (2019), 24(1), 26–37

Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Integrating Behavioral Science With Mobile (mHealth) Technology to Optimize Health Behavior Change Interventions Jane C. Walsh1 and Jenny M. Groarke2 1

School of Psychology, National University of Ireland, Galway, Ireland

2

School of Psychology, Queen’s University Belfast, UK

Abstract: Recent rapid advances in technology have provided us with a golden opportunity to effect change in health-related outcomes for chronic disease by employing digital technologies to encourage and support behavior change to promote and maintain health. Behavior change theories are the bedrock to developing evidence-based mHealth interventions. Digital technologies enable researchers to empirically test behavioral theories in “real-world” contexts using behavior change techniques (Hekler, Michie, et al., 2016). According to the European Commission (2014) among the world’s population of 7 billion, there are over 5 billion mobile devices and over 90% of the users have their mobile device near them 24 hr a day. This provides a huge opportunity for behavior change and one that health psychologists have already begun to address. However, while a novel and exciting area of research, many early studies have been criticized for lacking a strong evidence base in both design and implementation. The European Commission conducted a public consultation in 2016 on the issues surrounding the use of mHealth tools (e.g., apps) and found a lack of global standards was a significant barrier. Recently, the World Health Organization (WHO) mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist for specifying the content of mHealth interventions. Health psychologists play a key role in developing mHealth interventions, particularly in the management of chronic disease. This article discusses current challenges facing widespread integration of mobile technology into selfmanagement of chronic disease including issues around security and regulation, as well as investigating mechanisms to overcoming these barriers.

Keywords: mHealth, health behavior change, digital health, behavior change techniques

This narrative review examines the current challenges in health care and the role of behavioral science in addressing these. The potential for new mobile technologies to facilitate health behavior change will be examined within the context of the emerging evidence base for mobile health (mHealth) interventions. The potential for mHealth to increase our understanding of behavior change will be explored within the context of the World Health Organization’s (WHO) recommendations for conducting high-quality research on the efficacy of mHealth interventions, and within the European Commission’s guidelines for increasing the user-acceptability and uptake of mHealth apps.

Finally, future directions for the development of technologies and interventions that incorporate the principles of behavioral science will be explored.

European Psychologist (2019), 24(1), 38–48 https://doi.org/10.1027/1016-9040/a000351

Modern Challenges for Health and Well-Being: Self-Management of Healthy Lifestyle Behavior During the 20th century, the leading causes of death have changed from infectious diseases to those that relate to unhealthy behavior and lifestyle. The WHO estimates that around 63% of deaths globally are a result of lifestylerelated diseases and further estimates that by 2020, tobacco will account for 10% of all deaths worldwide (Alwan, 2011). Physical inactivity increases all-cause mortality risk by 20–30%, excessive alcohol use accounts for about 3.8% of deaths worldwide, and an unhealthy diet is linked to heart disease, stroke, diabetes, and cancer. Many

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of the leading causes of death in Europe (i.e., ischemic heart disease, cerebrovascular disease, cancer, and chronic respiratory disease) are all strongly related to behavior (Office for National Statistics, London, 2010). In 2016, the American Heart Association report stated that approximately 80% of cardiovascular diseases (CVDs) can be prevented through not smoking, eating a healthy diet, engaging in physical activity (PA), maintaining a healthy weight, and controlling high blood pressure, type 2 diabetes mellitus, and elevated lipid levels (Mozaffarian et al., 2016).

A 10% weight reduction in men aged 35–55 through dietary modifications and exercise would produce an estimated 20% decrease in coronary artery disease; it would also lower the degree of degenerative arthritis, gastrointestinal cancer, diabetes, stroke, and heart attack.

Considering the significant role of lifestyle behaviors in the development of chronic health conditions, the promotion of good health should move from a biomedical model focused on the physical and biological roots of illness, toward a focus on the management of behaviors that support or diminish health. Healthcare costs have been rapidly increasing in part because the diseases that are currently most prevalent are chronic in nature. Chronic diseases require continual treatment and monitoring and are thus more costly. Successful modification of health behaviors may help to reduce both the numbers of deaths and the incidence of preventable disease, as well as make a dent in the more than billions of Euros spent yearly on health and illness internationally (Mozaffarian et al., 2016). Therefore, encouraging people to adopt healthier lifestyles, and supporting those who wish to do so, is a highly desirable goal.

Health Behavior Change In view of the evidence of the link between behavior and health, it is clear that the implementation of evidence-based practice and public health depends on behavior change.

Changing unhealthy lifestyle behaviors, namely poor nutrition and lack of physical activity, are the cornerstone to preventing chronic disease (CDC; Tuso, 2014). Thus, behavior change interventions are fundamental to the effective practice of public health and clinical medicine. “Behavior change interventions” can be defined as coordinated sets of activities designed to change specified behavior patterns (Michie, van Stralen, & West, 2011). Interventions can be used to increase both uptake and optimal use of effective clinical services (e.g., vaccination, screening) and to promote healthy lifestyles (e.g., increase physical activity, quit smoking).

Behavior change theories can be used to predict outcomes and aid the development of interventions that target healthy and unhealthy behaviors. There are many longstanding, influential theories, including the Theory of Ó 2019 Hogrefe Publishing

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Planned Behavior (Ajzen, 1985), Goal-Setting Theory (Locke & Latham, 1990), the Health Belief Model (Janz & Becker, 1984), and Bandura’s (1986) Self-Efficacy Theory. For instance, a review of studies on smoking cessation, weight management, physical activity, and alcohol abuse found that interventions can increase self-efficacy, and this increase is related to subsequent health behavior change (Strecher, McEvoy DeVellis, Becker, & Rosenstock, 1986).

In addition, greater use of theory was associated with increased effect sizes in behavior change interventions and that interventions based on the Theory of Planned Behavior had particularly strong effects on health behavior change (Webb, Joseph, Yardley, & Michie, 2010). However, critiques have also highlighted the limitations of this theory (Sniehotta, Presseau, & Araújo-Soares, 2014).

In more recent years, the field has moved from adopting a single theoretical approach from an array of theories and models developed in the field of social psychology. Recent developments in health psychology have led to systematic guidance and lists of behavioral techniques which provide a mechanism to develop theory-based behavior change interventions, detail the mechanisms through which change is expected to occur and describe intervention content using shared terminology. Michie et al. (2011) Behavior Change Wheel (BCW) was developed from 19 frameworks of behavior change, synthesizing the common features of the framework and linking them to a model of behavior that was sufficiently broad that it could be applied to any behavior in any setting (Michie, Atkins, & West, 2014). The BCW provides a useful way of linking a model of behavior to common functions of interventions to change that behavior (e.g., education, persuasion, coercion, incentivization), and in turn, linking these intervention functions to policy categories (e.g., service provision, guidelines) that facilitate behavior change. The main goal of this BCW is to support intervention developers in adding theory-based behavior change elements to their interventions, to increase the chances of the intervention successfully changing behavior.

Multiple studies showed that the BCW provides an excellent framework to guide in the development of health behavior change interventions.

Michie and colleagues continued to advance our understanding of health behavior change by developing a taxonomy of “behavior change techniques” (BCTs; Michie et al., 2013). A BCT is defined as “the smallest active component of an intervention designed to change behavior,” and are both observable and replicable components of behavior change interventions. The Behavior Change Technique Taxonomy Version 1 (BCTTv1) contains 93 BCTs. Several methods have been successfully used to identify effective BCTs within complex interventions. One such method is meta-regression, a statistical technique to analyze evidence across studies (Michie et al., 2014). Interventions should European Psychologist (2019), 24(1), 38–48

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therefore be developed in a systematic and rigorous way, using the BCW to select one or more “intervention functions” and then from among 93 BCTs that can deliver these functions (Michie et al., 2011; Michie & West, 2016). Relevant BCTs should be selected based on a review of previous research and serve as the “active ingredients” of a successful behavior change (both traditional and digital) intervention. For example, “goal-setting” and “self-monitoring” have been shown to be effective strategies in increasing physical activity in a digital intervention using apps with university students (Walsh, Corbett, Hogan, Duggan, & McNamara, 2016). In contrast, the most effective BCTs targeting diet and physical activity in type 2 diabetes were found to be “action-planning,” “behavioral practice,” “instruction of behavior,” and “demonstration of behavior” (Cradock et al., 2017). A recent review found that the use of relevant BCTs significantly increased the success of weight loss programs (Hartmann-Boyce, Johns, Jebb, & Aveyard, 2014). Further, a systematic review of 85 online interventions for health behavior change found that interventions with a greater number of BCTs had larger effects than interventions with fewer BCTs (Webb et al., 2010). It has been proposed that the BCT taxonomy will be updated and refined as time goes on.

Despite these promising findings, the development of effective interventions is hampered by the absence of a detailed specification and reporting on their content, including the used BCTs. This limits the possibility of replicating effective interventions, synthesizing evidence, and understanding the causal mechanisms underlying behavior change. In order to successfully understand and change health behavior via technology, we must first accurately describe and analyze behavior and its antecedents. Theories of health behavior change coupled with evidence from existing intervention studies provide a base for considering key components for a health behavior change intervention by identifying the core components (active ingredients) of interventions: the behavior change techniques. Progress in developing effective interventions requires an understanding of how interventions work, that is, the mechanisms/ techniques by which interventions cause behavior change.

This requires clear links between defined intervention techniques and theoretical mechanisms of change. There is increasing recognition that the design of behavior change interventions should be based on relevant theories (Michie et al., 2011). Using theory to identify constructs (key concepts in the theory) that are causally related to behavior increase the likelihood of stronger intervention effects, as well as allowing for greater replication of interventions.

There are a number of stages to developing behavior change interventions, specifically planning and design, early development, acceptability and feasibility testing, European Psychologist (2019), 24(1), 38–48

and evaluation. Identifying the active components of interventions using the BCT taxonomy has become an increasingly common way of developing the theoretical content of interventions designed to change behavior at the planning and design phase of intervention development. The early development phase can also benefit from selecting appropriate BCTs and determining effective modes of delivery (e.g., face-to-face, text message, app, virtual reality).

Acceptability and feasibility testing with stakeholders and users can identify if the selection and delivery of BCTs is suitable for the population and desired outcome. Finally, if fractional factorial design (Box & Hunter, 1961) studies are employed, they can also evaluate which BCTs are effective for the target group, or standard RCTs can determine the efficacy of intervention for behavior change.

Mobile Technology and Health (mHealth) There has been rapid growth in the number of electronic health (eHealth) and mobile health (mHealth) interventions in recent years. Mobile applications (apps), text messages, wearables and sensors, interactive websites, and social media can improve health by supporting behaviors involved in disease prevention and self-management (e.g., physical activity, medication adherence) and delivery of evidencebased health care. Mobile phone use has almost achieved complete penetration with 96% of the global adult population having a mobile phone subscription (Sanou, 2015).

According to the European Commission (2014) among the world’s population of 7 billion, there are over 5 billion mobile devices and over 90% of the users have their mobile device near them 24 hr a day. Internet access is rapidly growing with approximately 400 million Internet users globally in 2000, rising to 3.2 billion by 2015. mHealth technologies have the potential to improve access to and use of health services, particularly among high-need and high-cost populations that have not been effectively engaged with in public health research and practice to date (Singh et al., 2016). Further to this is the collection of psychological, social, and contextual variables that are passively recorded or tracked (e.g., GPS location, social media activity) and can be used to understand processes and outcomes of behavioral health interventions and for empirically testing behavioral theories (Hekler et al., 2013).

There are an estimated 97,000 mHealth apps on the market. More than two-thirds of the apps cater for the consumer fitness and wellness sector, and one-third are targeting health professionals and aiming to increase the efficiency of healthcare systems (Research2Guidance, 2013). At the same time, there are a number of barriers to effective self-management of chronic conditions using mobile technologies. For one, access to reliable Internet Ó 2019 Hogrefe Publishing

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and quality devices is not distributed equally within and between nation-states. Another outstanding issue with mHealth apps and interventions is how best to sustain user engagement and overcome the declining rate of usage observed over time (Kohl, Crutzen, & de Vries, 2013; Tatara, Årsand, Skrøvseth, & Hartvigsen, 2013). mHealth and Interventions for Health Behavior Change The advent of mobile technologies (e.g., Fitbit and smart glasses) provides a unique opportunity to track health-related behaviors and outcomes (e.g., step count, blood pressure, location) enabling researchers to collect objective data that were previously based on self-report. Even better, this timeand place-specific data opens up the potential for researchers to implement context and “just-in-time” appropriate userfriendly interventions for providing behavioral support at key times when a person has the opportunity to change and is receptive to such support (e.g., Moller et al., 2017; Naughton et al., 2016). Recent evidence suggests that when interventions are used in the real-world context where behaviors occur, they have greater impact (e.g., Naughton et al., 2016). Mobile technology can provide detailed, unobtrusive assessment of behavior and its context, while complementary qualitative methods are crucial to fully understand and interpret user experiences (Michie et al., 2017).

The advancement of technology offers simple, convenient approaches to facilitate self-management of health, for example, by enabling easy monitoring of diet and physical activity (Thomas & Bond, 2014). Two recent randomized controlled trials found that a simple smartphone pedometer significantly increased physical activity (Glynn et al., 2014; Walsh et al., 2016). In the study by Glynn et al. (2014), 90 participants recruited from a primary care setting received information about the benefits of exercise.

After 8 weeks, daily step count had significantly increased for all participants. However, participants in the intervention condition used a pedometer app and had a significantly greater increase in steps per day relative to the control group receiving information only. Wearable activity monitors (e.g., Fitbit, Jawbone) provide a technological advance on pedometer apps allowing ongoing assessment of goal achievement. In a study with overweight/obese adults, these wearable activity monitors were associated with increased step count at 6-week follow-up. Interestingly, participants’ level of engagement with the mobile app accompanying the wearable device (operationalized as the number of user logins) was associated with increased steps (Wang et al., 2016).

Pedometer apps and activity monitors facilitate the BCTs of “goal-setting,” “self-monitoring,” and “action-planning” through visual feedback on a user’s step count and activity level. Two studies using pedometer apps found that physiÓ 2019 Hogrefe Publishing

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cal activity increases were greater in experimental conditions where pedometer use was combined with the BCT of “goal-setting” in relation to increasing daily step count (Compernolle, Cardon, De Bourdeaudhuij, De Cocker, & Vandelanotte, 2015; Mansi et al., 2015). A systematic review and meta-analysis on the efficacy of activity monitors to increase physical activity in an obese population finding that physical activity gains were significantly higher when the BCTs “goal-setting” and “feedback” were incorporated into the design of mHealth behavior change interventions (de Vries, Kooiman, van Ittersum, van Brussel, & de Groot, 2016). A review of the BCTs present in 167 highly ranked mobile apps for physical activity found that the most comment BCTs were “instruction on how to perform behavior,” “modeling behavior,” “feedback,” and “goal-setting” (Conroy, Yang, & Maher, 2014). However, the number of BCTs present in each app was limited (typically less than four).

The authors caution that users seeking to increase physical activity may therefore need to use more than one app to effectively change their behavior.

The use of mHealth technologies may also encourage adherence to a treatment plan (Doughty, 2011). There is evidence emerging from a systematic review of 13 studies that electronic reminder devices and short message service (SMS) reminders increase patients’ adherence to chronic medication, at least in the short-term (up to 6 month follow-up; Vervloet et al., 2012). A larger review of 107 articles was less conclusive reporting that mHealth solutions aimed at adherence have the potential to improve chronic disease management, but that the evidence for its efficacy is mixed.

However, more than half (56%) of 27 RCTs reviewed did find significant improvement in adherence behavior in patients with chronic diseases, with the majority of interventions using SMS reminders (Hamine, Gerth-Guyette, Faulx, Green, & Ginsburg, 2015). A review of 20 mHealth interventions for increasing adherence to medication reported that despite significant heterogeneity in the design and quality of interventions, 65% of studies reported positive effects on adherence (Anglada-Martinez et al., 2014).

The results of a good number of existing reviews in different populations have demonstrated the potential for mHealth to increase adherence, yet the evidence is currently inconclusive. Nonadherence behavior is complex, and therefore complex interventions are required for it to change. Qualitative comparative analysis of a systematic review of 60 complex interventions was used to identify combinations of BCTs that were most effective for improving medication adherence in outpatients with chronic conditions. Improvement in adherence was reported in more than half of the studies (57%). Of these studies, there were seven different configurations of BCTs that increased adherence.

However, the most common and efficacious combination of techniques was “increasing knowledge” coupled with European Psychologist (2019), 24(1), 38–48

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“increasing self-efficacy” (Kahwati et al., 2016). There is a wide range of apps available for increasing medication adherence. A content analysis of the BCTs presents in 166 such apps reported that 12 of a possible 96 BCTs were present across these apps and that 96% of the apps included the BCTs of “action-planning,” and “prompting/cues.”

More than one-third of the apps that were reviewed featured the BCTs “self-monitoring” and “feedback on behavior” (Morrissey, Corbett, Walsh, & Molloy, 2016). It is noteworthy that none of the available apps included the BCTs that were found to be most effective for increasing adherence in the qualitative synthesis by Kahwati et al. (i.e., “increasing knowledge” or “increasing self-efficacy”), indicating there is opportunity for greater integration of behavioral science theory and research in the design of apps, leading to improved quality and efficacy of mHealth interventions for improving adherence behavior.

The current mHealth evidence is disseminated in multiple forms including peer-reviewed literature, white papers, reports, presentations, and blogs. The evidence base is heterogeneous in quality, completeness, and objectivity of the reporting of mHealth interventions – thus making comparisons across interventions difficult. Despite the emergence of hundreds of mHealth studies and initiatives, there remains a lack of rigorous, high-quality evidence on the efficacy and effectiveness of such interventions (Agarwal, Perry, Long, & Labrique, 2015). To date, few developers of digital health interventions have specified how characteristics of their intervention map onto underlying evidence-based theories and techniques (Morrissey et al., 2016), and unsurprisingly, the current evidence around the effectiveness of technological devices as a health behavior change tool is limited. Some researchers have had success in the field. As described in the previous section, Glynn et al. (2014) had success in increasing physical activity in a primary care population using a pedometer app. However, this study along with others requires clearer specification of the behavior change techniques (BCTs) employed along with the underlying theory-based mechanisms of behavior change. There are many studies and systematic reviews signaling the promise of mHealth interventions for health behavior change. However, the reporting of complex behavioral health interventions, digital and traditional, often lacks sufficient details to know exactly what scientific basis was used to develop the intervention and how the intervention was offered to participants.

The Contribution of mHealth to Research and Theory on Health Behavior Change Thus far, we have focused on the benefit of incorporating the principles of behavioral science into the design of European Psychologist (2019), 24(1), 38–48

mHealth interventions. Yet, it is also true that the advent of mHealth can benefit behavioral science research and theory. Traditionally, assessments of health behavior have relied heavily on self-report (e.g., self-reported smoking, food diaries, medication adherence), which are beset with problems of poor recall, inaccuracies, and socially desirable responding. In contrast, mobile technology provides opportunity for objective measurement of both health behavior and health outcomes. For example, new wearable sensor technologies (e.g., bracelets, smart glasses) automatically provide minute-by-minute monitoring of objective measurements of behavior (e.g., physical activity). This provides researchers with an excellent opportunity to measure health-related outcomes that were previously reliant on self-report or were confined to assessments in a clinical or laboratory environment (e.g., stress, mood, heart rate).

At the same time, the self-report measures coupled with objective clinical measures of health (e.g., BMI, cholesterol, blood pressure) employed up to this point have helped to profile the links between health behaviors and health outcomes. Typically, self-report data is collected at a few distinct points in the trajectory of an illness (i.e., diagnosis, treatment, survivorship) or protocol of a research study (i.e., baseline, 6 and 12 months later), and while providing a useful snapshot of cross-sectional data, they often failed to grasp the personal, contextual, and social factors that influence self-management of health.

Further to this, the collection of psychological, social, and contextual variables that are passively recorded or tracked along with objective health data (e.g., GPS location, social media activity) can be used to understand processes and outcomes of behavioral health interventions and for empirically testing behavioral theories in different contexts and settings (Hekler et al., 2013).

One of the problems with eHealth and mHealth technology is that the speed of growth has not allowed enough time for theory development and research on its potential for health behavior change. The growth in the number of eHealth (e.g., online interventions) as well as mHealth interventions in the last few years, and recent advances in technology have provided researchers with a golden opportunity for empirically testing behavioral theories in “real-world” contexts within health service delivery models. Digital platforms allow for greater specification of existing behavioral theories and models. Methodologies derived from mHealth could help define how constructs relate to one another over time, and the predicted magnitude and direction of those relations (Hekler, Michie, et al., 2016). For example, mHealth research could determine the temporal and dynamic relationships between effective components of an intervention, such as BCTs (e.g., “goal-setting” and “feedback”), psychological constructs (e.g., self-efficacy), and behavioral outcomes (e.g., physical activity). These potential Ó 2019 Hogrefe Publishing

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interdependencies between intervention functions and outcomes are currently not well understood.

As previously described, BCTs are associated with increased behavior change (Kahwati et al., 2016; de Vries et al., 2016; Webb et al., 2010), and perceived self-efficacy and goals also positively influence behavior change (Bandura & Locke, 2003). Wearable sensors provide “feedback” to individuals on their level of physical activity, enabling “self-monitoring” of behavior and progress toward physical activity goals. Analysis of objective behavioral data (i.e., PA) collected in real time from wearable devices can be combined with measures of an individual’s sense of self-efficacy. Empirical tests can then determine if self-efficacy varies dynamically in connection with achievement of physical activity goals, and what impact this has on behavior (i.e., level of PA). Failure to reach PA goals will be associated with less PA, but does “self-monitoring” of failures in “goal-setting” also reduce an individual’s self-efficacy, which may, in turn, negatively impact health behavior change over time?

The shift from traditional to digital platforms presents researchers with an excellent opportunity to both develop and test theories based on behavior change techniques (Hekler, Michie, et al., 2016). While this is a novel and exciting area of enquiry, there are few studies in this area, and many of the early studies have been heavily criticized for lacking a strong evidence base in terms of both design and implementation.

The European Commission carried out a public consultation in 2016 to investigate the issues surrounding the use of mobile technology in the promotion of health and well-being. Contributions came from both individuals and organizations (e.g., pharmaceutical industry, telecommunications companies, healthcare providers, patients’ associations). The green paper on mobile health reported that a lack of global standards and regulation were a significant barrier to the effective use of mHealth technologies.

Primary concerns included issues around privacy and data security. Contributors described the need for standardization in data management practices, for example, the use of encryption and user authentication, noting that increased transparency would increase a user’s trust in applications.

Concerns regarding patient safety were also raised. There is currently no quality certification or regulation of mHealth apps, and this introduces the possibility that misinformation provided by an app might result in poor self-management decisions (e.g., taking medication incorrectly). Following this enquiry, the EU published a Code of Conduct for the development of new mHealth applications. The code provides practical guidelines for developers around issues of consent, privacy, data storage, and security. The aim is to guide key stakeholders in the safe use of these new health apps and to increase trust among the users of mHealth apps Ó 2019 Hogrefe Publishing

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that process personal and health data (European Commission, 2016). In addition, an expert panel was assembled to develop guidelines on procedures to develop safe and effective mHealth solutions (Ruck, Bondorf, & Lowe, 2016). The guidelines state that health apps should be user-friendly, desirable, credible, effective, reliable, secure, and safe for the target groups – including a formal risk assessment.

WHO Guidelines for Reporting mHealth Research In an attempt to move toward the establishment of global standards for mHealth research, the WHO mHealth Technical Evidence Review Group developed the mHealth evidence reporting and assessment (mERA) checklist (Agarwal et al., 2016). This checklist was designed to improve the quality of future research, to facilitate screening of emerging evidence, and identification of critical evidence gaps. These guidelines were developed with a view to improving reporting of the evidence base to facilitate policy makers in making decisions around mHealth intervention selection. The guiding principle for the development of these criteria was to identify a minimum set of information needed to define (1) what the mHealth intervention is (content), (2) user-centered design (technical and user features), and (3) where it is being implemented (context), to support replication of the intervention.

Content of mHealth Interventions: Theory-Based Design The WHO recommend that the original source of any educational content is reported, along with the theoretical basis for new or adapted content.

It is clear that the potential public health impact of new mHealth technologies can only be realized to the extent that digital health interventions are effective. The process of developing an effective mHealth intervention will benefit from applying evidence-based theories and techniques, as it will inform design characteristics (e.g., behavior change techniques) and indicate optimum conditions under which interventions and their specific characteristics will be most effective (Peters, de Bruin, & Crutzen, 2015).

This is important for digital health interventions given that they often require considerable initial investments in development (e.g., app development is timely and costly).

Morrison (2015) suggests that to achieve long-term sustainability of digital interventions more research on the effective elements of interventions instead of effective mHealth interventions is required. She argues that the reach and use of these interventions need more scientific input to increase the public health impact of Internetdelivered interventions.

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Also, highlighting the need to adopt theory-based design principles is a recent review paper that makes 16 evidencebased recommendations for the development of mental health apps (MHapps; Bakker, Kazantzis, Rickwood, & Rickard, 2016). Seven of the recommendations advocate improving MHapp design features through application of research and theory on mental health, in particular, cognitive behavioral therapy. Many recommendations are consistent with the effective BCTs for health behavior change described in this paper. For example, psychoeducation (i.e., providing “information” about mental health) is recommended, as well as reporting of thoughts, feelings, and behavior (i.e., “self-monitoring”). The application of theory provides a useful starting point to design technological interventions; the current paper recommends behavioral science theory due to its compelling evidence base. As stated, interventions including more BCTs have stronger effects on health behavior change (Webb et al., 2010).

Yet, studies have found that commercially available apps do not contain a large amount of BCTs (Conroy, Yang, & Maher 2014; Morrissey et al., 2016).

Stakeholder Involvement in User Design The WHO also highlights the importance of involving end users in the initial design phase to properly inform elements critical to success such as individuals’ and community characteristics as well as to determine effective modes of intervention delivery.

There has been increased recognition and emphasis placed on the importance of involving users and stakeholders from the outset in intervention design and implementation. One method of approaching this is to employ the “person-based approach” (Yardley, Morrison, Bradbury, & Muller, 2015). This approach involves in-depth qualitative research conducted with the users before the digital intervention is developed. This data is used to develop “guiding principles” that state the key objectives of the intervention and describe the key features of the intervention required to achieve each objective. They posit that qualitative research is crucial at all stages of intervention development and evaluation, including planning and design, early development, acceptability and feasibility testing, and evaluation in clinical trials and real-life settings. The person-based approach is highly compatible with the more in-depth approaches that have evolved within the disciplines of information systems and human–computer interaction, such as human-centered and user-centered design. These approaches seek to understand the user’s knowledge, skills, behavior, motivations, cultural background, and organizational context, and they involve users iteratively throughout development. Traditionally, user-testing has focused on utility and engagement, aiming to increase user’s enjoyment of and motivation to use technology (Kim, Kim, & European Psychologist (2019), 24(1), 38–48

Wachter, 2013; O’Brien, 2010). The person-based approach, however, is rooted within the discipline of health psychology and focuses primarily on the behavior change techniques the intervention is intended to deliver, and their desired implementation by the people using the intervention. Similar to user-testing methods aiming to increase the use of technology, the person-based approach aims to increase participants’ engagement with an intervention such that the intended outcomes of the behavior change intervention can be realized.

Glynn et al. (2015) conducted qualitative research to explore target users’ perspectives in the development of an app for self-management of hypertension. Patients with hypertension stated that “one size fits all” interventions to enhance self-management of lifestyle behavior are undesirable. Rather, patients prefer a personalized program via an app enabling them to prioritize their own approach to selfmanagement. Themes identified in the reports from patients in this study highlight that the source of the mHealth “prescription” was also an important factor and “trust” in the technology was highlighted as a key factor in relation to its potential effect on engagement with healthcare providers and motivation for engagement. The introduction of a new technology or platform for engagement requires concerted efforts to alleviate patient concerns and to create confidence in terms of quality and security.

Further, patients’ motivation to use mobile technology was influenced by the potential of technology to provide BCTs such as, “information,” “feedback,” “reward,” and “reinforcement” systems which could embed new selfmanagement habits. The potential for technology to facilitate a personalized flow of communication between patient and healthcare provider was recognized as important as was the ability of technology to facilitate tailored messaging and feedback for patients. The flexibility and inherent motivational ability of newer technologies seems to have the potential to improve the ability of patients to engage in sustained behavior change. However, evidence of long-term engagement is still lacking in many studies as the majority of users tend to stop using apps after just a few weeks (Kohl et al., 2013). The Mobile Application Rating Scale (Stoyanov et al., 2015) is a tool for assessing the quality of apps by researchers, professionals, and clinicians that can be used to aid the design and development of better quality mHealth apps. A user version of the scale was recently developed (Stoyanov, Hides, Kavanagh, & Wilson, 2016) offering a reliable and valid quantitative approach to stakeholder involvement in user design.

Context of Intervention Delivery The WHO recommends that researchers state what context or setting the mHealth intervention is taking place, the appropriateness of the intervention to the context, and Ó 2019 Hogrefe Publishing

J. C. Walsh & J. M. Groarke, Integrating Behavioral Science With mHealth

any potential for adaptation to other contexts. The mHealth intervention may be designed for a specific context, setting, or group of users. However, one advantage of mobile interventions is they can be designed in such a way that they can be delivered in any context, when needed, that is, “just in time.” The capacity of technology to collect time and place specific-data opens up the potential for researchers to implement user-friendly interventions that are context and time appropriate. For example, interventions that provide behavioral support (e.g., “information” about health consequences of behavior) at key times when a person is receptive to such support, or at key places where the person has the opportunity to change their behavior (e.g., Moller et al., 2017; Naughton et al., 2016). The use of mobile apps also enables the specification of dynamic temporal relationships, for example, timescale, latency, and delay (Naughton et al., 2016; Spruijt-Metz et al., 2015). From a theoretical perspective, work on prospective memory suggests that people make effective use of cues that are appropriate for goal attainment and that people readily execute an intended action in response to a specified cue when the action cue is encountered later during ongoing activity (Brandimonte, Einstein, & McDaniel, 1996). The recent study by Naughton et al. (2016) has shown promising finding using these methods with respect to smoking cessation.

Smoking behavior is particularly prone to lapse and relapse during quit attempts as cravings are often triggered by cues from a smoker’s immediate environment. Naughton et al. developed a context-aware smoking cessation app, Q Sense, which uses a smoking episode-reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time as needed. The development of this app was based on explanatory sequential mixed-methods design where data collected by the app informed semi-structured interviews.

Although underreporting of smoking occurred, the findings suggested that geofence-triggered support was regarded positively by participants. These new technological developments pave the way for “big data” to drive algorithmic solutions to provide optimum contextual cues for intervention delivery (Hekler, Klasnja, et al. 2016). This combined with a theory-informed and person-based content design creates the possibility for a timely, context-appropriate, personalized, and highly effective intervention to be delivered to participants.

Through widespread adoption, it is hoped that the use of these guidelines will standardize the quality of mHealth evidence reporting and indirectly improve the quality of mHealth evidence. In order to develop evidence-based, effective, and user-friendly mHealth interventions, researchers must move toward a more collaborative and multidisciplinary approach using these European standards as a framework. Ó 2019 Hogrefe Publishing

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Future Directions: Moving Toward Greater Personalization The use of new digital technologies allows for increased personalization of mobile health interventions based on an individual’s unique profile, thus increasing the likelihood of successful health behavior change. One reason to develop personalized interventions is that patients have identified personalized solutions as more desirable than generic apps for self-management (Glynn et al., 2015). As such, personalization may enhance the success of health behavior change interventions or may encourage greater engagement with mHealth. In a mental health context, tailored computerized cognitive behavioral therapy interventions have been found to be more effective than standardized interventions (Johansson et al., 2012; Nordgren et al., 2014; Silfvernagel et al., 2012).

Personalized solutions, however, pose another challenge for researchers to develop and monitor interventions that may change over time by adapting to the user’s changing requirements and altered goals. For example, a goal of “couch to 5k” may be the focus over an initial 6 or 8 week period, but a new goal or shift in focus and emphasis may be required to sustain levels of physical activity once this goal has been achieved, or indeed, to support continued goal-pursuit in the face of failure to achieve physical activity targets. This is particularly important in developing health behavior change interventions that foster maintenance and long-term sustainability of the desired health behavior (Almirall, Nahum-Shani, Sherwood, & Murphy, 2014).

These high-level adaptive changes are more achievable using new technology-based interventions by using live algorithmic analysis based on data collected by an app, both sensor-based and user-inputted. This type of analysis is complex and therefore requires a strong multidisciplinary approach with information technologists, medics, and health psychologists working closely together to make sense of and capitalize on the quantity and quality of data generated by new health technology.

Conclusion Rapid changes in technology coupled with recent developments in behavioral science provide an excellent opportunity to deliver personalized evidence-driven behavior change interventions. This paper has reviewed current evidence on the impact of mHealth for improving health behavior, focusing on the opportunity for behavioral science to improve mHealth interventions. The European Commission and the WHO guidelines serve to facilitate the development, implementation, and evaluation of effective European Psychologist (2019), 24(1), 38–48

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mHealth interventions. Specifically, the European Commission has called for a more theory-based approach to the development of mHealth apps and interventions. Behavioral science has a key role to play in developing effective interventions as the use of BCTs has been associated with increased success in achieving behavior change (de Vries et al., 2016; Kahwati et al., 2016; Webb et al., 2010). The European Commission has also recommended increased usability and utility of health apps. This can also be achieved by early stakeholder involvement, for example, using the person-based approach (Yardley et al., 2015).

Consideration must also be given to equal access to reliable Internet and quality of devices within and between different countries. In light of this, a strong multidisciplinary approach is required with input from information technologists, medics, and health psychologists working closely together to make sense of the data in order to develop optimum solutions for health behavior change.

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History Received April 21, 2017 Revision received February 20, 2018 Accepted April 19, 2018 Published online February 11, 2019 Jane C. Walsh School of Psychology National University of Ireland Galway Ireland jane.walsh@nuigalway.ie

Jane C. Walsh is the Director of the mHealth Research Group at the National University of Ireland and the CoLeader of the Health and Well-being Cluster Whitaker Institute. The bedrock of her research is underpinned by the theme “Health Behaviors for Healthy Ageing,” and she has recently secured EU fundings to conduct research on personalized technological solutions for healthy ageing. She is an active member of the H2020 European Network for the Joint Evaluation of Connected Health Technologies (ENJECT) and is a recognized expert in mobile technology and health behavior change.

Jenny M. Groarke is lecturer in health psychology at Queen’s University Belfast. Formerly Postdoctoral Researcher with the mHealth Research Group at the National University of Ireland, where she is leading research on the Irish Cancer Society funded project “Enhancing the health and well-being of overweight cancer survivors through tailored personalized behavior change interventions using mobile technology.”

She also worked as a researcher on a Challenging Breast Cancer Together study examining the efficacy of a Cognitive-Behavioral Stress Management intervention with women with breast cancer at NUI, Galway and University Hospital Galway.

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Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Best Practices and Recommendations for Digital Interventions to Improve Engagement and Adherence in Chronic Illness Sufferers Maria Karekla,1,2,3 Orestis Kasinopoulos,1 David Dias Neto,2,4 David Daniel Ebert,3,5 Tom Van Daele,3,6 Tine Nordgreen,3,7 Stefan Höfer,2,8 Svein Oeverland,3,9 and Kit Lisbeth Jensen3,10 1

Department of Psychology, University of Cyprus, Nikosia, Cyprus

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Psychology and Health Standing Committee of the European Federation of Psychology Associations, Brussels, Belgium

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e-Health Task Force of the European Federation of Psychology Associations, Brussels, Belgium APPsyCI – Applied Psychology Research Center Capabilities & Inclusion, ISPA – Instituto Universitário, Lisbon, Portugal

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Friedrich Alexander University, Erlangen-Nürnberg, Germany Thomas More University of Applied Sciences, Belgium

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Division of Psychiatry, Haukeland University Hospital, Bergen, Norway

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Medical University Innsbruck, Austria

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SuperEgo AS, Norway

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Clinical Psychology, Private Practice, Denmark

Abstract: Chronic illnesses cause considerable burden in quality of life, often leading to physical, psychological, and social dysfunctioning of the sufferers and their family. There is a growing need for flexible provision of home-based psychological services to increase reach even for traditionally underserved chronic illness sufferer populations. Digital interventions can fulfill this role and provide a range of psychological services to improve functioning. Despite the potential of digital interventions, concerns remain regarding users’ engagement, as low engagement is associated with low adherence rates, high attrition, and suboptimal exposure to the intervention. Human–computer interaction (e.g., theoretical models of persuasive system design, gamification, tailoring, and supportive accountability) and user characteristics (e.g., gender, age, computer literacy) are the main identified culprits contributing to engagement and adherence difficulties. To date, there have not been any clear and concise recommendations for improved utilization and engagement in digital interventions. This paper provides an overview of user engagement factors and proposes research informed recommendations for engagement and adherence planning in digital intervention development. The recommendations were derived from the literature and consensualized by expert members of the European Federation of Psychology Associations, Psychology and Health Standing Committee, and e-Health Task Force. These recommendations serve as a starting point for researchers and clinicians interested in the digitalized health field and promote effective planning for engagement when developing digital interventions with the potential to maximize adherence and optimal exposure in the treatment of chronic health conditions.

Keywords: digital interventions, e-health, recommendations, adherence, engagement

The Need for Digital Interventions for Chronic Health Conditions Chronic health illnesses cause considerable burden to individuals, families, and society, with significant impact on physical, emotional, and social functioning (Breivik, Collett, Ventafridda, Cohen, & Gallacher, 2006; Sprangers et al., 2000). Although there is a growing body of evidence that

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pharmacological and psychosocial interventions can effectively treat illness-related interference in daily life, numerous chronic illness sufferers remain untreated or inadequately treated. This is partly a result of access, mobility, and transportation problems (Jerant, von FriederichsFitzwater, & Moore, 2005); financial barriers; reluctance to seek treatment; and paucity of clinicians trained in evidence-based multidisciplinary treatments (Breivik et al.,

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2006; Jamison, Gintner, Rogers, & Fairchild, 2002; Jensen, Nielson, Romano, Hill, & Turner, 2000; Shapiro, Cavanagh, & Lomas, 2003). In an attempt to overcome such barriers to treatment, an interest in home-based selfmanagement support has emerged (Jerant et al., 2005).

Of particular interest are digital interventions (used here as an umbrella term for e-health, m-health, Internet-based, text message, self-management interventions, etc.) aiming to improve health care for persons with chronic conditions in the convenience of their own space and time (Bender, Radhakrishnan, Diorio, Englesakis, & Jadad, 2011; Bennett & Glasgow, 2009; Keogh, 2013; Long & Palermo, 2009; McGeary, McGeary, & Gatchel, 2012).

A digital health intervention is often based on a selfguided approach, executed through the use of a platform (a Website if computer-based or application if smartphone or tablet-based) by consumers who seek help for health or mental health-related issues (Barak, Klein, & Proudfoot, 2009). Different digital intervention delivery vehicles each come with their own unique challenges, as well as opportunities. For example, a smartphone health application allows for Ecological Momentary Assessment (i.e., capturing the user’s experience in the moment and less prone to recall bias) and portability, yet it also comes with a smaller screen size, possibility of battery drain, or a sense of intrusiveness by the user (Yardley et al., 2016). For purposes of this review, a digital intervention is defined broadly as a selfguided Internet- or computer-based health intervention and will not provide an in-depth consideration of each digital platform specifically.

The purpose of digital health interventions is to create behavior change, enhance knowledge, awareness, and understanding for health difficulties, through the use of interactive Web-based components (Barak et al., 2009).

Digital health interventions present with numerous benefits over traditional face-to-face ones, including wide accessibility, cost-effectiveness, access to treatment without waitingtime, overcoming stigma-induced barriers through anonymity, and providing quality treatment to the sufferer at their own individual pace (Brouwer et al., 2010; Brug, Oenema, & Campbell, 2003; Brug, Oenema, Kroeze, & Raat, 2005; Cuijpers, Van Straten, & Andersson, 2008; De Nooijer et al., 2005; Savvides & Karekla, 2015).

There is amounting support for the efficacy of digital cognitive behavioral interventions in improving management of patients’ health conditions, reducing pain intensity and catastrophizing, and improving physical and psychosocial functioning (Eccleston et al., 2014; Keogh, Rosser, & Eccleston, 2010; Macea, Gajos, Calil, Fregni, 2010; Velleman, Stallard, & Richardson, 2010). However, enthusiasm about the effects of digital-based interventions is tempered by findings presenting users’ failure to engage with the digital media (Macea et al., 2010). Lack of European Psychologist (2019), 24(1), 49–67

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engagement is associated with low motivation and adherence to the intervention, resulting in high dropout rates affecting treatment outcomes and effectiveness (Carlbring, Westling, Ljungstrand, Ekselius, & Andersson, 2001; Christensen, Griffiths, & Korten 2002). Understanding the reasons for low engagement and adherence in digital interventions may enable their improved evaluation and possibly lead to increased effectiveness and utility. It is paramount that practical strategies and recommendations to tackle this problem need to be developed.

The Challenge of Digital Health Interventions: (1) Definition and Conceptualization of Adherence and Engagement Adherence is a critical topic in the rapidly expanding area of digital interventions. Greater adherence to digital interventions leads to healthy lifestyle changes and consequently improved health outcomes (e.g., improved quality of life, reduced mortality rates; Donkin et al., 2011; Manwaring et al., 2008). Conversely, low adherence significantly limits treatment effectiveness, increases discontinuation and dropout (Glasgow, 2007; Hilvert-Bruce, Rossouw, Wong, Sunderland, & Andrews, 2012; Leslie, Marshall, Owen, & Bauman, 2005; Trompetter, Bohlmeijer, Veehof, & Schreurs, 2015). Within the medication literature, adherence is the extent to which a person’s behavior (e.g., taking medication) corresponds with agreed upon recommendations from a healthcare provider (Sabaté, 2003). In digital interventions, adherence is often regarded as the degree to which users engage with the content of an intervention as intended by the developer. Engagement is thus another crucial variable that impacts the effectiveness of digital health interventions.

Unfortunately, there is no consensus definition of engagement and different fields (e.g., computer science, psychology, and behavioral health) conceptualize it differently.

The behavioral science perspective of engagement defines it as a behavior in terms of intervention “usage” assessed via duration of contact with an intervention and frequency of visits, thus may be interchangeable with the term “adherence” (Couper et al., 2010a; Kelders, Kok, Ossebaard, & Van Gemert-Pijnen, 2012; Voils et al., 2014; Wang et al., 2012). Given substantial differences in definitions of engagement, a synthesis of factors affecting digital interventions engagement (intra-individual to contextual to designrelated factors) is difficult, hindering the formation of recommendations to guide digital intervention development. Perski, Blandford, West, and Michie (2017) in a recent review attempted to consolidate the literature definitions of engagement. They concluded that engagement is a Ó 2019 Hogrefe Publishing

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multidimensional construct and a dynamic process expected to vary over time both between and within users and is comprised of two parts: “(1) the extent (e.g. amount, frequency, duration, depth) of usage and (2) a subjective experience characterized by attention, interest and affect.”

An advantage of this conceptual framework is that it highlights both direct (e.g., content of intervention) and indirect (e.g., beliefs about intervention) influences on engagement; thus, this definition is adopted in this paper.

A related problem facing researchers and digital intervention developers is the challenge of measuring adherence and engagement. Having a clearer consensus definition of these terms can be a first step in establishing means via which to evaluate levels of adherence and engagement, as it is yet unclear what levels of adherence or engagement are desired for interventions to have an effect. Methodologies of evaluation, assessment of reliability, and related statistical issues need to emerge. Also, metrics to assess them (e.g., time spent within an intervention or components of an intervention, logins, intensity, and frequency of engagement with the content) need to be decided.

The Challenge of Digital Health Interventions: (2) Low Adherence and High Dropouts Almost twice as many chronic illness users drop out from self-guided digital interventions compared to traditional interventions (Macea et al., 2010), whereas guided interventions (i.e., either an actual therapist through e-mail, or a digital character through animation, guides the user through the content) have shown to result in similar adherence rates as face-to-face ones, for conditions such as depression (van Ballegooijen et al., 2014). Even among users who complete a digital intervention as intended, few adhere to the intervention in the desired way envisioned by the intervention creators (Kelders, Van GemertPijnen, Werkman, Nijland, & Seydel, 2011; Wangberg, Bergmo, & Johnsen, 2008). Nonadherence suggests that users are not optimally exposed to the intervention, a dose-response relationship problem, which consequently lessens the effectiveness of digital interventions (Donkin et al., 2011; Manwaring et al., 2008).

Proposed reasons for poor adherence include: sociodemographic or user-related characteristics, such as poor digital health literacy (O’Connor et al., 2016); being male (Lorig et al., 2002); lower education level (Karyotaki et al., 2015); and severity and longer chronicity of condition (Lorig, Ritter, Laurent, & Plant, 2006). Eysenbach (2005) proposed that participants in traditional randomized controlled trials (RCTs) go through several levels of clinical filtering prior to entering treatment, an indication of Ó 2019 Hogrefe Publishing

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commitment level. In contrast, the broad, easy, and unfiltered participant enrollment accompanied by the highly accessible digital interventions, may come with the cost of lower motivation or commitment of users to the intervention. This is further supported by a recent systematic review concluding that one of four overarching themes found to affect user engagement and enrollment in digital interventions relates to the engagement and recruitment approach (O’Connor et al., 2016). For example, difficulty understanding the recruitment message or lack of advice from the researcher/developer, as well as lack of clinical endorsement, is linked to poor engagement. Another contributor to low adherence is related to the developers’ approach to technology, envisioning digital means as only mere vehicles of delivering intervention content, without recognition of the dynamic nature of technology and its potential in engaging a user.

Apart from researchers and healthcare professionals, nonacademic or nonmedical, technical developers (e.g., programmers) are involved in digital interventions. Teams of IT technical developers and business specialists (developers, designers, user experience specialists, marketing specialists) as well as researchers or healthcare professionals need to efficiently collaborate among them, for successful implementation, continued development and evaluation of the digital intervention (Michie, Yardley, West, Patrick, & Greaves, 2017). In this paper, the term “researcher” will be used to account for the person who is often in direct contact with the user whereas the term “developer” will be used for the person commissioned to develop and program an information technology (IT) product.

This paper aims to propose research informed recommendations for engagement and adherence planning in digital interventions for chronic health conditions. It does not constitute an exhaustive set of recommendations based on all relevant theory, nor does it suggest that the theories and recommendations included are superior to those that are not been discussed. The purpose of this paper was twofold: (1) to serve as a starting point for researchers and developers interested in developing or utilizing digital health interventions and (2) to propose research informed recommendations for increased engagement and adherence planning in digital interventions for chronic health conditions.

Methods The first two authors used Google Scholar and PubMed as the main search engines, and conducted an electronic search of the relevant literature dating back to 1965. To identify key papers, search terms included: “digital health interventions,” “e-health, m-health, chronic illness,” European Psychologist (2019), 24(1), 49–67

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Digital interventions should, if suitable, include human or a sense of human contact to promote user accountability

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Instructions for use should be simple and direct

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Figure 1. Diagram summarizing dimensions and recommendations for improving engagement and adherence of chronic illness sufferers in digital interventions.

“recommendations,” “best practices,” “adherence,” “user engagement,” “system design,” “dropouts,” and “psychological theory.” An emphasis was placed in identifying systematic reviews and meta-analyses, and article references were also checked to identify additional relevant papers.

All types of articles were included except articles that were not peer-reviewed or not in English. The authors independently reviewed identified papers, coding for challenges presented to user adherence and engagement and recommendations provided.

A list of generated areas of importance was created by each of the two authors, and then these were discussed and grouped into categories/dimensions to formulate the proposed recommendations. These were then presented to expert members of two European Federation of Psychology European Psychologist (2019), 24(1), 49–67

Associations’ (EFPA) committees: Psychology and Health Standing Committee and e-Health Task Force. Feedback was received from the experts of the two committees, and adjustments were made to the final consolidated list of recommendations.

Results The final consolidated list consists of 10 recommendations organized around four dimensions: (I) A-priori Theoretical Planning, (II) Human–Computer Interaction, (III) Tailoring and Targeting to User Groups, and (IV) Active Assessment of Usage (see Figure 1). The format of each Ó 2019 Hogrefe Publishing

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recommendation consists of: (1) a Rationale section and (2) a practical Recommendation section to facilitate the readers in conceptualizing the theory and the application of each recommendation. The dimensions and their associated recommendations are interdependent, and while some overlap exists among them, it is suggested that they are considered in their entirety. Challenges faced by digital interventions in engagement and adherence as well as their respective recommendations and practical examples can be found in the Electronic Supplementary Material, ESM 1.

Recommendations Dimension I: A-Priori Theoretical Planning 1. The Design of Digital Interventions Should Include A-Priori Considerations of Adherence Utilizing a Digital Theory-Driven Approach Rationale. Psychologist creators of digital interventions tend to focus exclusively on their psychological content and may merely transfer face-to-face treatment content into a digital medium (e.g., transport the written content onto a Website; van Gemert-Pijnen et al., 2011). Though this approach intuitively appears beneficial, what tends to be lacking is the consideration for the unique technological abilities of digitalization that can potentially provide new ways of interacting with chronic illness patients. Utilization of a theoretical design framework in digital intervention planning cultivates and maintains user engagement and motivation to adhere to the intervention throughout its intended duration.

Examining the literature on digital interventions suggests that most digital programs evaluated are not rooted in specific theoretical frameworks (Christensen, Griffiths, & Farrer, 2009). A systematic review of 83 digital interventions reported that only a third (33 studies) had planned a-priori to include design principles to address adherence (Kelders, Kok, Ossebaard, & Van Gemert-Pijnen, 2012).

In addition, half of the studies (N = 18) that addressed adherence stated that encouraging adherence is a task for a counselor, thereby ignoring the potential of technology.

Of the remaining 15 studies that mentioned planning to increase adherence, 8 did so without any theoretically driven design basis (i.e., digital intervention design theory).

Adapting digital interventions to promote adherence is often done in an ad hoc manner without relying on a theoretical framework for the design of such interventions, missing in the process capabilities that would make the digital interventions more appealing and potentially improve adherence.

Recommendation. Creators of digital health interventions are encouraged to investigate and plan a-priori for the design of digital interventions using a theoretical framework or approach, which aims to engage or motivate users Ă“ 2019 Hogrefe Publishing

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to adhere to the intervention. It is recommended that researchers keep abreast of the latest research regarding theory-driven digital intervention design. Frequently used search terms to obtain access to theory-driven design frameworks may include: design, adherence, user engagement, theory-driven and/or framework, e-health, digital, motivation.

Furthermore, it is important that researchers include and clearly describe the use of a-priori design planning in their methodology.

To our knowledge, there is a scarcity in digital design theories reported in the literature. The two most widely researched theories, which will be emphasized in this paper are: (1) gamification and (2) persuasive technology. Gamification is the use of game design features in nongame contexts such as healthcare applications or digital interventions (Deterding, Dixon, Khaled, & Nacke, 2011). Persuasive technology is a theory-driven digital intervention design framework, which suggests that technology itself has the capacity of being persuasive (or engaging) through its role as a tool, a medium, and a creator of experiences (Fogg, 2002). The rationale and application of the features comprising these two conceptual approaches are discussed in more detail in the recommendations that follow.

2. Digital Interventions Should Consist of Theory-Driven Evidence-Based Psychological Intervention Content Rationale. The Web is filled with information, however, the credibility of the material and sources producing the material are often put to question and may even confuse users.

Indeed, a systematic overview of the impact of e-health on the quality and safety of health care, reports that numerous digital interventions make claims, which are not substantiated by empirical evidence (Black et al., 2011). Globally, there is a challenge to implement and disseminate empirically supported psychological treatments. Fairburn and Patel (2017) report that the coverage of such treatments does not exceed 50% in any country. Morrison (2015) argues that in order to enhance the impact and usage of digital interventions, it is essential that psychological theories (such as theories of social support and motivation) guide and inform the designing and delivering of these interventions. Therefore, best practices of digital interventions need to be implemented, in order for empirically supported treatments to be disseminated widely.

According to persuasive technology theory, a system must convey to the user a sense of system credibility (Fogg, 2002), through the following principles: (1) trustworthiness (i.e., system providing truthful, fair, and unbiased information); (2) expertise (i.e., system providing information demonstrating knowledge, experience, and competence); (3) surface credibility (i.e., similar to face validity, a system should provide a sense of credibility to the users upon first inspection); (4) real-world feel (i.e., system providing

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information or means to communicate with the people behind its content); (5) authority (i.e., system quoting that the material originates or has been evaluated by an acknowledged authority); (6) third-party endorsements (i. e., system providing endorsements from respected and renowned sources, such as a university); and (7) verifiability (i.e., system providing means to verify the accuracy of the site content via outside sources such as peer-reviewed research articles). A system that uses content from theory-driven evidence-based psychological approaches may provide a sense of trustworthiness, expertise, and credibility to the user. Previous research indicated that perceived credibility (as indicated by trust/expertise and depth) was significantly related to the intention of the user revisiting a Website (Hong, 2006).

Recommendation. It is encouraged that digital interventions for health-related problems, be based on theory-driven empirically supported psychological interventions. For example, a digital intervention for pain management may base its contents on an evidence-based psychological intervention, such as Acceptance and Commitment Therapy (Hayes, Strosahl, & Wilson, 1999), classified as an empirically supported treatment for general chronic pain conditions (Society of Clinical Psychology, Division 12 of the APA, 2011). Such an example is the recently developed “AlgeApp” chronic pain digital intervention (Karekla et al., 2017). To provide a sense of trustworthiness and credibility, the seven persuasive technology theory principles can be utilized. If the content is provided in written formats, citations, and empirical references supporting the approach can be used; whereas if the content is provided in an audiovisual dynamic format, links or preferably nonintrusive pop-up information should be accessible to the user who wants to check the credibility of the source.

3. The Planning of Digital Interventions Should Take Into Account Relevant Ethical Considerations Rationale. Many users seek help through digital interventions because they perceive this medium to offer numerous benefits (e.g., convenience of time and space, anonymity, lack of stigma). However, the absence of a physical presence may present with several challenges regarding ethical considerations, which may act as a barrier to the trustworthiness and safe use of the intervention (Hsiung, 2001).

Perhaps one of the most significant concerns is privacy of sensitive information (Arora, Yttri, & Nilsen, 2014). Confidentiality is generally recognized to constitute the basis for a helping relationship and building of trust (Haas & Malouf, 2002; Koocher & Keith-Spiegel, 1998). However, therapists need to be able to maintain practices of emergency and to act in the best interest of their clients. The ease of accessibility and relative anonymity within digital

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interventions may paradoxically make it more challenging to identify participants who might be in crisis (e.g., having suicidal or homicidal thoughts) or suffer from additional mental health problems and may need additional care (e.g., active psychosis, manic episodes, or substance abuse disorders). Ethical issues and especially ones of privacy, confidentiality, and emergency should be considered when planning a digital intervention, including consideration for the rights of non-research participant users.

Recommendation. Ethical considerations and solutions (particularly informed consent, anonymity and confidentiality, promoting welfare and avoidance of harm, and competency) should be considered during the developing stages of a project. Operating procedures need to be established so as to deal with ethical issues that may arise (Arora et al., 2014). Developing a-priori practices and principles of conduct for digital health projects is a crucial step in enhancing data collection and ensuring participant safety. According to the International Society for Mental Health Online (ISMHO, 2000) and the European Group of Ethics (2012), researchers should provide all users with an informed consent consisting of a detailed description of: (1) the purpose and process, (2) contact details of researchers, (3) any potential benefits (e.g., convenience of time and space, low cost, anonymity), (4) any risks that may arise from the use of the digital intervention (e.g., likelihood of technical difficulties, breach of confidentiality if the digital means are used in public areas or by others), and (5) safeguards taken to ensure confidentiality and privacy (e.g., use of encrypted platform of communication). Additionally, providing the users with transparency (clearly informing users) and the right to withdraw from the study or have their data deleted should be fundamental for all digital interventions (European Group of Ethics, 2012).

The inclusion of participants with certain mental health problems (e.g., active suicidal ideation, psychosis, manic episodes) or at-risk populations (e.g., drug users) entails risks and especially ethical concerns related to mandated reporting of suicidal, homicidal, or abusive situations. Creators of digital interventions need to assess from the beginning for these situations and establish action plans on how to deal with them, if they arise. Further, inclusion of comorbid active mental health problems may interfere with an intervention, contribute to nonadherence, or may render a program not suitable for the needs of these participants.

It is paramount that users are informed about the parameters of the program and are provided with alternative treatment options or a stepped care model of treatment. Overall, digital programs should provide users with relevant and accurate information regarding the content and goals of the specific program, including expected outcomes following program completion. The absence of such information leads to false or unrealistic expectations, Ó 2019 Hogrefe Publishing

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disappointment or lack of motivation to engage with the program, and consequently non-completion. For example, if a digital intervention aims to aid individuals with chronic pain to live a more valued life, users whose primary goal is to get rid of their pain or reduce its intensity are likely to feel unmotivated or even tricked by the researchers. This in turn will affect their engagement, adherence, and future involvement with digitalized interventions.

Dimension II: Human–Computer Interaction 4. Apply Theory-Driven and Empirically Supported Technological Characteristics in Digital Interventions Rationale. As stated in recommendation 1, digital intervention developers and researchers are encouraged to plan a-priori for adherence using theory-driven approaches to digitalization. This recommendation presents details regarding the most prominent theory-driven approaches and provides examples of how they can best be applied in practice.

Gamification, utilizes behavioral principles of, for example, positive reinforcement, along with mechanisms found to make games addictive and implements these principles in nongame digital situations (Cugelman, 2013). Reeves and Read (2009) present the following gamification principles as important: self-representation with avatars, threedimensional environments, narrative context, feedback, competition among users via ranks and levels achieved within a game, rewards, teaming up and communicating in parallel with others to achieve a goal, and time pressure.

Examples of gamified positive reinforcement include ingame rewards, badges, and challenges. These are found to be highly effective resulting in high usage and adherence (Cobb & Poirier, 2014). Similar support has been provided for gamification components of, visualization of progress and automated goal setting activities (Irvine, Gelatt, Seeley, Macfarlane, & Gau, 2013), and inclusion of a story line (Imamura et al., 2014). A recent systematic review of 61 RCTs evaluating 47 different intervention programs and their effects on adherence (Brown et al., 2016) found active gamified ingredients to explain only 9.4% of variance, suggesting that gamification features may not impact adherence to the level expected. Future studies are needed to compare interventions with versus without the inclusion of gamified elements (and different types of elements) for improving adherence, utilizing rigorous randomized trial methodologies. Until more research becomes available, researchers should be cautious when designing digital interventions to improve adherence based on gamified models alone.

Persuasive technology is another dominant theory-driven digital intervention design framework. A persuasive system may be defined as an information system (e.g., computerized software, Web-based intervention, smartphone Ó 2019 Hogrefe Publishing

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application), which by its nature can reinforce, change, or shape attitudes and/or behaviors (Oinas-Kukkonen & Harjumaa, 2009). Although it has been argued that all information systems may be regarded as capable of influencing users in some way (Oinas-Kukkonen, 2013), for a technology to be called persuasive, the persuasion has to be intentional or designed purposefully to guide the user toward behavior change (Fogg, 1997). Fogg (2002) proposed the “Persuasive System Design” (PSD) framework comprised of 28 technological characteristics in order to guide the design of interventions with an eye toward engagement and user adherence (Oinas-Kukkonen, 2013). These principles are distributed into the following 4 categories: (1) primary task support: principles supporting the core activity or behavior; (2) dialogue support: principles related to the user receiving feedback from the system; (3) system credibility support: principles responsible for making the system seem credible and trustworthy; and (4) social support: principles inducing motivation through social influence.

A review and meta-analysis of 83 digital interventions examining whether persuasive design influences adherence (Kelders et al., 2012) found 55% of variance in adherence being explained by human–computer interaction variables, such as PSD. Lacking from analysis was the category of “system credibility support,” as included studies did not report on this feature. Multiple regression analysis suggested that only the “dialogue support” category significantly contributed to adherence. Dialogue support features aim to engage the user through human–computer interaction via providing a significant degree of system feedback in a way that helps users keep moving toward their stated goal or target behaviors. An example of a dialogue support feature would be the use of text or e-mail reminders between intervention sessions to remind a user of a pre-reported goal (e.g., fitting into a dress in the case of a weight-loss program, or of improving breathing capability in a smoking cessation trial). Dialogue support features consist of: praise, rewards, reminders, suggestion, similarity, liking, and social role. Despite the significant contribution of dialogue support features in user adherence, Kelders and colleagues (2012) found that they are less commonly employed in digital interventions, with a mean use of 1.5 out of 7 possible features used in the interventions examined. Reminders and suggestions were the two most frequently employed features, while positive reinforcement (e.g., praise and rewards) was seldom used. This is interesting when considering that positive reinforcement is among the best means of establishing new behaviors and is one of the most empirically supported techniques available (Abraham & Michie, 2008; Michie et al., 2013). Yet, it appears that when transporting behavior change programs into digital means, the creators neglect the basic psychological theories of change (see also Recommendation 2).

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Recommendation. It is recommended that interventions be developed based on persuasive technology and particularly by incorporating the 7 dialogue support system features. Specific suggestions include: Praise: A key operant conditioning component is providing positive verbal reinforcement when a desired behavior is observed. Fogg (1997) found that a single dialog box praising the user was enough to engage, motivate, persuade, and empower the user without any actual available social presence (i.e., of a therapist). Praise can include words, images, symbols, or sounds as a way to provide feedback information based on exhibited behavior. An example can be the use of automated text messages for reaching individual exercise goals each day.

Rewards: Providing rewards works via a similar positive reinforcement mechanism as praise. This feature is common to both persuasive and gamified interventions as a means to signal achievement to the user. Examples of virtual rewards are budges, virtual trophies, allowance to alter media items (e.g., change sounds, backgrounds, avatar gender), or points that can be cashed in for an external reward (e.g., airline miles bonus; Liu, Hodgson, Zbib, Payne, & Nolan, 2014). Successful application of rewards follows traditional behavioral schedules of reinforcement and involves systematic and successive rewarding of desired behaviors while switching to intermittent rewards midway (Reynolds, 1968).

Reminders: The use of reminders to prompt target behaviors is again rooted in behavior theory and particularly the concept of shaping desired behavior. Digital intervention trials present the importance of reminders in increasing adherence, intervention effectiveness, and the likelihood that the user will achieve their goals (Neff & Fry, 2009; Webb, Joseph, Yardley, & Michie, 2010). Reminders should be frequent; however, the exact frequency is not yet determined; thus, this is stated with caution as many reminders may lead to notification fatigue (Dennison, Morrison, Conway, & Yardley, 2013; Proudfoot et al., 2010). Additionally, the provision of automatic reminders may lower the cost of an intervention while increasing completion rates (Titov et al., 2013).

Suggestion: Suggestions, similar to reminders, tend to be more direct. Suggestions often imply that those behind the system are experts providing their opinion about a particular behavior and its change. Suggestions may include recommendations based on empirical findings and offered in response to a user’s comment, data input, or reactions to questions posed within the intervention, etc. In order for suggestions to be effective, they have to provide the right content at the right time. An example illustrating the use of suggestions is in cases of promoting sleep hygiene in patients with chronic pain, and an

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automated message is sent to the user in the afternoon, proposing that they focus on activities and remain awake until the evening.

Similarity: System components should be designed to appear familiar to users in some meaningful way. According to the theory of self-efficacy (Bandura, 1982), people complete a task if they perceive themselves as capable of executing it, especially after observing someone they consider similar to themselves performing the behavior.

An example of utilizing the principle of similarity includes, in a digital intervention for chronic pain management, avatars (in the role of psycho-educators) are designed to visually resemble an average chronic pain user in terms of age, gender, language spoken, and health condition (e.g., exhibit movement difficulties; Karekla, 2017).

Liking: Involves the development of a system that is aesthetically attractive to its users. Geissler, Zinkhan, and Watson (2006) state that aesthetically appealing designs encourage users to continue browsing. Sonderegger and Sauer (2010) manipulated the visual appearance (highly appealing vs. non-appealing) of two functionally identical mobile phones and found that participants with highly appealing phones rated their appliance as more usable than participants with the unappealing model. Designs that use a variety of well-designed images, videos, and animations are more likely to capture users’ interest and engage them in the intervention.

Social role: Social role presents the medium through which the 7 system credibility principles (trustworthiness, expertise, surface credibility, real-world feel, authority, third-party endorsements, and verifiability; Fogg, 2002) can be transferred to the user. This medium might be in the form of a virtual character who also has a real-world feel and might have the role of a healthcare specialist, a “co-traveler,” a psycho-educator, a co-patient, and so forth (Karekla, 2017).

5. Digital Interventions Should, if Suitable, Include Human or a Sense of Human Contact to Promote User Accountability Rationale. Human or a sense of human contact has several layers and involves: building a therapeutic alliance, feeling accountable, and opportunity to contact a human if needed.

Poor therapeutic alliance is a significant predictor of premature termination (Mohl, Martinez, Ticknor, Huang, & Cordell, 1991). In face-to-face interventions, the therapist can coordinate his/her actions while simultaneously delivering intervention content, so as to establish and maintain a therapeutic relationship that will benefit and keep the client interested to continue the treatment. In digital interventions, however, content is provided through a

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medium (e.g., mobile device) in the form of texts, e-mails, and phone calls, which may create an obstacle for a therapeutic bond to be effectively built. Jasper and colleagues (2014) found that more time was needed to build a strong alliance in a digital intervention compared to a face-to-face intervention. Obstacles to fostering alliance in digital interventions included having too little information on the therapist behind the intervention.

The model of supportive accountability (Mohr, Cuijpers, & Lehman, 2011) proposes that effective researcher–user interaction can be achieved when mediated by a sense of accountability. Accountability refers to implicit or explicit expectations that an individual may be called upon to justify their actions or inactions (Lerner & Tetlock, 1999).

Accountability may be created when clear adherence goals are stated, and there are monitored and scheduled contacts to review the user’s progress. Yet, for accountability, it is necessary that researchers must be perceived (through their electronic interaction) by the users as trustworthy, benevolent, and expert (Mohr et al., 2011). There is evidence to suggest that when the users are accountable for their active participation in the intervention in a broken-down, processoriented (step-by-step) expectation (e.g., see following recommendation section), they are more likely to complete the target behaviors and produce better outcomes than when the user is accountable in an outcome-oriented expectation (Adams, 1965; Konow, 2000).

Additionally, the Efficiency Model of Support (Schueller, Tomasino, & Mohr, 2016) suggests that providing support resources should be based on assessing areas that the user may be lacking in (e.g., motivation, meeting needs of user, having sufficient knowledge to use an online tool). Human contact in this model is used to address these areas, which may further contribute to the therapeutic alliance (Schueller et al., 2016).

Digital interventions with some form of human support have a significantly greater success in engaging users than those without therapist guidance (Palmqvist, Carlbring, & Andersson, 2007). Baumeister, Reichler, Munzinger, and Lin (2014), in a systematic review, found that digital interventions with accompanying therapeutic support had a significantly lower dropout rate (OR = 2.67), more implemented modules per intervention (g = 0.52), and achieved great reduction of symptoms (g = 0.27) than interventions without such support. Reviews examining the dose-efficacy relation support that the number of completed modules rises with increased human support time.

However, an increase of therapist time beyond 100 min per patient within a 10-week digital intervention was found to have no incremental additional effect on efficacy (Andersson, Carlbring, Berger, Almlöv, & Cuijpers, 2009; Johansson & Andersson, 2012; Palmqvist et al., 2007).

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Recommendation. In order to establish a therapist-client relationship as in traditional therapeutic modalities, enhance accountability, and provide the sense of human contact, digital programs are encouraged to ask the users a-priori for permission to electronically interact with them.

It may be explained to users that permission to interact is sought, so as to provide useful and tailored feedback for improving their experience and enhancing possible benefits from the digital intervention. This interaction should be frequent (but not excessively so, though the exact helpful amount has not yet been determined), by providing encouragement and tailored feedback according to responses (utilizing previous recommendations). A way to foster therapeutic alliance could involve the therapist or animated virtual characters purposely revealing information about them, especially if offered details present a shared perspective with the user.

Utilization of broken-down, process-oriented expectations of usage, where the users’ active involvement is sought, is more likely to achieve accountability. Examples of process-oriented expectations include instructions to complete one module per week, to complete the specific activity within a specific time frame, to keep a weekly record of thoughts, and to log in x times per week. Active involvement may come in the form of users being provided with the option of what time during the day they may log in or be prompted (similar to making an appointment). Generic outcome-oriented expectations (e.g., instructions to complete all modules and activities) should be avoided.

Information about the development of the intervention and the human entities (e.g., researchers) behind an intervention should be made available to the user. Also a way to contact the researchers if need arises should be made available. Finally, as proposed by the Efficiency Model of Support, assessing and conceptualizing the needs of the user prior to providing human contact (e.g., if a user appears unable to complete a weight diary as intended probably due to low computer literacy as assessed prior to the intervention may be contacted by a human via telephone to guide them through its completion) may be more beneficial (Schueller et al., 2016).

6. Digital Interventions Benefit From Frequent Content Update Rationale. Digital intervention visits tend to decrease sharply after the initial weeks of use. Regular and frequent intervention content updates, that are made known to users through e-mail or text prompts, are related to repeated visits and higher frequency of user logins (Brouwer et al., 2011). Kelders and colleagues (2012) found that frequent updates of digital interventions are associated with user adherence.

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Recommendation. It is recommended that digital interventions are frequently updated with new content, addition of new modules, or with changes in the layout. Updates are more likely to attract the user’s attention if followed by e-mail or text prompts informing about updates.

Dimension III: Tailoring and Targeting to User Groups 7. Digital Interventions Should Be Designed to Take Into Account Known User Characteristics That Improve Adherence and Engagement Exploration of user-related sociodemographic variables provides useful information in understanding and predicting which individuals are at dropout risk. Numerous user characteristics and sociodemographic variables (e.g., gender, age, duration and severity of illness, quality of relationships, and computer literacy) are associated with lower adherence and/or higher dropout rates (Carlbring et al., 2001; Lange, Van De Ven, & Schrieken, 2003; Macea et al., 2010; Melville, Casey, & Kavanagh, 2010; Zarski et al., 2016). Knowledge of characteristics that place individuals at risk for dropping out enables the early identification of disengaged users and the implementation of strategies to prevent dropout.

To date, there are only two systematic reviews that investigated the impact of sociodemographic predictors of dropout within digital interventions, however, one focused on psychological disorders (Melville et al., 2010), whereas the other focused on chronic pain (Macea et al., 2010).

We were unable to find specific reviews or meta-analysis of predictors of dropout from digital interventions for various chronic health conditions. Thus, the rationale and recommendations for sociodemographic predictors of dropout (e.g., youth, males) below are based on these available reviews and studies.

7.1. Digital Interventions Should Be Particularly Careful When the Target Population Includes Youth Rationale. Studies of digital interventions for a variety of health-related behaviors (e.g., improving healthy diet and physical activity, diabetes self-management, smoking cessation) consistently report that younger users present with higher dropout rates whereas increasing age is associated with more adherence and usage as illustrated by number of logins (Kelders et al., 2011; Wangberg et al., 2008). Interestingly, however, youth utilizes digital technologies at greater rates (Buckingham, 2008). Possible explanations for lower youth adherence include that older participants may experience more health problems compared to youth (Japuntich et al., 2006). Additionally, youth who are more technologically savvy may find the quality or technological features of digital health interventions less interesting or challenging. However, numerous prevention and intervention programs (e.g., for smoking or healthy diet) particularly

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aim to target youth at an early age, so it is important to consider how digital means may be utilized to attract and engage youth.

Recommendation. Researchers are encouraged to be vigilant (e.g., oversample) when recruiting youth in digital interventions. Prior to designing a digital intervention, researchers are also encouraged to perform focus groups with youth in order to obtain feedback or suggestions of design features and intervention structure. Personalization and tailored interventions to youths’ needs, including specific youth communication features, language, music, videos, and unique interests, may increase engagement and motivation. In addition, the inclusion of gaming elements within digital interventions might be of particular importance for young participants.

7.2. Digital Interventions Should Be Particularly Careful When the Target Population Includes Males Rationale. Similar to traditional interventions, female participants are more likely to adhere to Internet-based programs and less likely to drop out (Lange et al., 2003; Macea et al., 2010). Lange and colleagues (2003), for example, reported 71% of males compared to 19% of females dropped out of their digital intervention, a pattern repeatedly observed by others and for a variety of health-related conditions (Carpenter, Stoner, Mundt, & Stoelb, 2012). Explanations of this phenomenon may be that as in traditional interventions, more females as opposed to males seek help and psychological interventions may be more appealing to women than men.

Recommendation. During the design of a digital intervention, it is proposed that attempts are made to personalize and tailor the content to be applicable and engaging for male users as well as females. Suggestions include the provision of choices (e.g., choice of a male or female avatar within a digital program; see Savvides & Karekla, 2015), material that may fall within the particular interests of males, and inclusion of motivational interviewing elements or values clarification prior to the initiation of an intervention.

7.3. Digital Interventions Should Be Particularly Careful When the Target Population Includes Long-Standing and Severe Chronic Health Conditions and Particularly When Chronic Pain or Psychological Problems Are Involved Rationale. Many chronic health conditions involve chronic pain (e.g., fibromyalgia, rheumatoid arthritis, chronic low back pain, chronic migraines, peripheral neuropathy, cancer pain, diabetic pain). Macea and colleagues (2010) in their meta-analysis showed that longer duration of chronic pain might significantly increase the likelihood of users dropping out of digital intervention. DiMatteo, Haskard, and Ó 2019 Hogrefe Publishing

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Williams (2007) in their meta-analysis reported similar associations between chronicity of health conditions and nonadherence and both objective and subjective severity of health condition and dropout (DiMatteo et al., 2007).

Users with longer duration and greater severity of chronic health condition may have an array of unsuccessful condition management or treatment attempts, which may result in lowered motivation to engage in new approaches (Horne & Weinman, 1999). Further, as severity of condition increases, expectations and interactions with health providers reduce in quality, making the more severely ill more prone to drop out (Hall, Roter, Milburn, & Daltroy, 1996; Kravitz et al., 2002).

Additionally, usually individuals with long-standing chronic health conditions especially those involving pain may become overly dependent on substance use (e.g., analgesics, opioids, alcohol) to manage their pain (Fishbain, Rosomoff, & Rosomoff, 1992; Hoffmann, Olofsson, Salen, & Wickstrom, 1995). Users with comorbid conditions are more likely to drop out and may also be more hesitant to utilize psychologically based methods (Corsonello et al., 2010). Moreover, poor adherence in patients with chronic health conditions may be mediated by feelings of pessimism or depression, social withdrawal, and hopelessness about survival (Carney et al., 1995; Christensen, Benotsch, & Smith, 1997).

Recommendation. Any digital intervention should include a thorough assessment of previous experiences with psychological interventions, previous unsuccessful attempts, motivation to engage in the digital intervention, and screen for substance use disorders or overmedication. Given that chronic users may have extensive experiences with traditional therapy modalities, any benefits of digital interventions (e.g., convenience of time and space, anonymity, minimization of dependency on others to drive them to treatment, self-management solutions) should be highlighted. Limitations of digital interventions should also be explicated from the beginning so as to reduce the likelihood of disappointment and de-motivation.

7.4. Digital Interventions Should Examine and Involve Where Possible the Social Environment of the User Rationale. The quality of an individual’s social environment (e.g., quality of the relationship with a significant other) may influence adherence in a digital intervention (Melville et al., 2010). Supportive partners who motivate the continued involvement of the users may lead to a lower risk of dropout. However, partners in addition to being a source of social support may actually be a potential source of stress (e.g., communication problems may result; Reid, Eccleston, & Pillemer, 2015) or even treatment saboteurs (most of the time without consciously realizing this) in a patient’s attempts for change (Kavanagh, 1992). For example, when Ó 2019 Hogrefe Publishing

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spouses of patients with diabetes conveyed disregard with the diabetics’ diet, the result was worse dietary adherence (Henry, Rook, Stephens, & Franks, 2013).

Recommendation. Digital interventions may include an assessment of perceived social support and of the relationship of the user with significant others or caretakers. Significant others may also be asked to become involved and have their attitudes toward the intervention assessed prior to commencing the intervention. Though matching and tailoring partner responses may be tricky for digital interventions and may increase chances of nonadherence when two people are involved, it can be accomplished via a variety of ways: (1) in the form of developing a dual platform where caregivers or significant others receive in parallel support, psychoeducation or online counseling about the impact that their support is having on the user, and management of their attitudes toward treatment; (2) providing for example additional relationship and communication with significant other modules for individuals who present with deficiencies in their social interactions; (3) positively reinforcing successful interactions and reduced negative expressed emotion.

8. Digital Interventions Should Include an Assessment of Computer Knowledge and Experience and Provide Easy Tutorials and Technical Assistance Rationale. One of the most common reasons for users dropping out of digital interventions involves computer illiteracy or lack of sufficient computer knowledge or experience (Carlbring et al., 2001; Kenwright, Marks, Gega, & Mataix-Cols, 2004; Lange et al., 2003). Common technical problems may occur when interacting with digital means, such as with Internet connection, slow loading of a Website, security concerns (Kuijpers, Groen, Aaronson, & Harten, 2013), or incompatibility issues (e.g., pre or post a software update). If technical difficulties occur and the user lacks the knowledge to respond to them, it is more likely that they will feel disengaged and discontinue.

Recommendation. Digital interventions should first assess their users’ levels of computing literacy and experience.

Second, developers should aim for use simplicity. This will increase the possibility for noncomputer literate individuals to be able to interact with the delivery medium (especially in cases where the target population is expected to have minimal interaction with digital means). Technical assistance should be provided as quickly and efficiently as possible. The ideal would be to have a 24-hour online technical support; however, we acknowledge that this may be impossible for most programs. Digital interventions should be designed for usage in a broad array of operating systems (e.g., Microsoft Windows, Mac OS) and platforms (e.g., computers, tablets, smartphones). Short video tutorials on ways to use the intervention, seek support, or solve

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common technical issues should be readily available and presented to the users before the intervention.

Dimension IV: Active Assessment of Usage 9. Instructions for Use Should Be Simple and Direct Rationale. According to Kelders and colleagues (2012), one of the most significant contributors to adherence in digital interventions is frequent intended usage. Frequent intended usage implies that users are provided with clear instructions and expectations about their active interaction with the intervention.

Recommendation. Users should be informed about intended usage prior to the intervention and be provided with clear expectations and simple instructions for use.

According to the model of supportive accountability, the user should be involved in the definitions of goals and expectations. Adequate justification of expectations (e.g., explanation about why the user is asked to interact with a digital intervention daily) should be followed by a mutual agreement, framed in terms of the benefits to the user (Mohr et al., 2011).

10. Digital Interventions may Utilize Web-Metrics to Assess and Monitor Adherence of Disengaged Users Rationale. Digital interventions need reliable and objective means of assessing adherence and engagement, while being sensitive to maintain user anonymity. A problem of assessing adherence across different digital intervention studies involves that variable means of assessment are used without uniformity across studies. Yardley and colleagues (2016) describe the value and considerations for using different methods of assessing engagement in digital interventions (e.g., self-report questionnaires, ecological momentary assessment, qualitative analysis of self-report data, smartphone sensors, logs of system usage data, and physiological measures); however, it is also important to establish specific assessment means so as to enable comparisons between studies.

Digital interventions have an advantage over face-to-face interventions for assessing adherence, in that Web-metrics, which are objective measures of usage are readily available via the digital medium (Donkin et al., 2011). The availability of Web-metrics (e.g., number of logins, activities and modules completed, time spent online, and pages opened) is a particular advantage of digital interventions, as traditional interventions usually have to rely on subjective means of retrospective self-report amenable to recall bias for information about adherence. However, despite the relative availability of Web-metrics, very few studies actually report Web-metric statistics, while some even continue to use selfreport for assessing adherence (Brouwer et al., 2011).

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and qualitative analysis of self-report, ecological momentary assessment, physiological measures, for accurate monitoring and tracking of users and thus efficiently and immediately intervene to reengage a user if needed. Christensen, Griffiths, Korten, Brittliffe, and Groves (2004) found that weekly tracking followed by reminders significantly reduced attrition in a cognitive behavioral therapy (CBT) intervention for depression. In addition, awareness of having ones performance or adherence monitored is found to increase accountability toward the intervention (Mohr et al., 2011), which leads to increased engagement.

However, if adherence monitoring is perceived as surveillance or as controlling without explicit rational, it may reduce compliance and contribute to demoralization (Lerner & Tetlock, 1999) and damage user adherence (Checkland, Marshall, & Harrison, 2004; Enzle & Anderson, 1993).

It is important to consider that disengagement from an intervention may mean a number of things (Yardley et al., 2016). For example, discontinued engagement may be due to successful behavior change or positive health outcomes mirroring sufficient mastery to the extent that the digital intervention is no longer needed. On the other hand, it may result from unsuccessful change/poor outcome, the user being bored with the specific intervention or facing technical issues with the device. High engagement and improved outcomes may be both due to individual characteristics (e.g., higher commitment to the procedures/greater motivation), or they may be observed in a study setting and not real life (because someone is monitored and expected to engage), or it may mean that someone struggles and requires more support. Given the range of reasons that may affect user engagement, it is important to utilize various methods of assessment and especially the easily available Web-metrics to be able to better clarify and understand the phenomenon of engagement in its different contexts.

Recommendation. Firstly, we encourage the use of all metrics available when assessing for adherence. Additionally as proposed by Donkin and colleagues (2011), measures of inactivity or idle time may also be useful. A time-out message in the form of a positive prompt can be included within the digital intervention for users who appear to be unresponsive for some time. Reporting of adherence (e.g., via Web-metrics) should be included in publications of findings.

Secondly, use of Web-metrics may be a useful way to identify the stage where a user begins to feel disengaged.

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as intended, could be personally contacted (via text, e-mail, or phone) with tailored feedback according to their needs, aiming to motivate reengagement. For instance, if a user appears disengaged, yet has expressed as part of the intervention that spending time with family is important to him/ her, a message reminding the user of their stated value and goals may motivate and reengage the user. Monitoring of engagement may also be helpful in cases where users are found to be at higher risk for dropout (e.g., present with sociodemographic parameters that place them at risk as discussed previously).

Prior to monitoring and contacting a disengaged user, researchers are encouraged to provide an adequate explanation to the user (e.g., in the consent form) about when they may be contacted and how this will be done. Clarifying to the user that the aim of monitoring is to provide feedback and indications of disengagement so as to motivate and mobilize the user to reengage and achieve stated goals.

It is important to ensure that all this is done in junction with the ethical considerations presented previously and clarifying to the individual that there will be no negative consequences, implications, or penalties if they choose to discontinue the intervention.

Discussion Digital interventions for chronic health conditions have bloomed over the past few years, with the advent of technology and the widespread accessibility of digital means.

Yet research on digital interventions and particularly on issues related to engagement and adherence of users remains in its infancy. This paper provided an overview of user engagement factors and proposed research informed and consensual recommendations (from two EFPA committees: Psychology and Health Standing Committee and e-Health Task Force) for engagement and adherence planning in digital intervention development. A four-dimensional set of 10 recommendations consisting of: (1) a-priori theoretical planning, (2) human–computer interaction factors, (3) tailoring and targeting to use groups, and (4) active assessment of usage were proposed, with supporting evidence and suggested recommendations. A combination of a-priori theoretical planning in terms of psychological approaches and designing of the intervention while maintaining ethical principles in the field of health is of paramount importance. This paper further argued that user characteristics (such as age and gender) in addition to computer literacy of users should be considered when planning a digital intervention and ways to do this were proposed. Finally, active monitoring using Web-metrics and intervening to reengage users early was suggested so as to significantly boost adherence rates. Ó 2019 Hogrefe Publishing

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Researchers and developers are strongly encouraged to be mindful of best practices and recommendations in respect to digital interventions and work as a team of professionals to achieve these. Specialists working in groups could put forward and be responsible for recommendations mostly tied to their specialty field. For example, the clinician or researcher could bring forth knowledge regarding theory-driven evidence-based psychological intervention content whereas the developer could propose empirically supported technological characteristics. However, recommendations such as maintaining ethical standards should be overarching across the whole digital intervention development and application process.

Future systematic reviews of adherence in digital interventions for chronic health conditions should include an examination of a combination of user characteristics and human–computer interaction factors, including mediators and moderators of change, so as to more clearly elucidate parameters affecting adherence to digital interventions.

We hope that the proposed recommendations will be adapted and examined by future research so as to investigate their impact on user engagement and adherence for chronic health conditions, but also for other chronic mental health difficulties and even acute conditions and health behavior change. This paper adopted a general definition of digital interventions to include a range of approaches from self-monitoring apps to Internet-based interventions to simple drug intake reminders. Again future research should examine whether the proposed recommendations are equally relevant and significant for all interventions modalities included under the digital intervention umbrella.

Overall, we strongly urge researchers to use a holistic apriori theoretical approach and planning and to consider the recommendations from all four proposed dimensions when designing a digital intervention. We hope that these recommendations will serve as a reference point to inform researchers and developers on how to maximize user engagement, which in turn will lead to greater adherence, lower attrition rates, optimized exposure to the intervention, and improve effectiveness in the treatment of chronic health conditions via digital interventions.

Electronic Supplementary Materials The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/ 1016-9040/a000349 ESM 1. Table (.pdf) Engagement and adherence to digital interventions: challenges, recommendations, and examples.

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History Received May 2, 2017 Revision received February 22, 2018 Accepted April 19, 2018 Published online February 11, 2019 Acknowledgments This review has been prepared in association with the European Federation of Psychology Associations, Psychology and Health Standing Committee, and e-Health Task Force. The authors wish to acknowledge and thank the expert members of the two European Federation of Psychology Associations’ (EFPA) committees: Psychology and Health Standing Committee and e-Health Task Force. The authors state that each member association appoints the individual members in these committees as their local experts in the respective field.

Maria Karekla Department of Psychology University of Cyprus 2108 Nicosia Republic of Cyprus mkarekla@ucy.ac.cy

Maria Karekla (PhD) currently holds the position of Assistant Professor of Clinical Psychology at the University of Cyprus and is the chair of the Clinical Psychology Doctorate committee. Her research focuses on areas of health promotion and the investigation of individual difference factors (especially experiential avoidance) as they relate to the development and maintenance of various behavioral difficulties (especially anxiety and health-related problems). She is a member of the Cyprus Psychologist Licensing Board, the EFPA Psychology and Health and e-Health Task Forces.

Orestis Kasinopoulos (MSc) is a PhD student in the Clinical Psychology Doctorate program at the University of Cyprus, Department of Psychology.

He is an external associate to the Spinal Cord Injury Rehabilitation Unit, Nicosia General Hospital, providing counseling and support services to physically disabled patients. In his PhD thesis, he has developed a digital ACT-based application, which aims to investigate the effectiveness in improving chronic pain management.

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David Dias Neto (PhD) is a Lecturer at Instituto Superior de Psicologia Aplicada – Instituto Universitário. He has published in the areas of process research in psychotherapy and clinical and health psychology. He is the current president of the clinical and health psychology division of the Portuguese Order of Psychologists.

He also works as a psychotherapist in private practice.

David Daniel Ebert (PhD, 2013) leads the E-Mental Health Unit and Behavioral Health Promotion & Technology (PROTECT) Laboratory at the Friedrich-Alexander-University Erlangen-Nuremberg. His work focuses on the prevention of mental health disorders in different settings (e.g., work, university), with a focus on the evaluation of Internet and mobilebased behavioral health technology interventions.

Tom Van Daele (PhD) is the head of the Expertise Unit Psychology, Technology & Society at Thomas More University of Applied Sciences (Antwerp, Belgium), a research fellow at KU Leuven (Leuven, Belgium) and the convenor of the EFPA TF on e-health.

His research focuses on e-mental health, m-health, and their relevance to mental health care and (mental) health prevention and promotion.

Tine Nordgreen (PhD, 2011) has been actively involved in scientific publications, grant applications, supervision of students on a master and PhD level and held presentations on national and international conferences in the field of Internet-based treatment. She is currently the PI of a cross-disciplinary project (http://intromat.no/), including 14 partners, with the aim to develop new technology supported treatments for various settings.

Stefan Höfer (PhD) is Associate Professor of Psychology at the Innsbruck Medical University, Department of Medical Psychology, Austria. He is an internationally well-regarded expert in the field of patient-reported outcome assessment, psychometrics, and health psychology. He served from 2015–2017 as convenor of the EFPA standing committee of psychology and health.

Ó 2019 Hogrefe Publishing

M. Karekla et al., Digital Interventions Adherence Recommendations

Svein Oeverland is a Clinical Psychologist. He is a specialist in both child/adolescent psychology and forensic psychology. He is both the manager of the digital health company Superego and Head of the national forensic unit of mandatory care. He is the editor of the Journal Sexology of the Norwegian Union of Clinical and Scientific Sexology.

Ó 2019 Hogrefe Publishing

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Kit Lisbeth Jensen is Psychologist and Clinical Psychologist and works in her private practice in Denmark.

She has worked in the field of ehealth since 2010 and is member of several Danish committees. She has also been a member of the e-Health Task Force 2015–2017.

European Psychologist (2019), 24(1), 49–67

Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Association Between Health Literacy, eHealth Literacy, and Health Outcomes Among Patients With Long-Term Conditions A Systematic Review Efrat Neter and Esther Brainin Department of Behavioral Sciences, Ruppin Academic Center, Emeq Hefer, Israel

Abstract: The objective of this paper is to synthesize and update findings from systematic review on health literacy and health outcomes among patients with long-term conditions, and extend the review to the digital domain. Health outcomes include clinical outcomes, processes of care, and health service use. Data sources are the following: (1) studies which appeared in two previous systematic reviews in 2004 and 2011 whose participants were people with long-term conditions or elderly (n = 54); (2) articles on health literacy and health outcomes identified in an updated 2011–2016 search (n = 26); (3) articles on eHealth literacy and its association with health outcomes (n = 8). Strength of evidence was determined by a qualitative assessment of risk of bias, consistency, and directness. There was a lack of consistent evidence on the relationship between health literacy and clinical outcomes despite the consistent evidence on the association with mortality. There was low to insufficient evidence on the association between health literacy and self-rated health/function and emotional states of anxiety and depression, alongside high evidence on lack of association with quality of life. There was insufficient to low evidence on the association between health literacy and behavioral outcomes (medication adherence, other health behaviors) and finally also low to moderate evidence on the association between health literacy and use of health services such as hospitalization and emergency department. In the eHealth literacy domain, there were few studies reporting association with health behaviors and self-rated health with inconsistent results. In conclusion, it is advocated to examine performed heath literacy and eHealth literacy in large longitudinal studies.

Keywords: health literacy, eHealth literacy, health service use, health outcomes, processes of care

Health Literacy and eHealth Literacy – Definition and Measurement Health literacy is defined by the World Health Organization as “the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand and use information in ways which promote and maintain good health” (World Health Organization, 1998, p. 20). A definition by the Institute of Medicine focuses on similar capacities that serve making “appropriate health decisions” (Cutilli, 2007; Parker, Ratzan, & Lurie, 2003). This concept is elaborated by Nutbeam (2000, 2008) as being comprised of three types. The first, functional literacy, involves reading, writing, and basic communication skills that allow functioning effectively in everyday situations. Critical literacy involves critically

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analyzing information and using information to exert greater control over life events and situations. Lastly, interactive literacy comprises of extracting information and deriving meaning from different forms of communication and to apply new information to changing circumstances.

Rudd, Kirsch, and Yamamoto (2004) explicate health tasks that depend on health literacy: The range encompasses activities related to health promotion (e.g., purchase food), health protection (e.g., decide among product options and use products), disease prevention (e.g., undergo screening or diagnostic tests), health care and maintenance (e.g., calculate timing for medicine), and system navigation (e.g., locate facilities or apply for benefits).

Historically, the attending physician was the primary source supplying medical and medication-related information, but nowadays a wider range of information sources

Ó 2019 Hogrefe Publishing

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is available to the public. These information sources include traditional media and electronic media, more specifically, the Internet (Hesse et al., 2005), and the literacy skill necessary to make use of these resources is labeled eHealth literacy. eHealth literacy encompasses basic literacy as well as information, media, health, computer and scientific literacies (the lily model; Norman & Skinner, 2006).

Health literacy is measured through both performance and self-report. Screening tools for clinical settings such as Shortened Test of Functional Health Literacy in Adults (S-TOFHLA; Parker, Baker, Williams, & Nurss, 1995), Rapid Estimate of Adult Literacy in Medicine (REALM; Davis et al., 1993), and Newest Vital Sign (NVS; Weiss et al., 2005) measure performance (Kiechle, Bailey, Hedlund, Viera, & Sheridan, 2015), focusing on domains that are thought to be markers of an individual’s overall capacity (Baker, 2006). Comprehensive measures such as the Health Activity Literacy Scale (HALS; Rudd et al., 2004) that include tasks in various health domains (health promotion, protection, maintenance, disease prevention, system navigation) also exist, yet a recent review on the use of health literacy measures (Mackert, Champlin, Holton, Muñoz, & Damásio, 2014) found low use of these measures and called for the development of measures that can be administered remotely online. Self-report measures that relate both to the above health domains and also to the cognitive skills involved – seeking, understanding (basic literacy and numeracy), evaluating, and applying health information – also exist (e.g., Sørensen et al., 2012; European Health Literacy Scale). eHealth literacy is assessed most often by the self-report measure eHealth Literacy Scale (eHEALS; Norman & Skinner, 2006). It is the only measure used in more than one study (Karnoe & Kayser, 2015). The measure focuses on finding information on the Internet and assessing it. A broader self-report scale that also addresses generating information was just recently developed (Van der Vaart & Drossaert, 2017), while studies on performed eHealth literacy are scarce (Neter & Brainin, 2017; Van der Vaart et al., 2011).

As electronic health resources in many forms (electronic health records, telehealth initiatives, mobile health-promoting applications, interactive health-related social media, and many online health information Websites) are changing many aspects of health care and health promotion, eHealth literacy is becoming increasingly vital in terms of health literacy. Moreover, the increased interaction with these resources, whether with health professionals, peers, or products, calls for evaluating users’ literacy in the digital health domain. Indeed, health literacy and eHealth literacy were found to be moderately associated (r = .36; Neter, Brainin, & Baron-Epel, 2015), sharing the skills of seeking and appraising/applying. Ó 2019 Hogrefe Publishing

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Association Between Health Literacy and eHealth Literacy With Health Outcomes High attention is bestowed on patients’ health literacy because it is recognized as affecting the communication with healthcare providers and patients’ health outcomes (Baker et al., 2007; DeWalt, Dilling, Rosenthal, & Pignone, 2007; Paasche-Orlow & Wolf, 2007; Schillinger et al., 2002; Yin, Dreyer, Foltin, van Schaick, & Mendelsohn, 2007; Zamora & Clingerman, 2011).

Poor health literacy has been reported as associated with various adverse health outcomes: navigation difficulties within the health system, inaccurate or incomplete reports related to medical history, missed doctor appointments, inaccurate use of medications in terms of timing (Baker et al., 2002; Baker, Parker, Williams, & Clark, 1998) or dosage (Baker et al., 1996), decreased rates of adherence to chronic illness regimens (Williams, Baker, Parker, & Nurss, 1998), and increased risk of hospitalization (Baker et al., 1998, 2002).

Health literacy was also found to be associated with functioning in the digital domain, so that low health literacy (and related skills) are negatively related to the ability understand (Zikmund-Fisher, Exe, & Witteman, 2014), evaluate online health information and trust in online health information (Diviani, van den Putte, Giani, & van Weert, 2015).

The findings on the association between health literacy and health outcomes persist after controlling for background characteristics such as socioeconomic status, age, and race (Schillinger et al., 2002), yet it is unclear what is the optimal strategy in adjusting for potential confounders (Bailey et al., 2014). On the one hand, it is argued that overadjustment could produce false-negative results of no association between health literacy and health outcomes when a true relationship actually exists (Bailey et al., 2014). On the other hand, findings on a major confounder such as intelligence indicate that much of the association between health literacy and health outcomes is accounted for by cognitive ability (Mõttus et al., 2014; Serper et al., 2014) to the point of viewing health literacy as a “domain-specific contextualized measure of basic cognitive abilities” (Reeve & Basalik, 2014). eHealth literacy is a more recent construct than health literacy, and much less research has examined its association with health outcomes (Karnoe & Kayser, 2015). Having the composite skills of eHealth literacy allows health consumers not only to increase the availability of health information (Knapp, Madden, Wang, Sloyer, & Shenkman, 2011; Milne et al., 2012; Muñoz, 2010; Neter & Brainin, 2012) but also to achieve positive health outcomes such as perceived or reported better communication with attending physician, enhanced use of medical insurance, health

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behaviors, self-management of health needs, and understanding of the disease/condition (Mitsutake, Shibata, Ishii, & Oka, 2012; Neter & Brainin, 2012).

A large number of reviews have been published on health literacy. Several of these reviews evaluated the association between health literacy and a broad spectrum of healthrelated outcomes, some including all populations (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011; DeWalt, Berkman, Sheridan, Lohr, & Pignone, 2004) and some focusing on a particular segments of patients (caregivers, people with diabetes or cardiovascular diseases), tools or interventions (Bailey et al., 2014; Kiechle et al., 2015; T. W. Lee, Lee, Kim, & Kang, 2012; Taggart et al., 2012; Yuen, Knight, Ricciardelli, & Burney, 2016). The latest comprehensive review (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011) judged the evidence on an association between health literacy and disease prevalence and severity (manifested in various clinical outcomes) as low, though the evidence on the association between health literacy and mortality was judged as high. They also inferred moderate evidence on the (negative) association between health literacy and increased use of health services, and low or insufficient evidence on processes of care as various health behaviors (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011, pp. 101–102).

As health literacy is most critical among people with long-term conditions, there is a special need to focus on them and examine its association with health outcomes across diseases, thus, the present review focuses on patients with long-term conditions and people over 65 years old (assumed to have long-term conditions). This is a population facing complex conditions requiring self-care which relies on either written materials or verbal instructions, both calling for literacy skills. The review includes studies reported in the two comprehensive reviews (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011; DeWalt et al., 2004) as it pertains to patients with long-term conditions and older adults, using these reviews’ quality assessment.

In addition, it includes studies on patients with long-term conditions and older adults published between 2011 and 2016, after these reviews were published. The current review focuses on outcomes most pertinent to patients with long-term conditions that go beyond knowledge and perceptions (e.g., perceived self-efficacy, similar to the review

by Kiechle et al., 2015, p. 1540) to the following three major outcome categories: (1) patients’ outcomes (e.g., health status, functioning, well-being, health-related QoL, emotional states); (2) processes of care (e.g., patient behaviors/actions/adherence, patient–healthcare providers communication); and (3) health service outcomes (e.g., number of contacts with clinicians, hospitalization, emergency department use), viewed as a proxy measure of functional navigation (Paasche-Orlow & Wolf, 2007).

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Only a single review was conducted on the association of eHealth literacy with health outcomes (Karnoe & Kayser, 2015), yet it examined many issues related to eHealth literacy; hence, the present work will conduct a systematic review focusing on health outcomes. Due to the limited amount of work accumulated thus far, community and primary care samples will be also included in the review, and not only patients with long-term conditions.

The aims of the current work are therefore to (1) examine the association between health literacy and patients’ outcomes, processes of care, and health service utilization among patients with long-term conditions and (2) extend this review to the digital domain by examining the association between eHealth literacy and health outcomes, processes of care, and health service utilization, this time in a diverse population.

Methods Materials and Search Procedures Two literature searches were conducted. The first focused on empirical articles on health literacy and outcomes published between the years 2011 and 2016 and the second one on empirical articles on eHealth literacy and health outcomes published since 2000.

A systematic search of peer-reviewed English-written empirical papers published between January 2011 and December 2016 was conducted in the following databases: CINHAL, Medline (including Cochrane Database of Systematic Reviews), PsycNet, ScienceDirect, Web of Science, and Wiley. A combination of two groups of key terms was used: health literacy and health outcomes. Additionally, manual searches of the reference lists were conducted.

The search resulted in 206 papers. Two reviewers (EN/ NE) independently screened identified abstracts.

The systematic search of peer-reviewed English-written empirical papers on eHealth literacy and outcomes focused on papers published between January 2000 and December 2016. It was conducted on the following databases: CINHAL, Medline (including Cochrane Database of Systematic Reviews), PsycNet, ScienceDirect, Web of Science, and Wiley. We applied combinations of two groups of keywords: eHealth literacy (or digital health literacy) and outcomes.

Additionally, manual searches of the reference lists were conducted. The search resulted in 130 papers. Two reviewers (EN/GT) independently screened identified abstracts.

Lastly, articles appearing in the 2004 and 2011 comprehensive reviews on health literacy and health outcomes (Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011; DeWalt et al., 2004) were all reviewed. Ó 2019 Hogrefe Publishing

Screening

Identification

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Records identified through database searching (n = 206)

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Record excluded (n = 141) Reasons for exclusion: -Not chronic patient (n = 44)

Records after duplicates removed (total: n = 164)

-Not Health Literacy (n = 28) -Not empirical (n = 48)

Included

Eligibility

-Not English language (n = 2) -Not quantitative (n = 4) Records assessed for eligibility (n = 164)

Studies included in review (n = 23)

-Not Health Outcome (n = 15)

Reference lists screened and 3 additional studies included

Articles eligible for inclusion N = 26 Figure 1. PRISMA 2009 flow diagram for health literacy.

Lastly, articles appearing in the 2004 and 2011 comprehensive reviews on health literacy and health outcomes (Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011; DeWalt et al., 2004) were all reviewed.

Inclusion Criteria, Exclusion Criteria, and Data Abstraction In the health literacy search, we (EN/NE) first selected publications which appeared in peer-reviewed journals (excluding dissertations, book chapters, qualitative studies, validation studies, narrative review articles, case reports, editorials, and letters). We then screened for empirical publications (1) which examined such outcomes as patients’ health-related outcomes, processes of care, and healthcare services use and (2) which were conducted on samples of patients with long-term conditions or older adults (Figure 1).

Of the 164 identified studies (see Figure 1), 141 were excluded (44 for not including patients with long-term conditions, 28 on not being on health literacy, 48 on not being empirical, 2 for being written in a language other than English, 4 for not being quantitative, and 15 for not including a health outcome; Moher, Liberati, Tetzlaff, & Altman, Ó 2019 Hogrefe Publishing

2009). In this stage, 23 articles meeting inclusion criteria were selected. Additional three articles were identified through review of the reference lists of the included articles; articles which did not employ multivariate analyses controlling for sociodemographic confounders were not included. There was an initial agreement between raters in 151 out of 164 studies (92% agreement). Discrepancies were resolved by a consensus method. Descriptive data were extracted by one researcher (EN) and then verified by the second researcher (NE).

The same procedure was applied in the eHealth literacy search except that samples from community and primary care settings were also included and statistical analyses were not inclusion criteria. Of the 130 identified studies (see Figure 2), 26 were duplicates, resulting in 104 screened studies. All 104 titles were screened, resulting in 56 studies whose abstracts were read. Reasons for exclusion of title or abstract were as follows: Twenty-nine on not being on eHealth literacy, 35 on not being empirical, 13 for not being quantitative, and 20 for not including a health outcome. In this stage, 7 articles meeting inclusion criteria were selected. One additional study was identified through the reference list of a review (Karnoe & Kayser, 2015).

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Identification

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Records identified through database searching (n = 130)

Screening

Record excluded (n = 97) Reasons for exclusion: Records after duplicates removed (total: n = 104)

-Not eHealth Literacy (n = 29) -Not empirical (n = 35)

Included

Eligibility

-Not quantitative (n = 13) Records assessed for eligibility (n = 104)

-Not Health Outcome (n = 20)

Studies included in review (n = 7)

Reference lists screened and 1 additional study included

Articles eligible for inclusion N = 8 Figure 2. PRISMA 2009 flow diagram for eHealth literacy.

Coding, Data Synthesis, Data Analysis, and Quality Assessment Procedures Findings on health literacy and outcomes are presented using three major outcome categories presented above: patients’ outcomes, processes of care, and health service outcomes. Studies which reported outcomes in more than one category are presented more than once, but their N was counted only once. The N of articles presenting results based on one study was counted once. Quality assessment of studies which appeared in the previous reviews was extracted (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011).

The data synthesis of the three outcome variables is presented in the Electronic Supplementary Material, ESM 1.

Overall strength of the evidence for each outcome was qualitatively assessed using the Agency for Healthcare Research and Quality (AHRQ) guidance, by grading the strength of evidence as high, moderate, low, or insufficient on the basis of the potential risk of bias of included studies, consistency of effect across studies, directness of the evidence, and precision of the estimate (Owens et al., 2010).

Risk of bias is assessed through two main elements: study design (RCT, observational; cross-sectional, prospective) and aggregate qualities, which in the current review relied European Psychologist (2019), 24(1), 68–81

either on previous assessments of quality conducted in the previous reviews or on assessments conducted by the authors (EN and either NE or GT). Consistency is assessed by having the same direction of effect, the same sign of effect size, and narrow range of effect size; in the current review, it was assessed by the first parameter, as effect sizes were not computed. Directness refers to measuring the (ultimate) outcome of interest (i.e., direct) or alternatively measuring surrogate outcomes (i.e., indirect) or conducting indirect comparisons; in the current review, it addressed whether an ultimate outcome was measured. The last criterion, precision, refers to certainty surrounding the effect estimate, and as such an effect was not computed in the current review, this criteria was not employed. Thus, risk of bias, consistency, and directness were used in judging the overall strength of the evidence for each outcome.

Results Description of Analyzed Studies The health literacy analysis included data from 80 original studies: 9 from the 2004 review, 45 from the 2011 review, Ó 2019 Hogrefe Publishing

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and 26 from the updated search, encompassing 65,065 participants (some of the studies are reported in several articles). The majority of studies were based in the USA; other original studies took place in Asia and Europe. The measures used in the studies were mostly S-TOFHLA (n = 26) and TOFHLA (n = 13), followed by the REALM (n = 26). Few studies used self-reported items or self-constructed scales. Nearly all studies assessed functional literacy and controlled for background variables. The eHealth literacy analysis included only 8 studies published mostly in Asia and North America. The earliest study examining such associations was from 2011. The studies encompassed 5,076 participants. A list of all articles included in the review appears in ESM 2.

Health Literacy and Health Outcomes The outcomes are grouped into three main categories: patients’ outcomes, processes of care outcomes, and health service outcomes.

Patients’ outcomes included diverse outcomes such as clinical (biochemical/biometric indicators), self-rated health and function, quality of life, and emotional condition.

The clinical indicators were diverse, including CD4 count and viral load in the case of HIV, glycemic control among patients with diabetes, cancer stage, blood pressure (among patients with hypertension, diabetes, or HIV), anticoagulation control, infections and kidney function among patients with renal disease and complications following surgery. The most prevalent clinical indicator was glycemic control. Most studies (with few exceptions; Mancuso, 2010; Schillinger et al., 2002) adjusted for background variables (quality ranged from fair to good), and this feature did not distinguish between studies that found an association to those which did not report an association between health literacy and the clinical indicator. Most were cross-sectional (n = 21) or retrospective (n = 3), but several (n = 7) were prospective. Overall, evidence on the relationship between health literacy and clinical indicators was inconsistent across studies (association in 13 studies out of 31), and the heterogeneity did not permit the estimation of an effect.

Studies did not differ in directness (all used direct assessments of outcomes). Therefore, this evidence was rated insufficient.

The evidence on perceived function and health was somewhat more consistent: 17 studies measured either self-rated health, function, limitations, or morbidity; 12 reported a positive association (with health and function) or negative (with limitations and morbidity), whereas 5 reported no association. All the latter were assessed as fair quality, whereas studies that reported an association were either of fair or of good quality. Again, studies did not differ in Ó 2019 Hogrefe Publishing

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directness, all measuring the outcomes of interest directly.

Due to lack of consistency and prevalent use of cross-sectional design, this evidence on association between health literacy and perceived function/health was rated as low to insufficient.

The evidence on the association between health literacy and quality of life was unequivocally consistent: no association. Most studies were cross-sectional (n = 5), while two were prospective. The evidence on the association between health literacy and emotional states was less consistent: 7 of the 10 studies reported negative association, while only two found no association and one found a positive unexpected association. Again, there was no relation to quality as most studies controlled for confounds and studies did not differ in directness, all measuring the outcome of interest. The evidence was judged to be low to insufficient due to its inconsistency and high prevalence of cross-sectional design.

The evidence on mortality was consistent and based on four good prospective studies including several thousand participants, all reporting a negative association between health literacy and mortality. The strength of the evidence was thus judged to be high, based on low risk for bias (i.e., design, number of participants in studies) and consistency.

Processes of care included behavioral outcomes. The most prevalent behavior studied was medication adherence (n = 20), with most studies being cross-sectional (n = 14) or retrospective (n = 2) and few prospective (n = 4), while all studies controlled for background variables and did not differ in directness. Most studies (n = 14) found a positive association between health literacy and medication management/adherence, with three of the four prospective studies reporting such an association. No studies reported negative association. Due to the high risk for bias embodied in the design and relatively high consistency, the evidence was rated as low.

Similar consistency was found in other diverse healthrelated behaviors such as physical exercise, vaccinations, cancer screening, self-care, appointment keeping, or using an inhaler. The studies were in diverse patient populations: patients with diabetes, asthma, CAD, transplantation, and elderly people. Ten studies reported on a positive association between health literacy and uptake of at least one health-promoting behavior, while five studies reported no association. Again, all studies conducted multivariate analyses where confounds were taken into account and did not differ in directness (all assessed direct outcomes). All the studies that reported no association were of fair quality, and four of them were cross-sectional, while there were three good studies (among the 10) which found a positive association. Here, too, the evidence on the association was rated as low to insufficient in strength.

Health service outcomes included mostly use of emergency department and hospitalization. Among the twelve studies, European Psychologist (2019), 24(1), 68–81

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three were judged as good and all three reported a negative association between health literacy and health service use; all the studies reporting no association (n = 3) were of fair quality, and overall eight of the twelve studies reported a negative association between health literacy and services use, so that adequate literate patients consumed less services. All studies measured the outcome directly. Due to the inconsistency and risk of bias in the design (high prevalence of cross-sectional), the evidence was judged as low to moderate in strength.

eHealth Literacy and Health Outcomes Studies on eHealth literacy have for the most part (two exceptions) used the eHEALS measure and were cross-sectional (see also Karnoe & Kayser’s review, 2015). Quality assessment of the studies was evaluated using four criteria: study’s N and the representativeness of the sample [representative sample (= 4), N 500 (= 3), 250 > N < 500 (= 2), N < 250 (= 1)]; use of theoretical framework (yes = 1, no = 0), the measure being used (4 = performance, 3 = validates self-report, 1 = proxy (use) or 1-item self-report); and the design (3 = prospective or experimental, 1 = cross-sectional, 1 = multivariate analysis, could be added). Overall quality ranged from 0 to 11, with 1–5 = poor, 6–9 = fair quality, and 10–11 = good quality. There was inter-rater agreement in 30 out of the 32 evaluations (93.8%). A Cohen’s κ calculated on the overall quality score (categories: poor, fair, and good, 8 agreements out of 8 in all studies) was 1.00, p < .005 with a standard error of 0.00. Of the eight studies examining association of eHealth literacy with a health outcome, only two studies surveyed patients with long-term conditions (lung cancer survivors, HIV) while the rest examined college student and community adults.

The health outcome was most often health behaviors (n = 6); in four of the studies, high eHealth literacy was found to be associated with health-promoting behaviors, while in one study, it was found to be positively associated with health-compromising behavior (risk behaviors related to HIV), and in one study, it was found as not associated with the health behavior (HPV vaccination). Self-rated health was examined in two studies and found not associated with health literacy in both cases. The strength of the evidence on both findings was judged to be moderate to low: On the one hand, the findings exhibited relative consistency and high directness (i.e., measuring the ultimate outcome of interest, albeit through self-reports), while on the other hand, there was a considerable risk of bias (expressed in small amount of studies, nonrepresentative samples, small Ns, and cross-sectional design) (Table 1).

A complete list of studies used in this review is available in ESM 2.

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Discussion The present review focused on patients with long-term conditions who continuously have to manage their health and for whom health literacy can be a critical resource. The review concentrated mostly on concrete outcomes – clinical indicators, behaviors, healthcare use, while also including patients’ outcomes such as perceived health and function, quality of life, and emotional conditions. The health outcomes associated with health literacy – spanning from emotional responses and quality of life, to clinical markers and to morbidity and mortality – present a complex and intriguing picture.

Summary of Findings and Theoretical Frameworks The only two domains with high strength of evidence concerned quality of life and mortality; in the former, there was consistently no association with health literacy, and in the latter, there was a consistent negative association. There was insufficient to low strength of evidence regarding all the other outcomes. Clinical outcomes were clearly with high heterogeneity, barring an inference on the state of the evidence beside one of insufficient evidence. Other outcomes – emotional conditions, perceived health and function, behaviors, and health service use – though with less heterogeneity, did not present a consistent direction in the association and exhibited high risk of bias mainly due to prevalence of cross-sectional design, in spite of the high directedness (Owens et al., 2010).

These findings mostly reiterate those of previous reviews (Al Sayah, Majumdar, Williams, Robertson, & Johnson, 2013; Bailey et al., 2014; Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011). For example, the Berkman, Sheridan, Donahue, Halpern, and Crotty (2011) review, which examined studies across many populations, and the review of Al Sayah et al. (2013), which focused on people with diabetes, both also reported inconsistent results in the association between health literacy and clinical outcomes, leading the authors to conclude that the evidence on these outcomes is insufficient (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011, p. 103) or weak (Al Sayah et al., 2013). The evidence of health behaviors was judged by these previous reviews to be low to insufficient, just as our evaluation indicated. They concluded the evidence on healthcare utilization to be moderate, whereas we concluded the evidence to be low to moderate; the difference in the evaluation stemmed probably from addition of studies that also added inconsistency in the direction of the results. In summary, the current review then further replicates previous reviews: The findings are similar among long-term patients across different diseases. Ó 2019 Hogrefe Publishing

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Table 1. Overview of eHealth Literacy studies Study, year (Reference)

eHealth literacy instrument

Study sample

Design and grade Outcome and association

(Blackstock et al., 2016)

eHEALS

Women (n = 63); US

Cross-sectional: Fair

(Britt, Collins, Wilson, eHEALS Linnemeier, & Englebert, 2015)

College students (n = 396), Cross-sectional: US Fair

Variables used in multivariate analysis

HIV transmission SRH risk Age, income, SRH behaviors; positively associated Health behavior: HPV – vaccination, no association

(Hsu, Chiang, & Yang, eHLS1 2014)

College students (n = 525), Cross-sectional: Taiwan Fair

(Milne et al., 2012)

eHEALS

(Mitsutake et al., 2012)

eHEALS

Lung cancer survivors (n = 83), Canada General adult sample (n = 2,970), Japan

Cross-sectional: Poor Cross-sectional: Fair

(Mitsutake, Shibata, Ishii, & Oka, 2016)

eHEALS

General adult sample (n = 2,115), Japan

Cross-sectional: Fair

(Neter & Brainin, 2012)

eHEALS

General adult sample (n = 1,289), Israel

Cross-sectional: Fair

(Xie, 2011)

Tasks

Older adults (n = 146), US Experimental: Fair Reported self-care, increased following instruction

Health behaviors; PA, nutrition and sleep are positively associated Quality of life, SRH; no association Colorectal cancer knowledge, screening behavior; positive association Health behaviors; PA, nutrition are positively associated SRH, no association

Perceived control as mediator to intentions Health status, health concerns – Age, marital status, education

Socio-economic, Internet use frequency – –

Note. eHEALS = eHealth Literacy Scale; SRH = Self-Rated Health; PA = Physical Activity.

Still, this pattern of results presents an obvious question: How is health literacy associated with mortality in a consistent manner yet not associated consistently with intermediate health outcomes, be they clinical, behavioral, emotional, or perceptual?

The answer seems to be initially methodological and then leading to theoretical conceptualization. Earlier reviews (Al Sayah et al., 2013; Bailey et al., 2014; Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011) and many empirical studies grappled with the issue of confounding factors or misestimating the relationship between health literacy and outcomes. These reviewers present and sometimes demonstrate (Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011) the conflict as one between addressing confounders, on the one hand, to overadjustment by including other variables than background characteristics (e.g., self-care, treatment regimen, health status), thus producing possible false-negative results of no association between health literacy and health outcomes. Note that there were hardly findings in an opposite direction: The heterogeneity is between no association and an association in one direction (positive or negative, depending on the outcome).

Overadjustment not only masks findings, but it also creates a theoretical chaos: Variables that could be conceptualized of as mediators or moderators are analyzed as confounders. A recent review on health literacy and health Ó 2019 Hogrefe Publishing

outcomes among patients with diabetes presented a framework incorporating sociodemographic determinants of health literacy, moderators such as social-cognitive factors and processes of care and clinical health outcomes (Bailey et al., 2014). The authors also noted that most of the mediators and moderators were understudied. It seems that the community of researchers in the health literacy domain needs to move beyond variables and constructs to an overall framework or multilevel theory, possibly similar to WHO’s five dimensions of adherence (Sabate, 2003).

Though conceptual models on determinants and outcomes of health literacy were proposed (Paasche-Orlow & Wolf, 2007), the field did not seem to adopt them by examining the models in a longitudinal design that affords inferring about pathways. It is clear that the domain of health literacy could greatly benefit from inclusion of health literacy performance assessment tools in large longitudinal studies, e.g., longitudinal studies on midlife and aging such as Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org), English Longitudinal Study of Aging (ELSA, http://www.elsa-project.ac.uk), Midlife in the United States: A National Longitudinal Study of Health and Well-being (MIDUS, http://midus.wisc.edu).

The few studies on eHealth literacy and health outcomes do not allow inference. The field is clearly young. The paucity of studies on eHealth literacy and health outcomes compelled including all samples and not only those European Psychologist (2019), 24(1), 68–81

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focusing on patients with long-term conditions. The eHealth field clearly needs to produce more studies examining whether the digital promise in the health domain is being realized in terms of better outcomes for those who are more skilled in using the Internet for health purposes.

Concurrently, the field needs, just as the field of health literacy, a conceptual framework (Norgaard et al., 2015).

It should be noted that both health literacy and eHealth literacy were measured almost exclusively by validated tools, a finding reiterating recent reviews of health literacy measurement (Nguyen, Paasche-Orlow, & McCormack, 2017) and on eHealth literacy measures (Karnoe & Kayser, 2015).

Limitations and Summary This review harbors several limitations. First, searches were limited to articles published in English. Second, the authors used the search term “health literacy,” assuming it is a widely used and accepted term rather than using all the search terms associated with the official query (mostly the names of the scales, e.g., “test of functional health literacy” or “rapid estimate of adult literacy”). Third, the findings on health literacy were briefly reported, taking into account the detailed description in previous publication (Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011). Fourth, the current review did not include in the review the control groups of RCT intervention studies; this could have added more studies, yet the decision was similar to other reviews that examined these studies separately (see Berkman, Sheridan, Donahue, Halpern, Viera, et al., 2011). An additional limitation is that few evidence concerning oral health literacy (speaking and listening skills) and outcomes exit and could be incorporated in a review. Lastly, the authors, similar to other systematic reviews (Bailey et al., 2014; Berkman, Sheridan, Donahue, Halpern, & Crotty, 2011; DeWalt et al., 2004; Kiechle et al., 2015; Taggart et al., 2012; Yuen et al., 2016), refrained from conducting a meta-analysis due to considerable heterogeneity in the samples, diseases, measures, and outcomes. Future studies could focus on either a disease or a population (e.g., caregivers) and conduct a quantitative analysis.

Despite these limitations, this review adds to the literature as it summarizes findings across different long-term conditions on a wide range of outcomes and its interpretation focuses on the conceptual level. Implications from this review go beyond a call to clinicians and educators to provide easily understood information and reduce complexity; the call is for an ecological model to be tested in longitudinal designs, or even better include the tools of health literacy and eHealth literacy (preferably performed and not self-reports) in already running longitudinal studies in the health domain.

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Electronic Supplementary Materials The electronic supplementary material is available with the online version of the article at https://doi.org/10.1027/ 1016-9040/a000350 ESM 1. Table (.pdf) Synthesis of reviews (2004, 2011) and new literature search (2011–2016) on health literacy and outcome among patients with long-term conditions.

ESM 2. Text (.pdf) List of studies included in the review.

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History Received May 13, 2017 Revision received April 11, 2018 Accepted April 19, 2018 Published online February 11, 2019 Funding This study has been supported by the Ministry of Science and Technology, Israel to Efrat Neter (http://dx.doi.org/10.13039/ 501100006245, 3-10840).

Efrat Neter Department of Behavioral Sciences Ruppin Academic Center Beit 3 Emeq Hefer Israel neter@ruppin.ac.il

Efrat Neter (PhD) is a behavioral scientist, working at the Behavioral Sciences Department at Ruppin Academic Center. Her research interests are primarily: (1) health behavior – antecedents, decision-making, promotion, and change (including RCTs), and particularly attempting to translate laboratory findings into largescale population interventions; (2) health information search on the Internet and particularly eHealth literacy; and (3) self-management of chronic medical conditions such as multiple sclerosis, where she studies self-care behaviors and medication adherence.

Esther Brainin (PhD) is a senior lecturer at the Department of Behavioral Sciences at Ruppin Academic Center in Israel. Brainin explores a wide range of topics linked to how information and communication technologies (ICT) lead to social change. For the past 20 years, she has conducted both empirical and theoretical studies in the intersection of technology and society, the digital divide and partial usage of the Internet, and the relationship between Internet usage, social stratification, and individuals’ empowerment.

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Special Issue: Adjustment to Chronic Illness Original Articles and Reviews

Supporting Adherence to Medicines for Long-Term Conditions A Perceptions and Practicalities Approach Based on an Extended Common-Sense Model Rob Horne, Vanessa Cooper, Vari Wileman, and Amy Chan Centre of Behavioral Medicine, Department of Practice and Policy, UCL School of Pharmacy, London, UK

Abstract: Pharmaceutical prescriptions are core to the treatment of most chronic illnesses, yet only half are taken as prescribed. Despite the high costs of nonadherence to individuals and society, effective adherence-promoting interventions are elusive. This is partly due to the sheer complicity of the issue. There are numerous determinants of adherence, both internal to the patient (intrinsic) and external (extrinsic, e.g., environmental or health system-related factors). Also, the relative importance of these determinants varies between individuals and even within the same individual over time and across treatments, presenting a challenge for intervention design. One complication is that interventions can target several levels: (1) patient (e.g., enhancing motivation and/or ability to adhere), (2) patient-provider interactions (e.g., improving communication and the prescribing process), and (3) the healthcare system (e.g., providing the opportunity to access medication through regulatory approval and co-payment schemes). Here, we focus on level 1: the patient. Although environmental factors are important, the effect of an intervention designed to change them will depend on how they impact on the individual. We describe the Perceptions and Practicalities Approach (PAPA), a pragmatic framework positing that adherence/nonadherence is essentially a produce of individual motivation and ability. Adherence interventions, targeted at any level, will therefore be more effective if tailored to address the perceptions and practicalities underpinning individual motivation and ability. We discuss how PAPA can be operationalized, including the application of theoretical models of illness and treatment representation (Necessity-Concerns Framework and Leventhal’s Common-Sense Model) to address salient adherence-related perceptions.

Keywords: adherence, Perceptions and Practicalities Approach (PAPA), Necessity-Concerns Framework (NCF), interventions, Common-Sense Model (CSM)

Nonadherence to Medication in Chronic Illness: The Need for More Effective Interventions The prescription of a medicine is one of the most common interventions in developed health economies (Kantor, Rehm, Haas, Chan, & Giovannucci, 2015) and is essential to the treatment of most chronic illnesses (Simpson et al., 2006). However, treatment adherence rates are highly variable, ranging from 0% to over 100% (Nieuwlaat et al., 2014), depending on the condition, therapy, and methods used to measure adherence. The World Health Organization (WHO) estimates that, on average, only half of prescriptions for long-term conditions are taken as advised (Sabaté, 2003). If the prescription was appropriate, this may represent a lost opportunity for health gain and increased financial costs resulting from unnecessary escalations of treatment (DiMatteo, Giordani, Lepper, & Croghan, European Psychologist (2019), 24(1), 82–96 https://doi.org/10.1027/1016-9040/a000353

2002; Sabaté, 2003). The complex comorbidities that accompany many long-term conditions, with their associated polypharmacy and complicated medication regimes, further exacerbate the problem of nonadherence (Murray & Kroenke, 2001).

Despite the costs of nonadherence to individuals and society, effective adherence-promoting interventions remain elusive. A series of systematic reviews have examined the efficacy of medication adherence interventions.

These began with a review of 13 randomized controlled trials (RCTs) in 1996 (Haynes, McKibbon, & Kanani, 1996) and culminated in a recent Cochrane review which included 46,962 participants in 182 published RCTs across 24 conditions (Nieuwlaat et al., 2014). Although the number of RCTs investigating interventions has increased substantially in the last two decades, the findings have changed little. Each systematic review has concluded that while adherence can be improved, most interventions have limited effectiveness (Nieuwlaat et al., 2014). Ó 2019 Hogrefe Publishing

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Adherence is difficult to quantify. Adherence measures vary across studies, making comparisons difficult, and measures differ in accuracy and reliability (Farmer, 1999; Horne & Clatworthy, 2010). Moreover, three stages in the medication-taking process can be delineated: initiation, execution, and persistence (Vrijens et al., 2012). Different assessment methods may be more applicable for one stage than another (e.g., prescription collection rates may be better at identifying non-persistence than nonadherence relating to poor execution), with each method having its own strengths and weaknesses (Farmer, 1999), yet accurate adherence monitoring is key for intervention development.

A further limitation of the current literature is that few interventions are described in sufficient detail to explain why some worked and others did not, or to reliably identify the active components of effective interventions (Hoffmann et al., 2014; Horne et al., 2005).

The Need for a Pragmatic Framework for Adherence Support A key challenge for the development of adherencepromoting interventions is the sheer complexity of the issue (Nieuwlaat et al., 2014). There are numerous determinants of adherence, both internal to the patient (intrinsic) and external (extrinsic, e.g., environmental or health systemrelated factors). Also, the relative importance of these determinants varies between individuals and even within the same individual over time and across treatments, presenting a challenge for intervention design. One complication is that interventions can target several levels: (1) patient (e.g., enhancing motivation and/or ability to adhere), (2) patient-provider interactions (e.g., improving communication and the prescribing process), and (3) the healthcare system (e.g., providing the opportunity to access medication through regulatory approval and co-payment schemes; Horne et al., 2005) (Figure 1).

In 2009 and 2016, National Institute for Health and Care Excellence (NICE) published guideline CG76 Medicines adherence: involving patients in decisions about prescribed medicines and supporting adherence. After a detailed review, NICE concluded that was no definitive gold standard intervention and that much could be done to improve the quality of support delivered to individuals. The development of their pragmatic guidelines drew on a review of compliance adherence and concordance in medicine taking, commissioned by the UK National Institute for Health Research (NIHR) (Horne et al., 2005) which had applied the Perceptions and Practicalities Approach (PAPA) as a framework Ó 2019 Hogrefe Publishing

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Healthcare System Policy & Practice Patient-Provider Interactions

Patient

INTERVENTIONS

Figure 1. Adherence interventions “lollipop” – multi-level targets for intervention (Horne et al., 2005).

for understanding nonadherence form the patients’ perspectives and for apprising adherence interventions. The NICE guidelines recommended the application of the PAPA advocating that addressing nonadherence should start with “an exploration of patients’ perspectives of medicines and the reasons why they may not want or are unable to use them and that to understand adherence to treatment we need to consider the perceptual factors (e.g., beliefs and preferences) that influence motivation to start and continue with treatment, as well as the practical factors that influence patients’ ability to adhere to the agreed treatment” (Nunes et al., 2009, p. 5).

In this paper, we outline the key feature of the PAPA. We will argue that to understand and address adherence, we should start with the patient and question how the treatment and, any interventions to support its use, impacts on their motivation and ability to follow the agreed treatment recommendations. Healthcare professionals have a duty to help patients make informed decisions about treatment and use appropriately prescribed medicines to best effect (Horne & Weinman, 2004). The NICE guidelines embody this principle stating that addressing nonadherence is not about getting patients to take more medicines per se.

Rather, it starts with an exploration of patients’ perspectives of medicines and the reasons why they may not want or are unable to use them.

We will review research investigating patient perceptions of medication with particular emphasis on “commonsense” representations of medication, and the illnesses they are prescribed for. We will suggest theoretical frameworks for conceptualizing the beliefs about medication (Necessity-Concerns Framework) and illness (Leventhal’s components of illness representation) that are likely to be particularly salient for adherence/nonadherence and discuss how they can be applied in an extended CommonSense Model.

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The Perceptions and Practicalities Approach (PAPA) PAPA (Horne, 2001; Horne et al., 2005) offers a simple framework to guide the development and appraisal of adherence-promoting interventions. It attempts to specify the “minimum ingredients” of adherence support targeted to the needs of the patient. The PAPA focuses on how the individual interacts with their treatment and derives from the basic premise that two key attributes that are considered essential for adherence: motivation and ability. It presumes that, although a wide variety of intrinsic factors (e.g., depression and anxiety; DiMatteo, Lepper, & Croghan, 2000) and extrinsic factors (e.g., environmental opportunities and constraints) are relevant, their effect on adherence is likely to manifest through enhanced or reduced motivation and/or ability.

Motivation and Ability: Essential Intrinsic Attributes for Adherence It is widely recognized that nonadherence may be both intentional (e.g., when we decide not to take the treatment or to take it in a way which differs from recommendations) and/or unintentional (e.g., when we want to follow the recommendations but are prevented from doing so by barriers that are beyond our control). If we begin with the individual, the minimum requirement for explaining variation in adherence/nonadherence is the interaction between two factors: motivation and ability (Horne et al., 2005). Motivation may arise from conscious decision-making processes but also more instinctive, intuitive processes (Kahneman, 2011). Likewise, a range of factors influence ability (e.g., knowledge and physical capability to administer the dosage form on time) (Horne et al., 2005; Piette, Heisler, Horne, & Alexander, 2006). These two aspects of motivation and ability, which are driven by intentional and unintentional processes, form the fundamental basis of the PAPA framework (Figure 2).

The division between intentional and unintentional processes may be blurred, with degree of overlap between motivation and ability represented in Figure 2 by the overlapping circles. For example, we may be more inclined to forget to take a medicine that we perceive to be unimportant than one that we perceive to be essential. Likewise, the provision of training on the best technique for administration of asthma medication via an inhaler was more effective in those who were more motivated to practice the technique (Ovchinikova, Smith, & Bosnic-Anticevich, 2011). Moreover, an intervention designed to improve patients’ ability to adhere by simplifying the regimen (and making it easier to take) might also increase motivation.

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The concepts of motivation and ability leading to action (in this case, action to take a medication) need not precede one another in succession. While in most cases, increasing motivation and/or ability can lead to an increase in action, in some cases, beginning the action itself (i.e., taking the medication) can lead to increases in motivation or ability.

For example, if a patient feels better after taking the medication, this may lead to increased motivation to continue to take the treatment.

The value in the intentional–unintentional, motivation– ability constructs is not in the fact that these are completely distinct and separate categories. Rather, it is in presenting a basic aide-memoir of the fundamental ingredients of adherence support. If we are designing or evaluating an intervention to change behavior, a basic starting point is to consider the recipient of the interventions and question whether it addresses both perceptions and practicalities and motivation and ability. The PAPA framework is therefore not intended as comprehensive theory. There are clearly many other factors that need to be considered if we are trying to map the antecedents of adherence and nonadherence.

Rather, the two overlapping circles suggest the “core” components of adherence support. This might serve as a foundation for more comprehensive modeling of determinants as discussed previously (Horne, 2006; Horne et al., 2005) and later in this paper.

Opportunity and Triggers Motivation and ability, which are usually thought of as intrinsic qualities, may also be influenced by extrinsic factors that make the behavior possible or prompt it. This was recognized in the “Opportunity” construct proposed by Ölander and Thøgersen in their motivation–opportunity– abilities (MOA) model (Thøgersen, 1995) and by Michie and colleagues in their COM-B model, where the behavioral determinants are labeled motivation, opportunity, and capability (Michie, van Stralen, & West, 2011).

Similarly, in Fogg’s behavioral model, the importance of external prompts or calls to action, such as the sounding of an alarm, is emphasized (Fogg, 2009). Fogg replaces the more generic opportunity construct proposed by Ölander and Thøgersen, with “Triggers” in the Fogg Behavioral Model (FBM) which incorporates motivation, ability, and triggers (Fogg, 2009). There is clearly considerable overlap between these constructs (Jackson, Eliasson, Barber, & Weinman, 2014).

Providing treatment opportunities (e.g., by making a medicine available or providing free access to it) might also enhance motivation, if the treatment is valued by the patient (Taira, Wong, Frech-Tamas, & Chung, 2006). Triggers may be external or internal and influence both motivation and ability to adhere. External triggers such as a text reminder Ó 2019 Hogrefe Publishing

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Intentional

Unintentional

processes

processes

MOTIVATION Perceptions e.g. Beliefs, emotions, and preferences

ABILITY Practicalities e.g. Capability and resource limitations

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Triggers [e.g. External / Internal cues / Prompts to action]

Unintentional

Intentional processes

processes

MOTIVATION

ABILITY Practicalities

Perceptions e.g. Beliefs, emotions, and preferences

Figure 2. Perceptions and Practicalities Approach (PAPA): motivation and ability as core intrinsic attributes for adherence.

e.g. Capability and resource limitations

Opportunity [e.g. Access to treatment through prescription / co-payments etc.]

E

could improve ability to take the treatment on time and motivate behavior to do so (Petrie, Perry, Broadbent, & Weinman, 2012). Conversely, an unwanted reminder might reduce our motivation to adhere if we are irritated by it (Mannheimer et al., 2006). Internal triggers such as symptom experiences might motivate adherence if the individual believes taking the medication will help relieve the symptoms (Figure 3).

Interventions to support adherence, especially to longterm treatments, are likely to be effective if they impact on both the specific perceptions (e.g., beliefs and emotions) and practicalities (e.g., capacity, resources, and opportunities) affecting the individual’s motivation and ability to start and continue with treatment (Horne et al., 2005). Both perceptions and practicalities can be moderated by external opportunities and triggers. However, a fundamental requirement of patient-centered adherence support is that it considers both the perceptions and practicalities linked to individual motivation and ability to start and continue with the agreed treatment plan.

Addressing motivation and ability through perceptions and practicalities may also a prerequisite to the formation of a (healthy) habit where taking medication becomes part of the patient’s routine. In this respect, we might envisage a logical flow of motivation–ability–habit.

Before describing the PAPA approach to adherence support, it is important to consider perceptions that are central to PAPA. Interventions that attend to the practicalities of adherence (e.g., by making the medicine readily available or by providing prompts or reminders) are unlikely to be effective if the patient has already made an intentional decision not to take their prescribed medication because of their beliefs. Understanding how we make decisions about treatments is key to the PAPA adherence framework.

From Overarching Frameworks to Specific Content Explaining variation in health-related behaviors has been a core aim for theory in health psychology (Conn, Enriquez, Ó 2019 Hogrefe Publishing

Figure 3. Perceptions and Practicalities incorporating Triggers and Opportunity.

Ruppar, & Chan, 2016; Holmes, Hughes, & Morrison, 2014). Most theories share the common assumption that the motivation to engage in and maintain health-related behaviors arises from beliefs that influence the interpretation of information and experiences and guide behavior (Conner & Norman, 1996; Horne & Weinman, 1998b).

The capacity of theoretical models to explain variance in adherence and other behaviors is, however, determined by the validity of the model and whether it contains the right constructs. It is also influenced by the way in which the constructs are operationalized. In social cognition models, the antecedents of behavior are specified at the process level (e.g., attitudes inform intentions, which influence behavior; Ajzen, 1991).

When theoretical models are used to develop interventions, they are likely to be more effective if they specify content (e.g., the beliefs that contribute to the positive or negative evaluations that constitute the attitude toward the behavior) as well as process (Marteau, Dieppe, Foy, Kinmonth, & Schneiderman, 2006; Noar & Zimmerman, 2005; Painter, Borba, Hynes, Mays, & Glanz, 2008).

This is recognized within Leventhal’s Common-Sense Model of self-regulation (CSM) where the content, as well as the process of illness representations, is specified (Leventhal et al., 1992). However, theoretical models of behavior are likely to be more explanatory when their content becomes more specific to the behavior in question (Francis, O’Connor, & Curran, 2012). Although representations of coping procedures (e.g., taking treatment in response to an illness threat) are implicit within Leventhal’s CSM, a more explicit consideration of perceptions of treatment is necessary when the model is applied to adherence (Aujla et al., 2016; Horne, 2003). A consideration of how we evaluate prescribed treatments and how these evaluations relate to the self-regulation of illness is central to the PAPA framework.

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Making Decisions About Taking Medicines: The Necessity-Concerns Framework Studies of adherence to prescribed medicines consistently link nonadherence to patients’ treatment beliefs, and in particular, how necessary the patient believes their medicine to be for their illness (necessity beliefs) relative to how concerned they are about taking it (concerns) (Horne et al., 2013).

Necessity beliefs might be thought of as the answer to two questions: “How much do I need this treatment to achieve a goal that’s important to me?” and “How much can I get away without it?”. Perceived necessity is not a form of efficacy belief: We might believe that a treatment will be effective but not that we need it. We might have a low necessity belief even if we understand the scientific evidence for the potential benefits of treatment. This might occur because we do not “value” that particular benefit or perceive it be important enough to overcome our concerns about taking the medicine. For example, we may understand that inhaled cortico steroids are effective for reducing inflammation in the airways in asthma, but we may not believe we need to take it every day, even when we feel well, as our necessity belief for the treatment is low.

Concerns: There is a commonality in the types of concerns that patients report about prescription medicines.

One obvious source of concern is the experience of symptoms as medication “side effects” and the disruptive effects of medication on daily living, but this is not the whole picture (Cooper et al., 2015). Many patients receiving regular medication who have not experienced adverse effects are still worried about possible problems in the future. These concerns often arise from the belief that regular use can lead to dependence or that the medication will accumulate within the body and lead to long-term harmful effects (Horne, Weinman, & Hankins, 1999). Another common concern is that regular use of medication now will make it less effective in the future (Horne, Parham, Driscoll, & Robinson, 2009). These core concerns seem to be fairly generic and relevant across a range of disease states and cultures, and they are typically endorsed by over a third of participants (Chapman et al., 2015; Clatworthy et al., 2009; Horne, 2001; Horne et al., 1999, 2009).

Medication concerns are evaluative summations of representations of the threat posed by medication. In common with illness representations, they have a cognitive and emotional dimension (Leventhal et al., 1998). Cognitive representations of treatment threat are likely to share a similar structure to illness representations (Horne & Weinman, 1998a). Representations of these risks (and benefits) of medication comprise beliefs about the timeline European Psychologist (2019), 24(1), 82–96

for onset and duration of effects, their likely consequences and the potential for control or cure (Leventhal et al., 1998).

Unpleasant symptoms may also be (correctly or incorrectly) labeled as side effects of the medication and the expectation of medication side effects may initiate a search for confirmatory symptoms (Heller, Chapman, & Horne, 2015; Nestoriuc, Orav, Liang, Horne, & Barsky, 2010).

Meaning of Medication and Sense of Self: Concerns also relate to the meaning that being on regular medication has for the individual and their sense of self. Taking a daily treatment may be an unwelcome reminder of an illness that has a negative impact on how people see themselves or perceive they are seen by others. In these circumstances, nonadherence might be seen as an implicit strategy for minimizing the impact on their sense of self (Cooper et al., 2002; Horne, 2003). Determining the necessity of a treatment may also be influenced by notions of self. There has been disappointingly little research in this area, but perceptions that one can resist the progress of disease by drawing on sources of “inner strength,” or by keeping a “positive outlook” emerged as reasons for deciding not to start clinically indicated antiretroviral treatments in interviews with human immunodeficiency virus (HIV) positive men (Cooper et al., 2002).

Balancing Necessity Against Concerns: The Necessity-Concerns Differential Necessity beliefs and concerns can be assessed using the validated Beliefs about Medicines Questionnaire (BMQ; Horne et al., 1999). A recent meta-analysis of 94 peerreviewed studies applying the BMQ in 23 long-term conditions across 18 countries showed that adherence to medication prescribed for long-term conditions was often related to necessity beliefs and concerns (Horne et al., 2013). These seem to influence adherence separately and in combination, although in some studies one or other of the constructs had a predominant influence on adherence (Horne et al., 2013).

This research suggests that many patients have a necessity-concerns dilemma in treatment decisions. Medication can be perceived as a “double-edged sword” in which the potential benefit is compromised by the tendency to harm.

For some, the dilemma is made more acute by the belief that efficacy and toxicity appear to go hand in hand and more effective medicines implicitly have more severe side effects (Leventhal, Easterling, Coons, Luchterhand, & Love, 1986).

A simple necessity-concerns differential (NCD) in which the BMQ concerns scale is subtracted from the BMQ necessity scale provides a crude indicator of the relative Ó 2019 Hogrefe Publishing

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importance of needs and concerns. However, as necessityconcerns are not complete opposites, an approach using polynomial regression has been suggested to provide a better picture of the relationships between beliefs and adherence than a simple NCD score (Phillips, Diefenbach, Kronish, Negron, & Horowitz, 2014). Despite the methodological limitations of this approach, a recent meta-analysis showed that NCD scores (which were normally distributed) were more strongly correlated with reported adherence than necessity beliefs or concerns considered in isolation (mean effect size correlations [95% CI]: necessity beliefs: 0.17 [0.14, 0.20]; concerns 0.18 [ 0.21, 0.15]; NCD 0.24 [0.18, 0.30]; Foot, La Caze, Gujral, & Cottrell, 2016; Horne et al., 1999). This indicates that patients who attained higher scores on the concerns scale than on the necessity scale were significantly less adherent.

These effects might operate through explicit and implicit processes. In some situations, nonadherence could be the result of a deliberate strategy to minimize harm by taking less medication. Alternatively, it might simply reflect the fact that patients who do not perceive their medication to be important are more likely to forget to take it. The impact of perceptions of treatment on adherence will also be influenced by beliefs about adherence, such as the importance of strict adherence to achieve the desired outcome (Siegel, Schrimshaw, & Raveis, 2000).

have more negative views about pharmaceutical medicines and vaccines, and be more reluctant to take medication or receive vaccines (Horne et al., 2013). Taken together, these sets of beliefs about medicines and about self in relation to medicines can be thought of as “pharmaceutical schema,” or in other words, how individuals structure their ideas about pharmaceuticals.

Negative pharmaceutical schema are linked to wider concerns about science, medicine, and technology within Western cultures (Calnan, Montaner, & Horne, 2005; Green et al., 2013). There is some evidence that general beliefs about medicines may vary across cultural and ethnic groups within the United Kingdom (Horne et al., 2004).

However, variation within groups is likely to be greater than between groups (Horne et al., 2004). Negative pharmaceutical schema are associated with greater concerns that specific medication will result in harm and with greater doubts about the personal need to take it (Chapman, Horne, Chater, Hukins, & Smithson, 2014; Horne et al., 2009). They influence the way in which information about the potential benefits and harms of a specific treatment are processed. In experimental studies, people with more negative pharmaceutical schema are more likely to think that symptoms are caused by the drug (i.e., attribute symptoms as side effects; Heller et al., 2015) and less likely to recall side effects correctly (Heller, Chapman, & Horne, 2017).

Pharmaceutical Schema and Social Representations of Medicines

An Extended Common-Sense Model of Self-Regulation (e-CSM)

Representations of specific medicines are influenced by more general beliefs (social representations) about medicines as a class of treatment (Horne et al., 1999). Many people are suspicious of pharmaceuticals, perceiving them to be fundamentally harmful, addictive substances that should not be taken for long periods of time, and which tend to be overprescribed by doctors (Horne et al., 1999). Moreover, the dangerous aspects of medication are often linked to their “chemical”/”unnatural” origins and to suspicions about the pharmaceutical industry (Britten, 1994; Horne et al., 1999; Pound et al., 2005). Negative views about medicines in general are often related to a broader “world-view,” characterized by suspicion of chemicals in food and the environment (Gupta & Horne, 2001) and the perception that complementary therapies (e.g., homeopathy/herbalism) are more “natural” and therefore safer than medicines (Green, Horne, & Shephard, 2013; Horne et al., 1999). Moreover, people vary in their perceptions of personal sensitivity to their effects of medicines, with many believing that they are more sensitive than other people to the effects of medicines. People with this view tend to

We suggest a symbiotic relationship between the NecessityConcerns Framework (NCF) and Leventhal’s CommonSense Model of self-regulation (CSM) in explaining variations in treatment uptake and adherence. The CSM helps us to understand the process by which treatment perceptions influence adherence, and how the content of illness representations relates to these treatment representations.

Equally, treatment perceptions and the NCF can be used to extend the explanatory power of the CSM in relation to treatment adherence (Horne et al., 2009). Figure 4 shows how the Necessity-Concerns Framework can be incorporated into Leventhal’s CSM, producing an extended model (e-CSM).

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Illness Perceptions and Treatment Necessity Beliefs We can differentiate two stages in how we arrive at our views about our need for a treatment. First, we must believe that the condition warrants treatment. Here, we try to achieve a “common-sense fit” between our concept of the European Psychologist (2019), 24(1), 82–96

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1. Symptom experiences/information trigger treatment perceptions, depending on attribution of cause e.g. illness attribution reinforces treatment necessity; attribution to side-effects reinforces concerns (Copper et al., 2009).

Contextual factors e.g. self-efficacy, social and cultural norms, personality

2

Specific and General representations of treatment e.g. Necessity beliefs, Concerns about prescribed medication (Specific) Pharmaceutical schema (General) 4

3

5

1 Illness Representation

Coping Procedure (action taken) e.g. adherence / nonadherence

Appraisal

Emotional response to illness

Coping Procedure

Appraisal

Health Threat e.g. symptom experiences or medical diagnosis

1 3

4

Emotional response to treatment e.g. worry, fear, anxiety, depression 2

5

2. Parallel processing of cognitive and emotional representations of treatment e.g. Having to take this treatment worries me” (Horne et al., 1999).

3. Illness perceptions and treatment beliefs have an internal logic as the individual strives for commonsense coherence. For example patients who perceived their illness to be a cyclical problem (linked to symptom experiences) rather than a chronic disease perceive fewer consequences, and were more likely to doubt the necessity for their treatment (Horne & Weinman, 2002; Horne et al., 2009).

4. Treatment perceptions influence adherence.

Adherence and non-adherence are both types of coping procedures (e.g. Horne & Weinman, 1999, Horne et al., 2013).

5. The outcome of adherence/non-adherence is appraised with subsequent reinforcement or change in treatment representations e.g. I omitted doses of my medication because I feel better (Horne et al., 2007).

Figure 4. Treatment representations extending Leventhal’s Common-Sense Model of Self-regulation (e-CSM).

problem (the illness) and the solution (the treatment). If we think the illness requires treatment, we then judge the relative necessity of different treatment options. These necessity evaluations are influenced by a range of factors including our illness prototypes and stereotypes (Horne & Clatworthy, 2010), pharmaceutical schema, past experiences of ourselves and others, social and cultural norms, and information we receive from various sources (Chapman et al., 2015). The way we evaluate the treatment is also affected by the symptoms we experience when we first perceive a threat to health (symptom perceptions). These symptoms can later influence our illness representations (i.e., how we make sense of, and understand, the illness – illness consequences and timeline). These, and other aspects of the e-CSM as applied to treatment, are discussed below.

Symptom Perceptions – Perceived Health Threat Initial perceptions of treatment necessity and subsequent treatment appraisals are influenced by our symptom experiences and expectations. Symptoms may stimulate medication use by acting as a reminder or by reinforcing beliefs about its necessity. Conversely, the absence of symptoms might lead to the interpretation that the condition is more benign than it actually is and lead to doubts about the need for treatment (Halm, Mora, & Leventhal, 2006). Moreover, symptoms can lead to concerns if patients interpret the symptoms as side effects (Cooper, Gellaitry, Hankins, Fisher, & Horne, 2009) or, alternatively, as evidence that the medication is not working (Leventhal et al., 1986). For example, in study of HIV positive individuals starting on antiretroviral treatment, patients who had a lack of improvement in their European Psychologist (2019), 24(1), 82–96

symptoms they attributed to HIV or to the medication had lower adherence (Cooper et al., 2009).

Illness Consequences and Timeline Symptom experiences inform representations of illness timeline and illness consequences, as well as perceptions of treatment necessity (Horne & Weinman, 2002). Many patients do not perceive a common-sense fit here. For example, patients who perceived their asthma to be a cyclical rather than a chronic disease were more likely to doubt the necessity of their preventer inhaler and not use it sufficiently (Horne & Weinman, 2002). Messages about treatment necessity are likely to be more convincing if they take account of the individual’s representation of their illness.

Causal Attributions Perceptions of necessity for different treatment options may be influenced by beliefs about the cause of the illness.

Changing lifestyle behaviors, such as stress or diet, may be perceived to be more necessary than taking medication if the person believes that stress or diet causes the illness.

For example, in a systematic review that pooled together qualitative research around patient perspectives of hypertension and drug adherence, many thought that hypertension was caused by stress; therefore, treatment was not needed when they did not feel stressed (Marshall, Wolfe, & McKevitt, 2012).

Control/Cure Individuals have different types of beliefs about illness control. The relationships between treatment beliefs and Ó 2019 Hogrefe Publishing

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Internal factors Appraisal: assessment and interpretation of outcomes relative to expectations External (environmental) factors e.g.

-

Opportunities Triggers Information Communication (e.g. healthcare professionals, friends etc.)

Cultural influences Health policy Social support Media Resources Views of significant others Social norms Financial reasons Medication costs

Perceptions of illness Perception and Symptom interpretation

Beliefs about specific prescribed medicines e.g.

- Necessity beliefs - Concerns

Intention to take medication

Adherence

Social representations of medicines

Perceptions of self in relation to illness - Personal resilience to illness - Self-efficacy - Perceived control

Perceptions of self in relation to treatment

-

Personal sensitivity to medication Self-efficacy Perceived control

Ability e.g.

-

Memory Dexterity Knowledge Organisation

Affect/mood e.g. anxiety, depression

Figure 5. Perceptions and Practicalities Approach – a detailed conceptual map of adherence.

beliefs about illness control are specific to the type of control belief. For example, necessity beliefs are positively correlated with beliefs that the illness will be controlled by treatment (a form of efficacy belief), but not with other types of control beliefs (e.g., chance/fate or personal control over illness) (Horne & Weinman, 2002). This relationship between illness control beliefs and treatment necessity beliefs depends on the perception that the particular treatment is appropriate (i.e., makes sense to the patient).

Parallel Processing of Cognitive and Emotional Representations of Treatment Cognitive and emotional aspects of treatment representations are processed in parallel. Beliefs that taking medication will result in unpleasant side effects may be a source of anxiety and worry. In some situations, such as the prescription of chemotherapy or radical surgery, people may fear the treatment more than the illness. In a series of interviews with 31 women, some stated that they would rather have the cancer than have to cope with the side effects of treatment, with one stating “I felt so horrible I said to my husband these tablets are making me feel so ill I think I’d rather take the risk with cancer than feel miserable, unhappy, fat” (Cahir et al., 2015, p. 3125). External factors influencing perceptual and practical barriers to adherence Figure 5 shows the interplay between internal and external factors impacting on adherence. Perceptual and Ó 2019 Hogrefe Publishing

practical barriers to adherence (internal factors) are influenced by external (environmental) factors, such as the quality of communication with healthcare professionals (Chapman et al., 2015; Jang & Bakken, 2017), the availability of social support (Jang & Bakken, 2017), and cultural factors such as religious beliefs (Spiers, Smith, Poliquin, Anderson, & Horne, 2016; Figure 4).

Internal contextual factors such as depression may also impact on adherence by accentuating perceptual and practical barriers (DiMatteo et al., 2000). For example, depression has been associated with greater concerns about medicines (Salgado et al., 2017), greater symptom burden (Leonhart et al., 2016), lower self-efficacy (Hilliard, Eakin, Borrelli, Green, & Riekert, 2015), and more negative illness perceptions (Nahlén Bose, Elfström, Björling, Persson, & Saboonchi, 2016). Depression may also reduce the person’s ability to organize and implement adherence schedules and routines, thereby increasing practical barriers.

The Perceptions and Practicalities Approach to Supporting Informed Adherence For interventions to be adopted in clinical practice, they need to be simple, clear, and require minimal effort to implement into practice (Flottorp et al., 2013). Research investigating European Psychologist (2019), 24(1), 82–96

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patients’ perceptions of illness treatment suggests that treatment beliefs (necessity-concerns) may be salient for adherence. We have suggested a pragmatic framework for medication beliefs and the importance of illness representations, as described in Leventhal’s Common-Sense Model, in influencing evaluations of treatment necessity and concerns.

We can apply this framework to operationalize some of the salient perceptions relating to adherence within a simple motivation–ability structure. Here, we outline a 3-step PAPA to supporting informed adherence.

Taking a “No-Blame” Approach to Facilitate Informed Adherence Adherence interventions should support informed treatment choices and help avoid choices that are based on misconceptions and misplaced concerns, such as not using preventer medication as the patient does not feel any difference after taking the inhaler (Horne et al., 2005). One way of determining whether informed choice has been achieved is to check whether the individual can demonstrate knowledge of relevant information about the treatment, then act according to their beliefs (Michie, Dormandy, & Marteau, 2003). This can be extended to a concept described as informed adherence – where “informing” goes beyond the provision of information to consider the individual’s perceptions of the illness and treatment (Horne & Weinman, 2004). Treatment recommendations should be presented in a way that considers the patient’s beliefs and seeks to address any incompatibilities between these beliefs and the evidence-based recommendations (Horne & Clatworthy, 2010). A no-blame approach is essential to encourage an honest an open discussion where the patient feels able to report nonadherence and express doubts and concerns about the treatment that many patients are reluctant to report (Nunes et al., 2009).

The 3-Step Perceptions and Practicalities Approach (1) Necessity beliefs: Communicate a rationale for the personal necessity of treatment that addresses the implicit question “Why do I need to follow this treatment to achieve a goal that is important to me.” This should demonstrate a common-sense fit with the patient’s beliefs about the illness and consider symptom expectations and experiences as specified in the e-CSM above (Figure 4).

(2) Concerns: Elicit and address concerns and outstanding information needs. Provide support with side effect management if indicated and address low self-efficacy beliefs relating to adherence European Psychologist (2019), 24(1), 82–96

(3) Practicalities: Make the treatment as easy and convenient to use as possible and help the patient to overcome the intention-behavior gap and support the formation of a treatment habit.

Perceptual and practical barriers to adherence can be addressed using appropriate behavior change techniques (BCTs; Michie, Johnston, Francis, Hardeman, & Eccles, 2008) as illustrated by the examples shown in Table 1.

Perceptions Cognitive behavioral therapy (CBT) and motivational interviewing (MI) are effective techniques for challenging unhelpful beliefs and increasing motivation, respectively (Beck & Dozois, 2011; Miller & Rollnick, 2002). Less intensive educational interventions have also proved effective in modifying beliefs about medicines (Clifford, Barber, Elliott, Hartley, & Horne, 2006; Magadza, Radloff, & Srinivas, 2009) including individually tailored text messages targeting individual patient’s beliefs based on the e-CSM (Petrie et al., 2012) and “active visualization” – a technique designed to enable patients to visualize the biological processes involved in treatment (Perera, Thomas, Moore, Faasse, & Petrie, 2014).

Practicalities Practical barriers to adherence, such as forgetting, are often the primary target of adherence interventions. Numerous studies have evaluated the efficacy of audiovisual reminders, short-term message service (SMS) messages, and pager messages, with varying degrees of success (Vervloet et al., 2012). Simplifying the regimen and reducing unnecessary polypharmacy is also of key importance, particularly in patients with complex comorbidities. Simplifying the regimen generally improves the ability to adhere (Ingersoll & Cohen, 2008) by making the treatment less intrusive (Cooper et al., 2010) and as easy and convenient as possible (Cooper et al., 2010). Reorganizing multiple medicines into dispensing boxes, and providing home delivery of medicines free of charge (Zillich et al., 2012) increases adherence in the short term, presumably by facilitating access to medicines and reducing treatment complexity.

These approaches should be tailored to the specific adherence barriers facing the patient as providing unwanted reminders may be detrimental to adherence (Mannheimer et al., 2006). Linking the behavior to specific environmental cues to encourage habit formation may be effective.

However, this is may then be susceptible to changes in the environment or routine such as when on holiday Ó 2019 Hogrefe Publishing

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Table 1. Potential behavior change techniques (BCTs) for addressing perceptual and practical barriers to adherence Behavior change technique

Example Examples of techniques for addressing perceptual barriers

Problem-solving

Identify barriers to medication-taking and discuss possible ways to overcome them

Re-attribution

Explore alternative explanations for nonspecific symptoms that the patient attributes to treatment side effects Provide information about how missing doses can affect disease control if the patient has doubts about the need for adherence Use pictures or animations to show what happens to the levels of medication in the body when medication is missed Elicit and evaluate the potential advantages and disadvantages to taking medication regularly If the patient perceives their medication as an unnatural chemical provide an alternative view of medication as supporting natural processes within the body

Information about health consequences Salience of consequences Pros and cons Framing/reframing

Examples of techniques for addressing practical barriers Prompts (Triggers) and Cues

Using reminders e.g., text messages or reminders from family members/carers

Habit formation

Establishing a routine around medication-taking, for example by taking treatment with breakfast each morning Formulate a plan for medication-taking, including plans for taking medicines when away from home Provide calendar or diary for the patient to use to record doses taken and identify patterns

Action planning – Intention implementations Self-monitoring Social support

Identify friends or family members to remind patient to take their medication

Adding objects to the environment

Provide a dosette box or blister pack to help organize medication

Anchoring to established habits

Suggest that the patient keeps their medication close to the bedside or next to their toothbrush

(Lally & Gardner, 2013). Implementation intentions can be used to link behaviors to specific environmental cues (Gollwitzer, 1993).

Tailoring to Individual Needs and Preferences Evidence suggests that interventions which are tailored to the individual’s needs and preferences, and influence the way the individual interacts with the treatment, are more likely to be effective (Gatwood et al., 2016; Hugtenburg, Timmers, Elders, Vervloet, & van Dijk, 2013; Kassavou & Sutton, 2017, 2018; Lewis et al., 2013). A recent multivariable meta-regression analysis demonstrated that tailored interventions explained the largest variance in adherence effect sizes (β = 1.100, p = .008) (Kassavou & Sutton, 2018). Theory-based interventions also appear to be more effective (Marteau et al., 2006; Noar & Zimmerman, 2005; Painter et al., 2008). A recent meta-analysis reporting on theory- or model-linked adherence interventions reported an overall effect size of 0.3 for the intervention versus control groups (Conn et al., 2016), an effect size which, while modest in size, is larger than that reported with simple reminder or adherence feedback interventions (which report effects sizes of approximately 0.1) (Demonceau et al., 2013; Fenerty, West, Davis, Kaplan, & Feldman, 2012). Ó 2019 Hogrefe Publishing

Tailoring can be achieved through identifying and addressing each patient’s perceptual and practical barriers to adherence, for example, by using validated questionnaires such as the Beliefs about Medicines Questionnaire (Horne et al., 1999), the brief Illness Perceptions Questionnaire (Broadbent, Petrie, Main, & Weinman, 2006) to elicit each patient’s perceptual barriers to adherence (Clifford et al., 2006; Perera et al., 2014). Questionnaires can also be used to identify practical barriers to adherence (Chapman et al., 2015; Clifford et al., 2006). Intervention components can then be tailored to address only those barriers that are experienced by the patient.

Beyond the content of the intervention, it is important that the delivery channel (e.g., healthcare professional, text, apps, etc.) is optimized for the individual. It is important to consider the wider context in which the intervention will be adopted (Tucker et al., 2017). This can be achieved by involving members of the target population in the development and testing of interventions (Yardley, Morrison, Bradbury, & Muller, 2015).

It is likely that the efficacy of adherence support increases as more elements of the PAPA are utilized. Some improvement in adherence might occur with practical support but these are likely to be enhanced if the intervention also addresses perceptions. To increase effectiveness, the intervention should be tailored to address the specific perceptions and practicalities influencing motivation and European Psychologist (2019), 24(1), 82–96

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ability for the individual and then reviewed at intervals over the course of a long-term condition.

Conclusion The Perceptions and Practicalities Approach (PAPA) provides a simple framework for understanding nonadherence and developing interventions to support optimal adherence to appropriate treatments. Research conducted across long-term conditions and in different sociodemographic groups shows that nonadherence is underpinned by intrinsic factors of motivation and ability, moderated by extrinsic factors such as opportunity and triggers. The PAPA considers the complex cognitive processes underlying decisions about whether to take and adhere to treatment, as well as the practical factors influencing ability.

This simple approach can guide the development of pragmatic adherence interventions by eliciting and addressing the specific perceptions and practical barriers influencing each patient’s motivation and ability to take their medicine as prescribed, thus bringing us one step closer to addressing the challenge of improving adherence in chronic illness.

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Rich, Z. C. (2017). Enhancing public health HIV interventions: a qualitative meta-synthesis and systematic review of studies to improve linkage to care, adherence, and retention. EBioMedicine, 17, 163–171. https://doi.org/10.1016/j.ebiom.2017.01.036 Vervloet, M., Linn, A. J., Van Weert, J., De Bakker, D. H., Bouvy, M. L., & Van Dijk, L. (2012). The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: A systematic review of the literature. Journal of the American Medical Informatics Association, 19, 696–704. https://doi.org/10.1136/amiajnl-2011-000748 Vrijens, B., De Geest, S., Hughes, D. A., Przemyslaw, K., Demonceau, J., Ruppar, T., . . . Urquhart, J. (2012). A new taxonomy for

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describing and defining adherence to medications. British Journal of Clinical Pharmacology, 73, 691–705. https://doi. org/10.1111/j.1365-2125.2012.04167.x Yardley, L., Morrison, L., Bradbury, K., & Muller, I. (2015). The person-based approach to intervention development: Application to digital health-related behavior change interventions.

Journal of Medical Internet Research, 17, e30. https://doi.org/ 10.2196/jmir.4055 Zillich, A. J., Jaynes, H. A., Snyder, M. E., Harrison, J., Hudmon, K. S., de Moor, C., & French, D. D. (2012). Evaluation of specialized medication packaging combined with medication therapy management: Adherence, outcomes, and costs among Medicaid patients. Medical Care, 50, 485–493. https://doi.org/ 10.1097/MLR.0b013e3182549d48 History Received June 7, 2017 Revision received May 15, 2018 Accepted May 24, 2018 Published online February 11, 2019 Funding Rob Horne reports personal fees from AbbVie, Amgen, Biogen, Idec, Gilead Sciences, GlaxoSmithKline, personal fees from Janssen, Pfizer, Roche, Shire Pharmaceuticals, MSD, Astellas, Astrazeneca, DRSU, Novartis, Universitätsklinikum Hamburg-Eppendorf, and is the cofounds of Spoonful of Sugar Ltd, a UCL-spin out company specializing in medicines adherence. Rob Horne and Vari Wileman are supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North Thames at Bart’s Health NHS Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. Amy Chan reports speaker fees from Novartis and has received an educational grant and consultancy fees from Janssen-Cilag. She was the recipient of the Medicines New Zealand 2015 award and is a freelance consultant for Spoonful of Sugar Ltd.All other authors have nothing to disclose.

Rob Horne Centre for Behavioral Medicine UCL School of Pharmacy University College London London WC1H 9HR United Kingdom r.horne@ucl.ac.uk

Rob Horne is Professor of Behavioural Medicine at University College London. His research focuses on the role of psychological and behavioral factors in explaining variation in response to treatment. His current interests center on the development of interventions to support engagement with essential treatments and on optimizing the nonspecific effects (placebo and nocebo components) of medicines. A key focus is on understanding patient and public representations of illness and treatment and how representations influence self-regulation in illness and treatment outcomes.

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R. Horne et al., Supporting Adherence to Medicines for Long-Term Conditions

Vanessa Cooper (PhD) is a Chartered Health Psychologist at University College London. She has 15 years of experience at the forefront of research into medicines-related behavior. Vanessa has expertise in a wide range of methodologies including qualitative and quantitative approaches, systematic reviews, and meta-analyses. She is experienced in the application of Experience-Based Co-Design to develop patient-centered approaches to healthcare delivery and in the development of interventions to support patient engagement with treatment.

Vari Wileman (PhD) is a postdoctoral researcher at the University College London, Centre of Behavioural Medicine, School of Pharmacy. Vari experienced in applying psychological methods to the study of behavior relevant to health, illness, and health care. Her particular interest lies in psychological factors associated with treatment nonadherence and acceptance in long-term conditions and has specific knowledge of developing psychological theorybased interventions and in conducting and evaluating psychological intervention randomized controlled trials.

European Psychologist (2019), 24(1), 82â&#x20AC;&#x201C;96

Amy Chan (PhD) is a postdoctoral researcher at the University College London, Centre of Behavioural Medicine, School of Pharmacy. Amy has led on several research projects in the area of adherence and chronic disease, and digital adherence support interventions. She has a particular interest and expertise in understanding medicines-related behaviors â&#x20AC;&#x201C; particularly around medication adherence in long-term conditions. Amy has provided consultancy to medical research organizations, nongovernmental organizations, and pharmaceutical companies and has been involved in developing patient-centric programs in a variety of healthcare settings.

Ă&#x201C; 2019 Hogrefe Publishing

EFPA News and Views Meeting Calendar April 25–28, 2019 7th World Congress on ADHD: From Child to Adult Disorder Lisbon, Portugal Contact: http://www.adhd-congress. org/ May 13–15, 2019 2019 Conference of the International Society for Research on Identity Naples, Italy Contact: http://identityisri.org/ conference/ June 5–7 2019 19th International Conference on Diversity in Organizations, Communities & Nations Patras, Greece Contact: https://ondiversity.com/ 2019-conference/call-for-papers

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June 20–22, 2019 26th International Conference of Europeanists Sovereignties in Contention: Nations, Regions and Citizens in Europe Madrid, Spain Contact: https://councilforeuropeanstudies.org/conferences/upcomingconferences/2019-conference July 2–5, 2019 XVI European Congress Psychology Moscow, Russia Contact: https://ecp2019.ru

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September 3–7, 2019 European Health Psychology Society – Annual Conference Dubrovnik, Croatia Contact: https://2019.ehps.net/ September 16–17, 2019 Aging & Social Change: Ninth Interdisciplinary Conference University of Vienna Vienna, Austria Contact: http://agingandsocialchange. com/2019-conference/call-for-papers

August 8–11, 2019 127th Annual Convention of the American Psychological Association Chicago, IL, USA Contact: http://www.apa.org/ convention

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EFPA News and Views

European Federation of Psychologists’ Associations What is EFPA? EFPA is the leading Federation of National Psychologists Associations. It provides a forum for European cooperation in a wide range of fields of academic training, psychology practice and research.

There are 37 member associations of EFPA representing about 300,000 psychologists.

The member organizations of EFPA are concerned with promoting and improving psychology as a profession and as a discipline, particularly, though not exclusively, in applied settings and with emphasis on the training and research associated with such practice.

The psychologists in the member associations include practitioners as well as academic and research psychologists.

The Federation has as one of its goals the integration of practice with research and the promotion of an integrated discipline of psychology.

A full list of all EFPA members associations can be found at http://www.efpa.eu/members A full list of all EFPA associate members can be found at http://www.efpa.eu/associate-members European Psychologist Members of EFPA Member Associations and other European psychology organizations supporting the European Psychologist are entitled to a special subscription rate of 149.00 per year The European Psychologist is published quarterly. Only calendar year subscriptions are available. Prices exclude shipping and handling charges. All subscriptions include print issues and access to full-text online.

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What EFPA Does • • • • • • • • • •

Representation, advocacy, and lobbying at European level Promotion of psychology education, research and profession EuroPsy certification Test User Accreditation Support for Member Associations European projects Advice on professional affairs, work areas, new developments Publicity and information sharing European Congress of Psychology European Psychologist (Official Organ of the EFPA, http:// www.hogrefe.com/j/ep) • The EFPA News Magazine (http://efpa.magzmaker.com/) EFPA Membership Membership is open to the national psychologist association of all European countries but there may be only one member association per country. EFPA has no individual members.

In countries where there is more than one national psychologist association, the Federation should endeavour to identify the most representative organization and, if appropriate, encourage the development of a national federation in order to promote cooperation among psychological associations.

Membership is determined by the General Assembly upon presentation of the applicant association’s articles, statutes, and code of ethics plus details of the membership (see EFPA Statutes Article 5).

As a European federation representing the interests of psychologists in Europe, EFPA is committed to making contact with member associations of psychologists from European countries, which are not yet members of EFPA. This is particularly the case in relation to psychologists’ associations from countries in the east of Europe, with whom EFPA is concerned to make contacts, to share information and to promote collaboration.

A member of the EFPA Executive Council has responsibility for developing these links and contacts, and the EC will attempt to help to support initiatives.

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President: Vice President / Secretary General: Vice President / Treasurer: EC Member: EC Member: EC Member: EC Member:

Telmo Mourinho Baptista (2015-2019) Robertas Povilaitis (2011-2019) Christoph Steinebach (2011-2019) Tor Levin Hofgaard (2011-2019) Eleni Karayianni (2015-2019) Josip Lopizic (2017-2021) Bruna Zani (2015-2019)

EFPA Head Office Brussels Director: Office Manager: Management Assistant: Management Assistant: Communication Coordinator:

Sabine Steyaert Julie Van den Borre Valerie Boni Ivana Marinovic Ruth Mozagba

EFPA Head Office Grasmarkt 105 / 39 1000 Brussels Belgium Tel.: +32 2 503-4953 Fax: +32 2 503-3067 E-mail: headoffice@efpa.eu Web http://www.efpa.eu/ For further information on EFPA events please visit the EFPA website: http://www.efpa.eu/allevents

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Instructions to Authors - European Psychologist European Psychologist is a multidisciplinary journal that serves as the voice of psychology in Europe, seeking to integrate across all specializations in psychology and to provide a general platform for communication and cooperation among psychologists throughout Europe and worldwide.

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November 2016

European Psychologist (2019), 24(1)

European Journal of Health Psychology

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Editor-in-Chief Claus Vögele, University of Luxembourg, Luxembourg Editorial Assistant Nicole Knoblauch, Luxembourg

ISSN-Print 2512-8442 ISSN-Online 2512-8450 ISSN-L 2512-8442 4 issues per annum (= 1 volume) Subscription rates (2019) Individuals US $120.00 / € 94.00 (print & online) Institutions From US $326.00 / € 249.00 (print only; pricing for online access can be found in the journals catalog at hgf.io/journals2019) Postage / Handling US $16.00 / € 12.00

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Associate Editors Verena Klusmann, Bremen, Germany Arnold Lohaus, Bielefeld, Germany Britta Renner, Konstanz, Germany Christel Salewski, Hagen, Germany Silke Schmidt, Greifswald, Germany Heike Spaderna, Trier, Germany

About the Journal The European Journal of Health Psychology was founded to provide a platform for research in health psychology, and for its application in a wide range of contexts. Health psychology is the scientific discipline within psychology that aims to promote and preserve health, to prevent diseases and contribute to their treatment by identifying disease-relevant aetiological processes, and to improve health provision.

Call for Papers The European Journal of Health Psychology invites you and/or your working group to submit papers on all aspects of the field!

The European Journal of Health Psychology strives to promote theory and practice in the analysis of psychological approaches to health and disease. Its aim is, therefore, to publish high quality empirical or experimental research as well as sound practiceoriented articles, current methodological developments, and comprehensive critical reviews of the scientific literature.

Electronic Full Text The full text of the journal – current and past issues (from 1999 onward) – is available online at econtent.hogrefe.com/loi/zgp (included in subscription price). A free sample issue is also available there.

The journal has been publishing highquality, innovative research since 1993 (until 2016 as Zeitschrift für Gesundheitspsychologie, ISSN 0943-8149).

Manuscript Submissions All manuscripts, including Electronic Supplementary Material (ESM), should be submitted online at www.editorialmanager.com/zgp, where full instructions to authors are also available.

Abstracting Services The journal is abstracted / indexed in Social Sciences Citation Index (SSCI), Social Scisearch, Journal Citation Report/Social Sciences Edition, PsycInfo, PsycLit, PsyJOURNALS, PSYNDEX, Scopus, IBZ, IBR, and European Reference List for the Humanities (ERIH). Impact Factor (Journal Citation Reports®, Clarivate Analytics): 2016 = 0.909

Assessment methods in health psychology “This book is an excellent overview of measurement issues that are central to health psychology.”

David French, PhD, Professor of Health Psychology, University of Manchester, UK

Yael Benyamini / Marie Johnston / Evangelos C. Karademas (Editors)

Assessment in Health Psychology (Series: Psychological Assessment – Science and Practice – Vol. 2) 2016, vi + 346 pp.

US $69.00 / € 49.95 ISBN 978-0-88937-452-2 Also available as eBook

Assessment in Health Psychology presents and discusses the best and most appropriate assessment methods and instruments for all specific areas that are central for health psychologists. It also describes the conceptual and methodological bases for assessment in health psychology, as well as the most important current issues and recent progress in methods.

A unique feature of this book, which brings together leading authorities on health psychology assessment, is its emphasis on the bidirectional link between theory and practice.

Assessment in Health Psychology is addressed to masters and doctoral students in health psychology, to all

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those who teach health psychology, to researchers from other disciplines, including clinical psychology, health promotion, and public health, as well as to health policy makers and other healthcare practitioners.

This latest volume in the series Psychological Assessment – Science and Practice provides a thorough and authoritative record of the best available assessment tools and methods in health psychology, making it an invaluable resource both for students and academics as well as for practitioners in their daily work.

So that’s how my mind works – Now I get it!

“As a fan of PSI theory for more than 20 years, I am very happy seeing it translated for popular consumption! The book makes the theory reasonably simple, with lots of fun illustrations.”

Kennon M. Sheldon, PhD, Professor of Psychological Sciences, University of Missouri, Columbia, MO

Johannes Storch / Corinne Morgenegg / Maja Storch / Julius Kuhl

Now I Get It!

Understand Yourself and Take Charge of Your Behavior 2018, vi + 248 pp.

US $34.80 / € 27.95 ISBN 978-0-88937-541-3 Also available as eBook Using the example of four colleagues working together in a small company, Now I Get It! shows us the main personality types and their strengths and weakness in such a way that we gain real “now I get it!” insights into what is going on in our own and others’ subconscious.

How does my mind work and what kind of personality do I have? When we can answer these questions and have come to terms with who we are, then the solutions to many issues that arise in everyday life will fall into place. What sort of people do I get on with best and how can I

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best deal with the others? Are there recurring stressful situations in my professional or private life, and how do I resolve them?

This humorously written and illustrated book, by the world’s leading experts in personality systems interaction (PSI) theory and the Zurich Resource Model (ZRM), gives us profound insights into our and other people’s subconscious thoughts – so we can adapt our own behavior and interactions to improve our quality of life. Cartoons and worksheets help us on our way.