Vion He CBT-based Mental Health Apps’ Treatment Outcomes in Heterosexual and LBTQ+ College Students with Depression

heterosexual, and the other half will identify as LGBTQ+. To be eligible, students must be at least 18 years old, have a smartphone or a tablet with internet access, and exhibit at least a mild level of depression, as measured by the 9-item Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001). Individuals who are using SSRI(s) at the time of recruitment will be excluded, as the effects of antidepressants on depression may interfere with the study outcomes. Individuals with suicidal ideations within the month prior to recruitment will also be excluded, due to participant safety and ethical considerations. Instead, they will be directly referred to professional psychological services.

Procedure

As a first step, participants will complete a demographic survey and a PHQ-9 assessment (Kroenke et al., 2001) to determine their depression level. Those who score 5 or higher, indicating at least a mild level of depression, will be randomly assigned to one of the two groups: (a) doing activities on the SuperBetter app or (b) doing activities on the MoodKit app. The participants will further be separated into heterosexual and LGBTQ+ students, resulting in four groups total. Both apps have demonstrated a significantly positive impact on alleviating depression in randomized controlled trials in a largely heterosexual sample (Bakker et al., 2018; Roepke et al., 2015). Because the two apps have different emphases when approaching depression, including both in this study can help compare results and distinguish between features that contribute to the efficacy of the intervention. SuperBetter is a game-based app that helps build resilience and improve mental health by helping individuals build self-esteem and self-acceptance through activities such as “hugging yourself” and “writing down things you feel grateful for” (Roepke et al., 2015). Participants assigned to this group will be instructed to complete a To-Do List every day with seven activities of their choice. MoodKit is an app that focuses on mood self-management. It contains four main tools: mood-improving activities, a thought checker, a mood tracker, and a journal (Dahne et al., 2019). Participants assigned to this group will be instructed to record their mood and complete at least three mood-improving activities every day and use the journal and the thought checker as needed. After two months, all participants will be assessed again for depression. Researchers will also assess their depression levels three months after the intervention to see if there is any lasting effect.

Measures

Demographic form. A demographic form will be administered to collect information on participants’ age, gender, sexual identity, race, academic standing, use of professional mental health services (e.g., therapy or life coaching) and antidepressants, suicide ideations, Internet access, use of electronic devices, and technical difficulties if any.

Depression. Depression will be assessed using the PHQ9 (Kroenke & Spitzer, 2001), a self-report scale that measures depression and its severity. The PHQ-9 consists of nine items on a four-point Likert Scale, eight of which measure the frequency of symptoms from not at all to nearly every day, and one of which measures the extent of difficulty these symptoms cause in everyday life, from not difficult at all to extremely difficult. The PHQ-9 has been shown to be a reliable depression assessment tool with a Cronbach’s alpha of approximately 0.86 (Kroenke et al., 2001), and validity has been established, especially in the college student population (Keum et al., 2018; Kroenke & Spitzer, 2001).

Data Analysis Plan

A repeated measures analysis of co-variance (ANCOVA) will be conducted to help determine if there are significant differences in treatment outcomes between the four groups, using the mean differences in PHQ-9 scores before the intervention, immediately after, and three months later, controlling for covariates (e.g., the use of mental health services). If there are differences, post-hoc tests will be used to determine which group yields the best result.

Discussion

The proposed study seeks to examine whether there are differences in treatment outcomes of CBT-based mental health apps on depression in LGBTQ+ and heterosexual students. While mental health apps have the potential to improve access to mental health care for LGBTQ+ youth, not much is known about whether they are effective in addressing the needs of this population (Schueller et al., 2019). This study might contribute to a theoretical understanding of whether sexual identity (identifying as a LGBTQ+ or not) moderates the apps’ effects on depression. Given the potential of mobile mental health in increasing LGBTQ+ youth’s service utilization rate (Rozbroj et al., 2015; Schueller et al., 2019), this study might enhance our understanding of the impact of these apps on treating depression in LGBTQ+ college students. It will help elucidate whether there is a need to improve such apps or develop more apps, to better meet their needs. Future research might examine and compare the impact of other types of mental health apps to determine the optimal treatment approach, as well as expand the measures of depression beyond self-report measures, so as to avoid the results may be subject to response bias. Future research might also examine and compare the impact of other types of mental health apps to determine the optimal treatment approach, as well as expand the measures of depression beyond self-report measures, so as to avoid response bias. Moreover, the outcomes of this study can be especially helpful in the context of the current COVID-19 pandemic. As LGBTQ+ youth are experiencing a heavier mental health burden and increased demand for services due to limited resources and support available, CBT-based apps may have potential to serve this population in the aftermath of the pandemic (Moore et al., 2021; Salerno et al., 2020).

References

Backhaus, I., Lipson, S. K., Fisher, L. B., Kawachi, I., & Pedrelli, P. (2019). Sexual assault, sense of belonging, depression and suicidality among LGBQ and heterosexual college students. Journal of American College Health, 69(4), 404–412. Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2018). A randomized controlled trial of three smartphone apps for enhancing public mental health. Behaviour Research and Therapy, 109, 75-83. Beck, A. T. (1970). Cognitive therapy: Nature and relation to behavior therapy. Behavior Therapy, 1(2), 184–200. Broglia, E., Millings, A., & Barkham, M. (2019). Counseling with guided use of a mobile well-being app for students experiencing anxiety or depression: Clinical outcomes of a feasibility trial embedded in a student counseling service. JMIR mHealth and uHealth, 7(8), e14318. Brown, A., Rice, S. M., Rickwood, D. J., & Parker, A. G. (2016). Systematic review of barriers and facilitators to accessing and engaging with mental health care among at‐risk young people. Asia‐Pacific Psychiatry, 8(1), 3-22. Bruehlman-Senecal, E., Hook, C. J., Pfeifer, J. H., FitzGerald, C., Davis, B., Delucchi, K. L., Haritatos, J., & Ramo, D.E. (2020). Smartphone app to address loneliness among college students: Pilot randomized controlled trial. JMIR Mental Health, 7(10), e21496. Charkhandeh, M., Talib, M. A., & Hunt, C. J. (2016). The clinical effectiveness of cognitive behavior therapy and an alternative medicine approach in reducing symptoms of depression in adolescents. Psychiatry Research, 239, 325-330. Dahne, J., Lejuez, C. W., Diaz, V. A., Player, M. S., Kustanowitz, J., Felton, J. W., & Carpenter, M. J. (2019). Pilot Randomized Trial of a Self-Help Behavioral Activation Mobile App for Utilization in Primary Care. Behavior therapy, 50(4), 817–827. Driessen, E., & Hollon, S. D. (2010). Cognitive behavioral therapy for mood disorders: Efficacy, moderators and mediators. Psychiatric Clinics of North America, 33(3), 537–555. Dunbar, M. S., Sontag-Padilla, L., Ramchand, R., Seelam, R., & Stein, B. D. (2017). Mental health service utilization among lesbian, gay, bisexual, and questioning or queer college students. Journal of Adolescent Health, 61(3), 294-301. Elliott, M. N., Kanouse, D. E., Burkhart, Q., Abel, G. A., Lyratzopoulos, G., Beckett, M. K., Schuster, M. A., & Roland, M. (2015). Sexual minorities in England have poorer health and worse health care experiences: A national survey. Journal of General Internal Medicine, 30(1), 9-16. Ellis, A. (1962). Reason and emotion in psychotherapy. Secaucus, N.J: Citadel Press. Lyle Stuart. Hofmann, S. G., Asnaani, A., Vonk, I. J. J., Sawyer, A. T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive Therapy and Research, 36(5), 427-440. Hollis, C., Falconer, C. J., Martin, J. L., Whittington, C., Stockton, S., Glazebrook, C., & Davies, E. B. (2016). Annual research review: Digital health interventions for children and young people with mental health problems-a systematic and meta-review. Journal of Child Psychology and Psychiatry, 58(4), 474-503. Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287-298. Keum, B. T., Miller, M. J., & Inkelas, K. K. (2018). Testing the factor structure and measurement invariance of the PHQ-9 across racially diverse U.S. college students. Psychological Assessment, 30(8), 1096–1106. Kilicaslan, J., & Petrakis, M. (2019). Heteronormative models of health-care delivery: Investigating staff knowledge and confidence to meet the needs of LGBTIQ+ people. Social Work in Health Care, 58(6), 612-632. Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32(9), 509–515. Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. Lipson, S. K., Lattie, E. G., & Eisenberg, D. (2019). Increased rates of mental health service utilization by US college students: 10-year population-level trends (2007–2017). Psychiatric Services, 70(1), 60-63. Lucassen, M., Samra, R., Iacovides, I., Fleming, T., Shepherd, M., Stasiak, K., & Wallace, L. (2018). How LGBT young people use the internet in relation to their mental health and envisage the use of e-therapy: Exploratory study. JMIR Serious Games, 6(4), e11249. McCloud, T., Jones, R., Lewis, G., Bell, V., & Tsakanikos, E. (2020). Effectiveness of a mobile app intervention for anxiety and depression symptoms in university students: Randomized controlled trial. JMIR mHealth and uHealth, 8(7), e15418. Meyer, I. H. (2003). Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: Conceptual issues and research evidence. Psychological Bulletin, 129(5), 674-697.

Mohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., Caccamo, L., Stiles-Shields, C., Schueller, S. M. (2017). IntelliCare: An eclectic, skillsbased app suite for the treatment of depression and anxiety. Journal of Medical Internet Research, 19(1), e10. Moore, S. E., Wierenga, K. L., Prince, D. M., Gillani, B., & Mintz, L. J. (2021). Disproportionate impact of the COVID-19 pandemic on perceived social support, mental health and somatic symptoms in sexual and gender minority populations. Journal of Homosexuality, 68(4), 577-591. Porras-Segovia, A., Díaz-Oliván, I., Gutiérrez-Rojas, L., Dunne, H., Moreno, M., & Baca-García, E. (2020). Apps for depression: Are they ready to work?. Current Psychiatry Reports, 22(3), 1-9. Roepke, A. M., Jaffee, S. R., Riffle, O. M., McGonigal, J., Broome, R., & Maxwell, B. (2015). Randomized controlled trial of SuperBetter, a smartphone-based/internet-based self-help tool to reduce depressive symptoms. Games for Health Journal, 4(3), 235-246. Rozbroj, T., Lyons, A., Pitts, M., Mitchell, A., & Christensen, H. (2014). Assessing the applicability of e-therapies for depression, anxiety, and other mood disorders among lesbians and gay men: Analysis of 24 web-and mobile phone-based self-help interventions. Journal of Medical Internet Research, 16(7), e166. Rozbroj, T., Lyons, A., Pitts, M., Mitchell, A., & Christensen, H. (2015). Improving self-help e-therapy for depression and anxiety among sexual minorities: An analysis of focus groups with lesbians and gay men. Journal of Medical Internet Research, 17(3), e66. Salerno, J. P., Williams, N. D., & Gattamorta, K. A. (2020). LGBTQ populations: Psychologically vulnerable communities in the COVID-19 pandemic. Psychological Trauma: Theory, Research, Practice, and Policy, 12(S1), S239–S242. Sanchez, N. F., Sanchez, J. P., & Danoff, A. (2009). Health care utilization, barriers to care, and hormone usage among male-to-female transgender persons in New York City. American Journal of Public Health, 99(4), 713-719. Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6(3), 243-255. Stawarz, K., Preist, C., Tallon, D., Wiles, N., & Coyle, D. (2018). User experience of cognitive behavioral therapy apps for depression: An analysis of app functionality and user reviews. Journal of Medical Internet Research, 20(6), e10120. Williams, K. A., & Chapman, M. V. (2011). Comparing health and mental health needs, service use, and barriers to services among sexual minority youths and their peers. Health & social work, 36(3), 197-206.

Family Support, Motivation for Change, and Treatment Entry of Individuals with Substance Use Disorders

Julia E. Leschi

Substance abuse is a public health crisis, with around 20 million adults in the United States struggling with a substance use disorder in 2017 (Substance Abuse and Mental Health Services Administration [SAMHSA], 2018). One of the major issues hindering substance abuse recovery is that too few people enter treatment (Cunningham et al., 1993; Kessler et al., 2001; Wang et al., 2004). Of those who do, there is often a treatment delay of at least a decade after identification of the substance use problem, and many do not complete treatment (Cunningham et al., 1993; Kessler et al., 2001; Wang et al., 2004). Research has identified motivation to change as one of the most reliable indicators of treatment readiness and engagement (De Leon et al., 1999; Heather et al., 1993; Hiller et al., 2002). Motivation to change evolves in five stages: pre-contemplation, contemplation, preparation, action, and maintenance (Prochaska & Diclemente, 1984). Where a patient is situated on this continuum at treatment entry has been linked to treatment outcomes and dropout rates (DiClemente et al., 2004; Heather et al., 1993; Klag et al., 2010; Simpson et al., 2002; Simpson et al., 1993). Thus, learning more about the factors leading to an increase in motivation to change is an important step in trying to improve the success of substance abuse treatment.

Families can play an important role in helping loved ones pursue treatment. Research has found that family support plays a considerable role in preventing substance use and in supporting recovering users following treatment (Dishion et al., 2003; Kumpfer et al., 2003). Expressed concern from one’s social network has also been found to greatly increase the likelihood of someone committing to treatment, reinforcing the impact family and friends can have on someone’s desire and ability to change (Pollini et al., 2006; Rapp et al., 2007). These findings all point towards the importance of families in tackling substance use issues, yet few studies have directly explored the association between family support and treatment entry.

Community Reinforcement and Family Training (CRAFT; Meyers & Wolfe, 2004), an intervention program highlighting the use of positive reinforcement from family members (referred to as Concerned Significant Others [CSO]) to get loved ones (referred to as Individual Patients [IP]) into treatment, has shown great promise in recent years (Brigham et al., 2014; Meyers et al., 2002; Roozen et al., 2010). CRAFT is a behavioral intervention aiming to both improve the CSO’s wellbeing and promote the IP’s treatment entry by transforming the IP’s environment (Kirby et al., 2017; Meyers et al., 2011). This happens through changes in the CSO’s communication, the use of positive reinforcement for sober behaviors, and a reduction of the CSO interfering with the consequences of substance use, among others (Meyers et al., 2011). CRAFT has been shown to be significantly more effective at engaging treatment-resistant loved ones than traditional approaches taught in Al-Anon Family Groups, a popular 12step program for family members and loved ones of substance abusers, which encourages family members to detach from their loved ones and accept their powerlessness in the face of the loved one’s illness (Brigham et al., 2014; Meyers et al., 2002; Roozen et al., 2010). The mechanisms through which CRAFT functions have scarcely been researched, but existing studies suggest that family support and motivation to change could play a significant mediating role (Dishion et al., 2003; Kumpfer et al., 2003; Meyers et al., 2011), since the CRAFT approach depends on the IP having a relationship with their family, and aims to push the IP to seek treatment of their own volition.

The association between family support, motivation to change, and treatment entry must be further researched so that families can further help the recovery of their loved ones. Hence, this study proposes to address the following research questions: What are the differential effects of CRAFT and Al-Anon on family support and motivation to change for individuals struggling with substance use disorders? To what extent is the effect of CRAFT (vs. Al-Anon) on SUD treatment entry mediated by levels of family support and motivation to change?

Proposed Method

Participants

The proposed study will include 300 substance-abusing individuals and their CSOs. All participants will be over 18 years old. CSOs should be a direct relative, spouse, or cohabitating romantic partner of the IP who is in regular contact with the IP. Participating substance users will fit the diagnostic criteria for a DSM-V Substance Use Disorder and not be in therapy or receiving pharmaceutical treatment for addiction. The sample will aim to be as representative of the general population as possible in terms of gender, education level, socioeconomic status, race and ethnicity.

Procedure

Participating CSOs and IPs will be recruited through flyers inviting family members of treatment-resistant substance abusers to participate in a six-month long treatment program, posted in community spaces of towns located in counties with high rates of drug use, as well as on Facebook groups for loved ones of substance abusers. In order to only include CSOs of treatmentresistant IPs, CSOs will be asked their IP’s hypothetical reaction to being asked to enter treatment. CSOs will be excluded if