Jop 2017 31 issue 1

Volume 31 / Number 1 / 2017

Volume 31 / Number 1 / 2017

Journal of

Psychophysiology

Journal of Psychophysiology

Editor-in-Chief Michael Falkenstein Editorial Board Monika Althaus Markus Breimhorst Tavis Campbell Istvan Czigler Patrick Gajewski Edward Golob Sien Hu Julian Koenig Cristina Ottaviani Patrick Papart Walter Sannita Henrique Sequeira Franck Vidal Jin-Chen Yang Juliana Yordanova

An International Journal

Contents Editorial

Higher Brain Function and the Laws of Thermodynamics: Hans Berger and His Time Walter G. Sannita

1

Articles

Test-Retest Reliability of Pediatric Heart Rate Variability: A Meta-Analysis Oren M. Weiner and Jennifer J. McGrath

6

Journal of Psychophysiology (2017), 31(1)

Differences in Pre-Attentive Processes of Sound Intensity Change Between High- and Low-Sensation Seekers: A Mismatch Negativity Study Siqi He, Yao Chai, Jinbo He, Yongyu Guo, and Risto Na¨a¨ta¨nen

29

Brief Report on the Psychophysiological Effects of a Yoga Intervention for Chronic Stress: Preliminary Findings Kaitlin N. Harkess, Paul Delfabbro, Jane Mortimer, Zara Hannaford, and Sarah Cohen-Woods

38

Ó 2017 Hogrefe Publishing

Editorial Higher Brain Function and the Laws of Thermodynamics Hans Berger and His Time Walter G. Sannita Department of Neuroscience, Ophthalmology, Genetics, Mother and Child Health, University of Genova, Genova, Italy

“I had arranged electrodes on the optic nerve of a toad in connection with some experiments on the retina. The room was nearly dark and I was puzzled to hear repeated noises in the loudspeaker attached to the amplifier, noises indicating that a great deal of impulse activity was going on. It was not until I compared the noises with my own movements around the room that I realised I was in the field of vision of the toad’s eye and that it was signalling what I was doing.” (Adrian, 1928) Comparable acknowledgements of the blessing of serendipity have been casually given, among others, by Alexander Fleming, David Hubel and Torsten Wiesel, and in the occasion of the discovery of mirror neurons (Cattaneo & Rizzolatti, 2009; Hubel & Wiesel, 1959, 1962, 1977; Rizzolatti & Craighero, 2004; Fadiga, personal communication). Edgar D. Adrian (1889–1977) promptly capitalized on serendipity by understanding that the retina modulates in frequency the action potential in transferring information to the brain (Adrian, 1928). Whether had he conceived this modality of signal transmission within the nervous system in ways consistent with the modern science of informatics is irrelevant: the very concept of neuronal coding provided novel quantitative basis in the investigation of nervous function and mechanisms. Adrian went further on in this line of research with a sequence of discoveries to be found ever since at the beginning of textbooks. His findings may somehow appear obvious today but have impacted as few others did – and have lead to modern neurophysiology and neuroscience. Nobel laureate in 1932 (with Charles S. Sherrington, whose merits had been denied for two decades by a stubborn member of the Nobel Committee), he eventually became the first baron Adrian of Cambridge in 1942.

In those days, interest was focused on the action potential – a relatively large, all-or-nothing event. Ó 2017 Hogrefe Publishing

Adrian’s recording system, brilliantly developed by Keith Lucas (1879–1916), made use of cathode ray tubes, capillary electrometers and thermionic valves to amplify action potentials to a modest 5,000 times. Investigation of amplitude-graded signals originating from neuronal systems in the human brain cortex required a different approach and more sensitive, reliable, non-invasive tools.

Hans Berger (1873–1941), a German psychiatrist with questioned experience in neurophysiology and none in electricity and methodology, was working in almost complete isolation to solve this problem (of which he was most probably unaware) while looking for something else.

He has been given universal credit for the discovery of human electroencephalography (EEG).

Others had already recorded mass brain signals at psycho-sensorial rest and in response to sensory stimulation in animals. The very first, physiologist Richard Caton, presented his results in 1875 in Liverpool, where he became the city mayor and Sherrington was appointed professor of physiology two decades later. Others replicated Caton’s work in Eastern Europe by the end of the century (Collura, 1993; Haas, 2003; Stone & Hughes, 2013). Only following Sherrington and Adrian’s work, however, research on electrophysiology expanded from the end of the 20s to the mid-30’s, especially in USA. In Germany, Oskar Vogt founded in 1931 (and the Rockefeller Foundation financed) a major research center in Berlin (the Kaiser-Wilhelm Institut fűr Gehirnforschung). Berger succeeded in recording mass brain signals first from animals (1902), then from patients with skull defects after neurosurgery, and finally from healthy volunteers (1924). He first used recording systems comparable to that of Keith and Adrian to move soon to an Edelmann string galvanometer (sensitivity: 1 mV/cm) and then to a Siemens galvanometer upgraded from the one used by Einthoven. Sensitivity (130 μV/cm) happened to be compatible with the average amplitude of

Journal of Psychophysiology (2017), 31(1), 1–5 DOI: 10.1027/0269-8803/a000192

2

Editorial

human brain signals and also allowed non-invasive recording through skin electrodes. Berger described the brain activity at rest (alpha rhythm ever since), the alpha blocking in response to eye opening, desynchronization after sensory stimulation and during cognitive activities (beta rhythm), and some EEG patterns of sleep and epilepsia (Brazier, 1957, 1964; Collura, 1993; Gloor, 1969a, 1969b; Haas, 2003; Stone & Hughes, 2013). The results of his work were published between 1929 and 1938 – all with the same title and a serial number, all in a German journal of psychiatry (Berger, 1929, 1938).

Berger was reportedly demure and reserved almost to invisibility, detached, badly under-esteemed by fellow psychiatrists in his country and unknown abroad. He had no experience in physiology or electricity (Millett, 2001; Schulte, 1959; Walter, 1953); in his time, clinicians (psychiatrists in particular) had mostly a humanistic background. Besides, his writing needed a careful decrypt and resulted nonetheless vague and erratic when he tried to interpret his experimental observations with inadequate physiological expertise.1 The Berlin group, whose electrophysiological technology was among the best available at the time, emphasized Berger’s drawbacks contemptuously. With arrogance, Alois E. Kornmüller dismissed the alpha rhythm as artifact and Berger’s whole work as misguided and irrelevant. His disdain was understandable, though: in those days, Willem Einthoven’s electrocardiogram was the golden standard of electrophysiology while neurophysiology was still contrived in mechanistic fashion: a large signal well sized at rest, but reduced during sensory stimulation or cognitive activities was simply unconceivable.

British neurophysiology strictly favored experimental procedures with a number of variables kept to a minimum, sometimes to single units, and therefore “. . .still could not accept the brain as a proper study for the physiologist. . .”

(Walter, 1953). Berger’s work was known, but almost completely disregarded. Frederick L. Golla (1878–1968), then at the Maudsley Hospital in London and future director of the Burden Neurological Institute in Bristol, 1

2

was the first to become interested, according to an account (Walter, 1953). Berger’s experience must have appealed to Adrian as a promising approach in the investigation of the temporal-spatial functional specialization of neocortex.

Brian Matthews had devised a sensitive and highly reliable amplification system (sensitivity: 10 μV/cm; high-frequency cutoff a 950 Hz, compressed to 64 Hz in the writing output) that allowed recording at unprecedented levels of signal and was the last word in accuracy.2 Adrian volunteered to serve as the recorded subject. Luckily enough, his alpha rhythm happened to be large and regular, whereas his young coworker William Grey Walter’s was of low amplitude and there would be no EEG had he been the subject (Walter, personal communication).

Adrian acknowledged Berger’s merits and in so doing also the work done before by others (Adrian & Matthews, 1934); his reputation made the validation unquestionable.

Technology developed rapidly and interest increased and spread, especially in USA (Stone & Hughes, 2013). Ease of use and peculiar non-invasiveness, as well as the identification of reliable EEG indicators of brain disorders (such as brain tumor and epilepsy) by Golla and Walter in Bristol, Herbert Jaspers, William Lennox, Frederic Gibbs, Hallowell Davis in USA and Canada, among others, promoted Berger’s methodology as a tool of excellence in diagnostic neurology. Clinical use prevailed as a result of the medical needs and due pragmatism, and electroencephalography was dominated for decades by the correlative study of transient waveforms and brain disorders (Hari & Salmelin, 2012; Schomer & Lopes da Silva, 2011). Research on the neuronal generating mechanisms developed in parallel (Creutzfeld, Watanabe, & Lux, 1996; Nuñez, 1989; 1998; Nuñez, Wingeier, & Silberstein, 2001) until upgraded computing technology allowed extensive application of two inceptions by W. Grey Walter barely conceivable at his time, namely the mathematical quantification of the background EEG signal and topography (Walter, 1953).

Fourier analysis allowed statistical processing, brain mapping the investigation of sources, therefore providing an approach that can benefit of combined application with

Berger became director of the Psychiatric clinic of Jena University in 1919, in this succeeding to Otto Binswanger whose previous coworker Oskar Vogt later demolished Berger’s work. Only at retirement in 1938 he became professor, but of psychology. The Nazis were hostile to him and his research, according to some sources. For others, he supported the SS and was member of the infamous Erbgesundheitsgericht (Court for Genetic Health). Berger’s isolation was probably the result of his (at the time extravagant) research rationale and weird methodologies, his ferocious introversion and his writing style. Communication is made difficult by sentences such as “. . .the energy liberated by the breakdown of complex chemical compounds will be transformed into heat, electrical processes and nervous processes which cause an enduring alteration of the exited (cerebral) area. . .”, or “(psychphysical processes associated to consciousness). . . cause enduring residual alterations of connections and centers (cerebral),” or “. . .in certain states . . . the cortex behaves as if it were energized by electrical power. . .” (Gloor, 1964). After visiting him in Jena, W. Grey Walter wrote that Berger: “. . .was not regarded by his associates as in the front rank of German psychiatrists, having rather the reputation of being a crank. He seemed to me to be a modest and dignified person, full of good humour, and as unperturbed by lack of recognition as he was later by the fame it eventually brought upon him. But he had one fatal weakness: he was completely ignorant of the technical and physical basis of his method. He knew nothing about mechanics or electricity. . .” (Walter, 1953). Co-workers found him selfhanged in a room of his former institute, early in the morning of June 1st, 1941.

Edgar D. Adrian is credited for remarking that: “. . .the history of electrophysiology has been decided by the history of electrical recording instruments. . .”.

Journal of Psychophysiology (2017), 31(1), 1–5

Ó 2017 Hogrefe Publishing

Editorial

magnetoencephalography and fMRI (Hansen, Kringelbach, & Salmelin, 2010; Hari & Salmelin, 2012). Extensive EEG research became thus possible in fields as diverse as neurology and psychiatry, sleep, neuropsychology and neuropharmacology, aging, cognition, psychophysiology, pathophysiology of consciousness, etc. (Carozzo, Fornaro, Garbarino, Saturno, & Sannita, 2006; Drinkenburg, Ruigt, & Ahnaou, 2015; Falkenstein, 2011; Gevins, Leong, Smith, Le, & Du, 1995; Goldstein & Chervin, 2016; Meucci & Sannita, 2010; Monti & Sannita, 2016; Schomer & Lopes da Silva, 2011; Yordanova & Kolev, 2009).

Hans Berger had misdirected his efforts. His intent appears to have been how to measure the energy consumed by sensory processing and cognition (Gloor, 1969a, 1969b, 1994). No technology available at the time was minimally compatible with such a task, and Berger candidly made use of what at hand, no matter how primitive; he studied (qualitatively) the pulsatility of brain arteries. Sherrington had investigated brain circulation and its correlation to brain function 35 years. before in animal experimental setups (Roy & Sherrington, 1890). Berger tried to measure the changes in pulsation of the cortical arteries of patients with skull defect after neurosurgery or head trauma.

He was most likely observing changes in the volume of total brain circulation, but noted nevertheless that arterial pulsations increased after cocaine and in concomitance with sensory stimulation or simple cognitive activities, whereas no changes were detectable in the arterial circulation outside the skull (quoted by Gloor, 1994). Such approach appears today bizarre. However, the concept itself of correlation between regional brain activation and increase in the demand of oxygen (and therefore blood) supply first hypothesized by Roy and Sherrington is at the very ground of modern neuroimaging technologies (Huettel, Song, & McCarthy, 2009; Logothetis, 2008; Raichle, Toga, & Mazziotta, 2000). Disappointed by the variability of the plethysmographic response, Berger turned to an even more crude and unpromising approach by trying to measure the variations in local brain temperature during sensory or cognitive tasks. Again, he used what readily available, for example, mercury thermometers inserted to contact the brain surface through skull holes in local anesthesia. Disappointed again, he resorted to electricity, electrodes and string galvanometers. In this, he was following a cultural line originated long before from the unexpected evidence that animal tissues can generate electricity, of which we usually remember only the contributions by Luigi Galvani and Emil Du Bois-Reymond (Collura, 1993; Haas, 2003). Berger knew nothing of electricity (Walter, 1953),1 but Kornmüller’s remark that he ignored electrophysiology completely should be reconsidered in perspective. The notion of recordable electrophysiological events occurring in neuronal tissues was Ó 2017 Hogrefe Publishing

3

relatively widespread already in those days. Willem Einthoven received a Nobel award for his continuous research on the electrocardiogram sources in 1924, right when Berger began human EEG recordings with comparable methodologies. Einthoven and others had independently investigated the electrical response of the retina by the turn of the 19th century, and some debate was still going on in Berger’s time about the relationship between stimulus duration and response amplitude (Armington, 1974; Einthoven & Jolly, 1908; Gotch, 1903; Holmgren, 1865). Besides, Berger certainly had contacts with the scientific community since joining the group lead by eminent Otto Biswanger, whose position in Jena he eventually held from 1919 to retirement. Kornmüller and Adrian became promptly aware of Berger’s work and it is hard to believe he was fully unaware of theirs’. His goals were others, though; once again he did not obtain what he was looking for, but in the process of failing gave science a nearly epochal discovery.

Biochemistry had discovered that cell metabolism (and function therefore) was regulated by processes that require relevant amounts of energy and thus follow the laws of thermodynamics. The evidence of such contiguity between processes and mechanisms pertaining to physics and those peculiar to animal life was a tremendous change in those times scenario, but could be assimilated, although with intellectual effort. Physics and physiology had common ancestors – and, after all, the evidence concerned the liver, heart, muscles, kidneys, digestion, respiration. With greater effort could become acceptable even that brain neurons compare to thermal machines, but only to the extent brain functions could be categorized into a classification system in which “elementary” or “higher” (cognitive) brain functions be held as different. We are aware today that the border between cognitive and non-cognitive is often arbitrary and any ranking of brain functions in terms of relevance is useless. In Berger’s time, modern psychology allowed a categorization of brain activities simply by linking them to the observation modes, irrespective of their complexity in the neurophysiological domain. However, Hobbes’ position was enduring tradition in British culture, but Descartes’ view of mental operations having no physical or space substance was still alive. Even today the so-called mind-body problem remains largely unresolved (Agassi, 2007; Nagel, 1993; Robinson, 2003; Searle, 2004; Sperry, 1980). John Eccles and Karl Popper have revived the Cartesian dualism by complicating it further into a trialistic vision separating perception and consciousness from intellectual activities, and accused detractors (perhaps not without reasons) for “embracing monism in order to escape the enigma of brain-mind interaction with its perplexing problems” (Eccles & Popper, 1977). Because of the discrepancy (and ambiguity) resulting Journal of Psychophysiology (2017), 31(1), 1–5

4

Editorial

from parallel independent approaches, for instance, we are still debating whether consciousness is different from, and independent of other brain functions as it appears (Monti & Sannita, 2016).

Berger maintained that “psychophysiological activity” is a functional unit of brain cortex (“ein einheitliches Ganzes”).3 Pierre Gloor has derived from Berger’s writings that he could not accept the dominant dualism of his time and some immaterial entity interacting with biological matter in the brain was as unacceptable to him as it was a mind-body problem treated independently of the laws of physics (Gloor, 1994; Millett, 2001). His rationale was based on the insight that all brain processes be sustained by cell metabolism and therefore blood supply, with some energy transfer; in this respect, Berger would have been intellectually more or less where we are today, according to Gloor (Gloor, 1994). The interpretation appears quite hindsight, though, as there is no indication about Berger’s awareness of the tremendous development of science (especially physics) during the first decades of the 20th century and his intent to transfer those concepts into medical neuroscience. Neuroscience was in its infancy, more so for a clinician with humanistic background working in Jena. One example of the turmoil neuroscience was going through in Berger’s times is synapses – imagined by Ramon y Cajal (1902) to oppose Camillo Golgi’s theory and documented only decades later, after a long-lasting controversy between the chemical or electrical nature of neuronal transmission that was crucial in the 20th century neuroscience (Eccles, 1964). Gloor’s suggestion that Berger was somehow a conceptual forerunner (frustrated by technological inadequacy) of today’s neuroimaging techniques (PET scan, fMRI) appears more benevolent than substantial, motivated mostly by the high regard due to a methodology (electroencephalography) that has been innovative and remains powerful and reliable in the functional investigation of brain function and in clinical neurology.

Research in the first decades of the 20th century often appears both ambitious and naïve to our modern scientific eyes, somehow bizarre. The idea of some psychic energy independent of brain mechanisms and function, perhaps immaterial, looked probably more acceptable in Berger’s time than we may estimate today. It is unclear to which extent Berger meant to kill it, but he certainly helped.

3

Serendipity is a blessing only to those who can make proper use of it. The extent to which such a merit should be recognized to Berger as it has been the case with Adrian, Fleming and others can be a matter of debate. That electroencephalography has been developed by mistake while looking for something else is after all irrelevant.

References Adrian, E. D. (1928). The basis of sensation. New York, NY: WW Norton.

Adrian, E. D., & Matthews, B. H. C. (1934). The Berger rhythm, potential changes from the occipital lobe in man. Brain, 57, 345–359.

Agassi, J. (2007). The changing features of the body-mind problem. Review. The Journal of Physiology, 101, 153–160.

Armington, J. C. (1974). The electroretinogram. New York, NY: Academic Press.

Berger, H. (1929). Über das Elektrenkephalogramm des Menschen [On the electroencephalogram of men]. Archiv für Psychiatrie und Nervenkrankheiten, 87, 527–570.

Berger, H. (1938). Über das Elektrenkephalogramm des Menschen [On the electroencephalogram of men] (14th report). Archiv für Psychiatrie und Nervenkrankheiten, 108, 407–431.

Brazier, M. A. (1957). Electroencephalography. Progress in Neurology and Psychiatry, 12, 262–276.

Brazier, M. A. (1964). The electrical activity of the nervous system.

Science, 146, 1423–1428.

Carozzo, S., Fornaro, S., Garbarino, S., Saturno, M., & Sannita, W. G. (2006). From neuroscience to application in neuropharmacology: A generation of progress in electrophysiology. Clinical EEG and Neuroscience, 37, 121–134.

Cattaneo, L., & Rizzolatti, G. (2009). The mirror neuron system.

Archives of Neurology, 66, 557–560.

Collura, T. F. (1993). History and evolution of electroencephalographic instruments and techniques. Journal of Clinical Neurophysiology, 10, 476–504.

Creutzfeld, O. D., Watanabe, S., & Lux, H. (1996). Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and epicortical stimulation. Electroencephalography and Clininical Neurophysiology, 20, 1–18.

Drinkenburg, W. H., Ruigt, G. S., & Ahnaou, A. (2015). PharmacoEEG studies in animals: An overview of contemporary translational applications. Neuropsychobiology, 72, 151–164.

Eccles, J. C. (1964). The physiology of synapses. Berlin, Germany: Springer.

Eccles, J. C., & Popper, K. R. (1977). The self and its brain. Berlin, Germany: Springer.

Einthoven, W., & Jolly, W. A. (1908). The form and magnitude of the electrical response of the eye to stimulation by light at various intensities. Quarterly Journal of Experimental Psychology, 1, 373–416.

To explain the exceptional development (and the apparent qualitative discontinuity) of higher brain function in humans simply on the ground of the enormous increase in neuronal number, synaptic connections and integration appeared questionable already to Sherrington more than one century ago: “. . . To describe the action of nerve as integrative is, although true, hardly insufficient for a definition. If the nature of an animal be accepted as being that of a whole presumable by all its parts, then each and every part . . . is integrative. (Cancer, the growth of which being outside the integrative plan of the body is destructive both to the normal body and itself.) Our search for a more satisfying definition of nerve has then to ask what is the specific contribution which nerve makes to the animal integration. . .”. (Sherrington, 1906). More recently it has been noted that the “. . .increasing size of the brain does not explain it. . .” (Walter, 1953).

Journal of Psychophysiology (2017), 31(1), 1–5

Ó 2017 Hogrefe Publishing

Editorial

Falkenstein, M. (2011). Early and recent trends in psychophysiology. Journal of Psychophysiology, 25, 159–163. doi: 10.1027/ 0269-8803/a000058 Gevins, A., Leong, H., Smith, M. E., Le, J., & Du, R. (1995). Mapping cognitive brain function with modern high-resolution electroencephalography. Trends in Neurosciences, 18, 429–436.

Gloor, P. (1969a). The work of Hans Berger. Encephalography and Clinical Neurophysiology, 27, 649.

Gloor, P. (1969b). Hans Berger and the discovery of the electroencephalogram. Encephalography and Clinical Neurophysiology, Suppl. 28, 1–36.

Gloor, P. (1994). Berger lecture. Is Berger’s dream coming true?

Encephalography and Clinical Neurophysiology, 90, 253–266.

Goldstein, C., & Chervin, R. (2016). Waking up to sleep research in 2015. Lancet Neurology, 15, 15–17.

Gotch, F. (1903). The time relations of the photoelectric changes on the eyeball of the frog. The Journal of Physiology, 29, 388–416.

Haas, L. F. (2003). Hans Berger (1873–1941), Richard Caton (1842–1926), and electroencephalography. Journal of Neurology, Neurosurgery, and Psychiatry, 74, 9.

Hansen, P. C., Kringelbach, M. L., & Salmelin, R. (Eds.). (2010).

MEG. An introduction to methods. New York, NY: Oxford University Press.

Hari, R., & Salmelin, R. (2012). Magnetoencephalography: From SQUIDs to neuroscience. Neuroimage 20th anniversary special edition. NeuroImage, 61, 386–396.

Holmgren, F. (1865). Metod att objektivera effektenav ljusintryck pa retina [An objective method to study the light effect on the retina]. Upsala Läkareförenings Förhandlingar, 1, 177–191.

Hubel, D. H., & Wiesel, T. N. (1959). Receptive fields of single neurones in the cat’s striate cortex. The Journal of Physiology, 148, 574–591.

Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160, 106–154.

Hubel, D. H., & Wiesel, T. N. (1977). Ferrier lecture. Functional architecture of macaque monkey visual cortex. Proceedings of The Royal Society B Biological Sciences, 198, 1–59.

Huettel, S. A., Song, A. W., & McCarthy, G. (2009).

Functional magnetic resonance imaging (2nd ed.). Sunderland, MA: Sinauer.

Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869–878.

Meucci, R. & Sannita, W. G. (Eds.). (2010). Consciousness and its descriptors. Journal of Psychophysiology, 24, 1–148.

Millett, D. (2001). Hans Berger: From psychic energy to the EEG. Perspective in Biology and Medicine, 44, 522–542.

Monti, M. M. & Sannita, W. G. (Eds.). (2016). Brain function and responsiveness in the disorders of consciousness. New York, NY: Springer.

Nagel, T. (1993). What is the mind-body problem? Ciba Foundation Symposium, 174, 1–7.

Nuñez, P. L. (1989). Generation of human EEG by a combination of long and short range neocortical interactions. Brain Topography, 1, 199–215.

Ó 2017 Hogrefe Publishing

5

Nuñez, P. L. (1998). Neocortical dynamics of macroscopic-scale EEG measurements. IEEE Engineering in Medicine and Biology Magazine, 17, 110–117.

Nuñez, P. L., Wingeier, B. M., & Silberstein, R. B. (2001).

Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks. Human Brain Mapping, 13, 125–164.

Raichle, M. E., Toga, A. W., & Mazziotta, J. C. (Eds.). (2000). Brain mapping: The systems. London, UK: Academic Press.

Ramon y Cajal, S. (1902). Textura del sistema nervioso del hombre y de los vertebrados [Texture of the nervous system of man and vertebrates]. Madrid, Spain: Imprenta y Libreria de Nicola´s Moya.

Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system.

Annual Review of Neuroscience, 27, 169–192.

Robinson, H. (2003). Dualism. In S. Stich & T. Warfield (Eds.), The Blackwell guide to philosophy of mind (pp. 85–101). Oxford, UK: Blackwell.

Roy, C. S., & Sherrington, C. S. (1890). On the regulation of the blood-supply of the brain. The Journal of Physiology, 11, 85–158.

Schomer, D. L. & Lopes da Silva, F. H. (Eds.). (2011).

Niedermeyer’s electroencephalography: Basic principles, clinical applications, and related fields (6th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.

Schulte, W. (1959). Hans Berger: A biography of the discoverer of the electroencephalogram. Münchener Medizinische Wochenschrift, 22, 977–980.

Searle, J. (2004). Mind: A brief introduction. Oxfort, UK: Oxford University Press.

Sherrington, C. S. (1906). The integrative action of the nervous system. New Haven, CT: Yale University Press.

Sperry, R. W. (1980). Mind-brain interaction: Mentalism, yes; dualism, no. Neuroscience, 5, 195–206.

Stone, J. L., & Hughes, J. R. (2013). Early history of electroencephalography and establishment of the American Clinical Neurophysiology Society. Journal of Clinical Neurophysiology, 30, 28–44.

Walter, W. G. (1953). The living brain. New York, NY: WW Norton.

Yordanova, J., & Kolev, V. (2009). Event-related brain oscillations: Developmental effects on power and synchronization. Journal of Psychophysiology, 23, 174–182. doi: 10.1027/0269-8803.

23.4.174

Walter G. Sannita Department of Neuroscience, Ophthalmology, Genetics, Mother and Child Health University of Genova 3, largo p. Daneo 16132 Genova Italy wgs@dism.unige.it

Journal of Psychophysiology (2017), 31(1), 1–5

Article

Test-Retest Reliability of Pediatric Heart Rate Variability A Meta-Analysis Oren M. Weiner and Jennifer J. McGrath Pediatric Public Health Psychology Laboratory, Concordia University, Montreal, Quebec, Canada Abstract: Heart rate variability (HRV), an established index of autonomic cardiovascular modulation, is associated with health outcomes (e.g., obesity, diabetes) and mortality risk. Time- and frequency-domain HRV measures are commonly reported in longitudinal adult and pediatric studies of health. While test-retest reliability has been established among adults, less is known about the psychometric properties of HRV among infants, children, and adolescents. The objective was to conduct a meta-analysis of the test-retest reliability of time- and frequencydomain HRV measures from infancy to adolescence. Electronic searches (PubMed, PsycINFO; January 1970–December 2014) identified studies with nonclinical samples aged 18 years; 2 baseline HRV recordings separated by 1 day; and sufficient data for effect size computation. Forty-nine studies (N = 5,170) met inclusion criteria. Methodological variables coded included factors relevant to study protocol, sample characteristics, electrocardiogram (ECG) signal acquisition and preprocessing, and HRV analytical decisions. Fisher’s Z was derived as the common effect size. Analyses were age-stratified (infant/toddler < 5 years, n = 3,329; child/adolescent 5–18 years, n = 1,841) due to marked methodological differences across the pediatric literature. Meta-analytic results revealed HRV demonstrated moderate reliability; child/adolescent studies (Z = 0.62, r = 0.55) had significantly higher reliability than infant/toddler studies (Z = 0.42, r = 0.40). Relative to other reported measures, HF exhibited the highest reliability among infant/toddler studies (Z = 0.42, r = 0.40), while rMSSD exhibited the highest reliability among child/adolescent studies (Z = 1.00, r = 0.76). Moderator analyses indicated greater reliability with shorter test-retest interval length, reported exclusion criteria based on medical illness/condition, lower proportion of males, prerecording acclimatization period, and longer recording duration; differences were noted across age groups. HRV is reliable among pediatric samples. Reliability is sensitive to pertinent methodological decisions that require careful consideration by the researcher. Limited methodological reporting precluded several a priori moderator analyses. Suggestions for future research, including standards specified by Task Force Guidelines, are discussed.

Keywords: pediatric, heart rate variability, reliability, respiratory sinus arrythmia, psychometric

Heart rate variability (HRV) reflects the variance in time between consecutive sinoatrial depolarizations (i.e., NNintervals) and is an established index of autonomic cardiovascular modulation. Commonly reported time- and frequency-domain HRV measures, typically derived using continuous electrocardiogram (ECG) recordings and specialized analysis software programs, each provide a unique and nuanced perspective of autonomic functioning (Ernst, 2014; Kleiger, Stein, & Bigger, 2005).

Mathematically derived time-domain measures include the standard deviation of NN-intervals (SDNN; reflecting parasympathetic, sympathetic, and circadian influences), the root mean square of successive NN-interval differences (rMSSD), and percentage of successive NN-intervals that differ by > 50 ms (pNN50); these latter indices both reflect parasympathetic influences (Malik, 1997). Frequencydomain analyses decompose NN-intervals into sinusoidal waveforms based on preestablished frequency bandwidths (Berntson, Quigley, & Lozano, 2007). Standard adult HRV frequency bandwidths are defined as High Frequency Journal of Psychophysiology (2017), 31(1), 6–28 DOI: 10.1027/0269-8803/a000161

(HF 0.15–0.40 Hz), Low Frequency (LF 0.04–0.15 Hz), Very Low Frequency (VLF 0.0033–0.04 Hz), and Ultra-Low Frequency (ULF < 0.0033 Hz; Berntson et al., 1997; Task Force, 1996). Studies using pharmacological blockade demonstrate that HF (or respiratory sinus arrhythmia, RSA) chiefly reflects parasympathetic and respiratory influences (Akselrod et al., 1981; Berntson, Cacioppo, & Quigley, 1993; Cacioppo et al., 1994; Chen & Mukkamala, 2008).

Physiological mechanisms underlying LF, VLF, and ULF have also been studied using pharmacological blockade (e.g., Akselrod, Eliash, Oz, & Cohen, 1985; Akselrod et al., 1981; Cacioppo et al., 1994), but are less well established.

VLF and ULF are rarely reported in pediatric studies. LF: HF ratio can also be derived; however, its interpretation as reflecting autonomic balance is frequently debated, largely because both autonomic branches contribute to LF, and autonomic activity is not exclusively reciprocal (e.g., Berntson, Cacioppo, & Quigley, 1991; Reyes del Paso, Langewitz, Mulder, van Roon, & Duschek, 2013). As such, LF:HF more likely reflects overall autonomic modulation. Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Decreased SDNN, HF, LF, VLF, and LF:HF are indicative of poor health and worse outcomes among conditions such as cardiac arrhythmia, obesity, hypertension, Type 1 and Type 2 diabetes, and psychological disorders in both adult (e.g., Boer-Martins et al., 2011; Henry, Minassian, & Paulus, 2010; Schmid, Schönlebe, Drexler, & MueckWeymann, 2010; Thayer & Sternberg, 2006) and child samples (e.g., Akinci, Celiker, Baykal, & Tezic, 1993; Baharav, Kotagal, Rubin, Pratt, & Akselrod, 1999; El-Sheikh & Hinnant, 2011; Martini et al., 2001; Xie et al., 2013). Considering its implications for health, HRV must be accurately measured, analyzed, and interpreted to minimize erroneous and potentially detrimental conclusions. Meaningful inferences about a relation between temporal changes in HRV and health require evidence for test-retest reliability in adults and children; however, the psychometrics of pediatric HRV are not well established.

HRV Measurement, Analysis, and Reporting: Implications for Test-Retest Reliability Two committee reports have provided guidelines for HRV measurement and analysis (Berntson et al., 1997; Task Force, 1996). While these guidelines are frequently cited in the HRV literature, wide variability in study methodology remains. Further, available guidelines do not reflect theoretical and methodological advances in HRV measurement within the last two decades (e.g., Cerutti, Goldberger, & Yamamoto, 2006; Denver, Reed, & Porges, 2007), and do not account for distinctions in pediatric HRV measurement (e.g., Bar-Haim, Marshall, & Fox, 2000). Pertinent methodological considerations for HRV test-retest reliability are reviewed below and are conceptually organized into four categories: (i) study protocol; (ii) sample characteristics; (iii) ECG signal acquisition and preliminary processing; and (iv) HRV analyses. These categories coincide with milestones of salient methodological decisions.

Study Protocol Study protocol methodological decisions include length of the follow-up period or test-retest interval; use of a standardized recording protocol; and, recording time of day and related waking state. Declining reliability with longer test-retest intervals is not unexpected (Cohen & Swerdlik, 2002), and has been observed among young adults (e.g., Cipryan & Litschmannova, 2013) and among infants and youth. For example, Perry and colleagues (2013) observed declining HF reliability in toddlers across 1 to 2 year follow-up intervals (r = 0.52; r = 0.34, respectively). El-Sheikh and Hinnant (2011) similarly observed declining HF reliability in children across 1 to 2 to 3 year follow-up intervals Ó 2016 Hogrefe Publishing

7

(r = 0.63; r = 0.54; r = 0.32, respectively). Another factor that can influence HRV reliability is the extent of protocol standardization. Namely, test-retest reliability can be optimized by restricting participant behaviors that affect autonomic physiology (e.g., exercise, caffeine intake, postural changes), and by standardizing the ECG recording protocol across all participants and study assessments (cf., Jennings et al., 1981). Notedly, researchers strive to balance testretest reliability with ecological validity, which may diminish with more rigorous study protocols.

Guidelines do not specify an ideal time of day for recording ECG, leaving this study protocol decision to the discretion of researchers. Circadian variations in HRV have been observed in adults (e.g., Armstrong, Kenny, Green, & Seely, 2011), children (e.g., Massin, Maeyns, Withofs, Ravet, & Gerard, 2000), and infants as early as 7–12 weeks old (Hoppenbrouwers et al., 2012). Sympathetic dominance peaks just after awakening and withdraws during the day, while parasympathetic, or vagal, dominance becomes augmented throughout the night, reaching its peak before awakening (Guo & Stein, 2003; Huikuri et al., 1990).

Pediatric studies report mean-level changes in HF, VLF, and LF:HF across day, night, and 24-hr recordings (Faulkner, Hathaway, & Tolley, 2003); however, few studies have examined how recording time influences HRV reliability. Further, waking state (awake vs. napping) during daytime hours is a relevant issue for studies conducted with infants and toddlers.

Sample Characteristics Sample characteristics relevant to HRV reliability include age or developmental span; study exclusion criteria; and, participant biological sex. Sample heterogeneity can result from recruiting participants across a wide age range (e.g., 8–18 years), or with poorly defined, or lack of fidelity to, exclusion criteria (e.g., medical illness/condition, medication use). Evidence for HRV reliability in a homogenous sample is necessary to establish the validity of HRV measures obtained from that sample. Heterogeneous samples that span wider age ranges may actually limit reliability attributable to normal HRV changes across developmental periods. For example, lower test-retest HF reliability was observed for samples spanning from 2 months to 5 years of age (r = .30, Bornstein & Suess, 2000b) and from 5 to 14 years of age (Spearman’s ρ = .26; Gentzler, Rottenberg, Kovacs, George, & Morey, 2012). Relatedly, lower 2-year test-retest HRV reliability has been reported among toddlers, compared to children (e.g., 3–5 years: r = 0.34, Perry et al., 2013; 8–10 years: r = 0.54, El-Sheikh & Hinnant, 2011). Study exclusion criteria are often less rigorous or unspecified among the pediatric HRV literature, relative to the adult literature (e.g., Kennedy, Rubin, Hastings, & Maisel, 2004; Rigterink, Fainsilber Katz, Journal of Psychophysiology (2017), 31(1), 6–28

8

& Hessler, 2010). Medications (e.g., antiarrhythmic agents, beta-blockers) that have been associated with lower meanlevel LF, HF, LF:HF, and SDNN in adults (Penttilä, Kuusela, & Scheinin, 2005; Shaffer & Combatalade, 2013; Task Force, 1996) have been rarely examined in relation to HRV, or HRV reliability, among children (e.g., Buchhorn et al., 2012). Conversely, several acute and chronic illnesses (e.g., obesity, diabetes) are associated with declines in sympathovagal balance among clinical samples of adults and children, relative to nonclinical samples (e.g., Kochiadakis et al., 1997; Latchman et al., 2011; Martini et al., 2001; Sandercock, Bromley, & Brodie, 2005). Thus, sample homogeneity is relevant for reliability.

Sex differences in HRV have been observed in adults and children, although results are often inconsistent. Adult males typically display greater sympathetic dominance (e.g., higher LF, LF:HF), while females typically display greater parasympathetic dominance (e.g., higher HF, rMSSD; Antelmi et al., 2004) and higher HRV reliability (Sookan & McKune, 2012). Conversely, while boys have displayed increased mean-level NN and SDNN relative to girls (Faulkner et al., 2003; Silvetti, Drago, & Ragonese, 2001), male sex has also been associated with higher HF among 9- and 11-year-olds (El-Sheikh, 2005). There may be sex-specific shifts in sympathovagal balance during childhood development related to changes in sex hormone concentrations (e.g., testosterone, estrogen), which in turn, exert distinct, sex-specific effects on blood pressure and heart rate (Spear, 2000). Evidence suggests that sex differences in HRV reliability may exist among both adults and children; however, pediatric studies examining biological sex differences in HRV reliability are rare.

Electrocardiogram (ECG) Signal Acquisition and Preprocessing Electrocardiogram signal acquisition and preprocessing decisions include length of the acclimatization period; recording posture; ECG sampling rate; and, signal filtering.

Allowing participants to acclimate to their surroundings prior to ECG recording is recommended by HRV measurement guidelines (Berntson et al., 1997; Task Force, 1996). Resting HRV measures index baseline autonomic cardiovascular control and serve as a reference from which to compare change. However, participants instructed to rest quietly during the baseline recording may not actually be fully at rest (i. e., anxious, humming quietly, ruminating) if they did not have time to adequately habituate. A prerecording acclimatization period following the application of electrodes and prior to baseline recording helps participants to familiarize with their surroundings and the physiological sensors, and reduces stress-related changes in physiological activity prior to data collection; this may be especially true among infants and young children. Sharpley (1993) noted that a 15 min Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

acclimatization period duration may be sufficient among adults, but advocated for durations that are optimal for each participant. Including a prerecording acclimatization period may augment HRV reliability by reducing unsystematic variation in resting heart rate activity; however, acclimatization periods distinct from baseline are uncommon, or are not routinely reported, among pediatric studies.

Parasympathetic dominance is often observed in seated or supine postures, while sympathetic dominance is often observed in standing or head-up-tilt postures (Cacioppo et al., 1994; Kleiger et al., 2005); thus, HRV, and HRV reliability, are influenced by postural position. HF reliability, but not LF reliability, was notably higher among adults measured across 24 months in supine versus standing postures (ICCHF = 0.89 vs. 0.79; ICCLF = 0.81 vs. 0.79; Kowalewski & Urban, 2004); similar results were obtained among children for heart rate and SDNN measured across 2 weeks (ICCHR = 0.78 vs. 0.65; ICCSDNN = 0.79 vs. 0.69; Dietrich et al., 2010), and among adolescents for HF and LF measured across 1 year (rHF = 0.37 vs. 0.25; rLF = 0.37 vs. 0.31; Mezzacappa et al., 1997). Considering these findings, sympathetic modulation may be less affected by postural changes than parasympathetic modulation. Shifts in sympathovagal balance across childhood development (Spear, 2000) highlight a salient consideration when examining the influence of recording posture on pediatric HRV reliability.

Electrocardiogram sampling rate may be pertinent to the reliability and accuracy of HRV data. Guidelines recommend 500–1,000 Hz as optimal for HRV sampling, although 250 Hz may also be acceptable (Berntson et al., 1997; Task Force, 1996). Studies that have empirically examined error variation attributable to ECG sampling rate are largely based on small sample sizes (e.g., N = 1–5), simulated comparisons, or evaluation of lower sampling rates (e.g., 64 Hz, 128 Hz, 256 Hz; cf., Merri, Farden, Mottley, & Titlebaum, 1990; Singh, Vinod, & Saxena, 2004; Wittling & Wittling, 2012). Other studies have suggested that sampling rates above 500 Hz may not have incremental utility (cf., Riniolo & Porges, 1997; Singh, Singh, & Banga, 2014).

Sampling rates that are too slow, or even too fast, may reduce R-wave timing precision, contribute unwanted variance, and limit reproducibility. The ideal sampling rate also may be sample-specific (Merri et al., 1990; Singh et al., 2004) and require adjustment to account for temporal changes in age and weight, for example.

Few studies report the proportion of data excluded due to artifacts. This sharply contrasts with guideline recommendations and restricts knowledge about the integrity, quality, and interpretability of HRV data. The effects of poor continuity and stationarity of ECG signals due to artifacts (e.g., poor electrode adhesion, movement, aberrant heartbeats) are known and have been widely considered using real Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

and simulated HRV data (Berntson & Quigley, 1990; Berntson & Stowell, 1998; Kim, Lim, Kim, & Park, 2007; Salo, Huikuri, & Seppänen, 2001). Thus, manually editing ECG data prior to HRV analysis is highly relevant for HRV reliability. Digital automated filtering of noise and overlapping frequency components (i.e., spectral leakage) is also pertinent. Porges’ (1985) moving polynomial filter is commonly used in pediatric HRV studies to remove background heart rate trends and may eliminate the need to statistically control for heart rate. HRV reliability in the context of different (or absent) automated filtering algorithms has been examined among adults (Lee & Chiu, 2010; Singh et al., 2004), but rarely in children.

Heart Rate Variability Analyses Heart rate variability analyses include data reduction decisions pertaining to length of the recording duration analyzed; epoch length; and, frequency bandwidth selection.

These HRV analyses occur post-ECG signal collection and prior to commencing statistical analyses. Choosing the ECG recording duration to be analyzed for HRV is pertinent for capturing the extent of short- and long-term variability within a signal, and for the reliability of resulting measures.

Guidelines specify that ECG be recorded for at least 1 min to assess HF, 2 min for LF, and 50 min for VLF. However, as 2 min may not be enough to derive reliable estimates of LF (Heathers, 2014; Kleiger et al., 2005), 5 min recording durations are generally accepted as a standard in short-term studies (Berntson et al., 1997; Task Force, 1996). Among adults, LF reliability was higher when derived from 5 min, compared to 2.5 min recordings (ICC = 0.82 vs. 0.78; Marks & Lightfoot, 1999). Among infants, recordings 3 min produced more reliable measures of HF (Richards, 1995). It is plausible that reliability of some measures may decrease with longer recording durations (e.g., NN), while others increase (e.g., VLF) or remain unaffected (e.g., LF:HF). Comparing identical length recording durations is relevant when examining HRV reliability (Dalla Pozza et al., 2006).

An ECG recording can be analyzed for HRV using the mean of a single analytical epoch or by analyzing the mean of multiple shorter epochs. For example, a 30 min recording can be analyzed as thirty 1 min epochs, six 5 min epochs, or one 30 min epoch, among several other epoch lengths. Deriving HRV using multiple epochs may reduce the impact of uncorrected artifacts or slower heart rate trends (Izard et al., 1991; Salo et al., 2001), but may introduce a selection bias, or a loss of variability that oscillates at periods longer than the analyzed epoch (Berntson et al., 1997, 2007; Porges & Byrne, 1992). McNames and Aboy (2006) demonstrated that adult HF was reproducible across epochs ranging from 10 s to 10 min, while LF required at least 10 min epochs. Richards (1995) observed Ó 2016 Hogrefe Publishing

9

that infant HF was more reliable when a 75 s recording was analyzed in fifteen 5 s epochs, rather than five 15 s epochs. Thus, epoch selection is germane to pediatric HRV reliability and should be explicitly reported to enhance the interpretability of results.

Heart rate variability frequency bandwidths in pediatric samples differ from those defined for adults, due to young children’s higher respiration rate. Namely, the HF bandwidth should be modified to include the respiratory frequency of the pediatric sample (0.3–1.30 Hz infants; 0.24– 1.04 Hz young children; Bar-Haim et al., 2000; Fox & Porges, 1985). If adult bandwidths are erroneously applied to pediatric frequency-domain measures, which is not uncommon, resulting data will inaccurately estimate autonomic cardiovascular control and distort subsequent interpretations. Spectral frequency bands should be carefully considered and explicitly reported to enhance the interpretability of results. Frequency bandwidth selection is likely pertinent to HRV test-retest reliability; however, few studies have specifically examined this, especially among children and adolescents.

Current State of Knowledge and Study Aims There is considerable evidence for moderate to excellent reliability of HRV measures during controlled resting conditions in healthy adult participants (cf., Sandercock et al., 2005, for systematic review). Relatively less evidence is available from studies with infants, children, and adolescents. Reliable HRV measurement requires careful consideration of multiple methodological factors (e.g., Jarrin, McGrath, Giovanniello, Poirier, & Lambert, 2012), many of which are detailed in frequently cited HRV measurement guidelines. There is a notable lack of conformity to these guidelines, which increases potential for error upon replication, limits the interpretability of results, and makes it challenging to draw meaningful comparisons about HRV reliability across different studies. Considering the relative lack of knowledge regarding pediatric HRV psychometrics reviewed above, the overarching aim of this meta-analysis was to systematically examine and empirically synthesize available evidence for test-retest reliability of time- and frequency-domain HRV measures from infants, children, and adolescents. Methodological decisions that could influence HRV reliability were examined as possible moderators, including (a) study protocol decisions (i.e., study follow-up length; consistent time of day recording; waking status), (b) sample characteristics (i.e., explicit exclusion criteria; proportion of male participants); (c) ECG signal acquisition and preprocessing settings (i.e., prerecording acclimatization period; recording posture; sampling rate; filtering algorithm); and (d) HRV analytical decisions (i.e., analyzed Journal of Psychophysiology (2017), 31(1), 6–28

10

recording duration; epoch lengths; frequency bandwidth selection). Finally, given reported developmental differences, HRV reliability was compared between younger versus older children.

Method Literature Search Strategy An electronic literature search was performed within PsycINFO and PubMed (MedLine) databases from January 1, 1970 to December 31, 2014, using terms regarding pediatric populations (e.g., infants, children, adolescent), HRV measures (e.g., HF, RSA, SDNN), and psychometrics (e.g., reliability, test-retest, reproducibility). This search yielded 579 nonredundant journal articles; review of their titles indicated 347 were possibly relevant due to mention of HRV measures. Next, the abstracts of these 347 articles were reviewed and selected for follow-up if they suggested that HRV was measured at least twice from nonclinical pediatric or age-unspecified samples; 88 articles met this criterion. Then, ascendancy and descendancy approaches (i.e., backward and forward citation searches of 88 initial articles) identified 60 additional articles. One other article was obtained after sending solicitation letters to prominent authors for additional or unpublished data. Thus, a total of 149 nonredundant articles were retrieved for full review (see Figure 1).

Article Inclusion and Exclusion Articles were included in the meta-analysis if: (a) time- and frequency-domain HRV measures were obtained at least twice during separate, resting baseline conditions; (b) participants were nonclinical infants, children, or adolescents with a sample mean age less than or equal to 18.0 years; and (c) sufficient data were presented for effect size computation (i.e., reporting only means was insufficient).

Articles were excluded based on an a priori hierarchy: (i) not an empirical study (k = 1); (ii) sample size less than 10 (k = 4); (iii) sample of only clinical (e.g., diabetes, anxiety disorder) or special-population individuals (e.g., preterm infants, athletes; k = 9); (iv) fewer than two baseline HRV recordings were obtained (k = 53); (v) HRV data were obtained solely during overnight sleep (k = 3); (vi) NNinterval data not recorded from continuous ECG (k = 4); (vii) HRV data were averaged across rest and task conditions (k = 3); (viii) baseline HRV data not reported (k = 22); (ix) data were redundant with another included study (k = 1; more complete article retained). In total, 49 articles met final inclusion criteria (see Appendix and Figure 1).

Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Reliability of Article Selection and Coding A random sample of 10% of titles, abstracts, and articles were blindly re-coded after a 4-month period by the original (OW) and an independent (JL) rater. Excellent intra- and inter-rater agreement for title selection were obtained (Cohen’s kappa, κ = 1.0, for both). Excellent intra-rater (κ = 0.92) and good inter-rater agreement (κ = 0.8) for abstract selection were obtained. Abstract selection discrepancies were resolved through discussion. Excellent intrarater reliability was obtained for categorical (κ = 0.93) and continuous coding decisions (Intraclass Correlation, ICC = 1.0 [1.0, 1.0]). Good inter-rater reliability was obtained for categorical (κ = 0.83) and continuous coding decisions (ICC = 0.96, [0.93, 0.98]). Excellent intra- and inter-rater agreement for effect size selection were obtained (ICC = 1.0, [1.0, 1.0]).

Article Coding and Data Extraction Study follow-up length was coded in months. If demographic information (e.g., age, weight) was sex-stratified, a weighted mean was calculated. Demographic variables (mean participant age [years], sex [percent male], ethnicity [African American, European American, Hispanic, Asian, Mixed, Other]), and anthropometrics were coded (height [in], weight [lbs], BMI [kg/m2], blood pressure [mmHg], puberty [Tanner stage]). Explicit participant exclusion criteria were coded dichotomously (yes/no) within the following a priori categories: (a) prescription medication use; (b) medical illness or condition (e.g., chronic illness, diabetes); (c) mental health diagnosis (e.g., cognitive, intellectual, behavioral disorder); and (d) anthropometric characteristics (e.g., low birth weight, being obese). Study location was coded (university/hospital laboratory; school; home; nursery). Time of ECG recording was coded (morning 06:00– 11:59; afternoon 12:00–17:59; evening 18:00–23:59). ECG sampling rate was coded in Hz. ECG recording posture was coded ([1] seated, [2] supine, [3] standing) and “changed” was coded when participants assumed different postures between study assessments; supine was assumed among studies with infants, unless otherwise specified.

Durations for prerecording acclimatization period, total ECG recording, baseline ECG recording, analyzed recording, and epoch were coded in minutes. A dichotomous code (yes/no) indicated whether: (a) participants were required to remain awake for the entire recording duration; (b) study location, recording time, posture, acclimatization period, and ECG recording durations were identical across all follow-up assessments; (c) manual editing and digital automated filtering algorithms (e.g., Hanning/Hamming window, polynomial window) were applied to ECG data; (d) HRV frequency bands were age-appropriate Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

11

Publications identified in PsycInfo and PubMed using the following combinations: (“neonatal” OR “infant” OR “toddler” OR “child” OR “children” OR “adolescent” OR “adolescence” OR “teen” OR “youth” OR "young adult") AND (“heart period” OR “heart rate variability” OR “HRV” OR “respiratory sinus arrhythmia” OR “RSA” OR “vagal tone” OR “inter-beat interval” OR “IBI” OR “heart interval” OR “R-R interval” OR “RR interval” OR “N-N interval” OR “NN interval” OR “SDNN” OR “SDANN” OR “SDNNi” OR “rMSSD” OR “pNN50” OR “high frequency heart rate variability” OR “high frequency HRV” OR “low frequency heart rate variability” OR “low frequency HRV”) AND (“reliability” OR “reliable” OR “psychometric” OR “stability” OR “stable” OR “reproducible” OR “reproducibility” OR “intra-individual” OR “test-retest” OR “repeatable” OR “repeatability” OR “longitudinal”) Filters: Species: humans; Language: English; Date: Jan 1, 1970–Dec 31, 2014; Age Range: birth-18yrs Yielded (k=579) Title review: Excluded if no suggestion that HRV was obtained from infants, children, or adolescents Excluded (k=232) Abstract review: Excluded if no mention of: a) longitudinal study design, b) measuring HRV at least twice, and/or c) infant, child, or adolescent participants Excluded (k=259) Potentially relevant studies (k=88) Descendancy approach: Review reference list of identified studies for more potentially relevant studies Yielded (k=34) Ascendancy approach: Review titles of studies that cited identified articles by searching Web of Science Yielded (k=26) Solicitation for unpublished relevant data Yielded (k=1) Potentially relevant studies retrieved and reviewed (k=149)

Study exclusion hierarchy: -Not empirical study (k=1) -Sample N < 10 (k=4) -Clinical/unique sample only (k=9) -Recorded HRV < 2 times across full study length (k=53) -Recorded HRV only during overnight hours (k=3) -Did not derive HRV using continuous ECG data (k=4) -HRV data averaged across rest and task conditions (k=3) -Baseline HRV data not discussed or reported (k=22) -Redundant data (k=1)

Studies included in meta-analysis (k=49) Figure 1. Flow chart for article identification and inclusion in meta-analysis.

Ó 2016 Hogrefe Publishing

Journal of Psychophysiology (2017), 31(1), 6–28

12

(e.g., HF: 0.24–1.3 Hz infant/toddler, 0.15–0.40 Hz child/ adolescent), as per available guidelines and recommendations (Bar-Haim et al., 2000; Fox & Porges, 1985; Task Force, 1996); and (e) a single epoch was used to define the entire analyzed signal. “Unmentioned” was coded if a target variable was not reported.

Baseline HRV was defined as ECG-derived HRV measures that were prior to and distinct from any experimental study condition (e.g., stressor task, tilt, exercise). Baseline time- and frequency-domain HRV measures were coded using a priori categories. Heart period and mean NN-interval (ms) were coded as NN. SDNN and “heart period standard deviation” (ms) were coded as SDNN. rMSSD was coded (ms). HF in absolute (ms2) and normalized units (n.u.), respiratory sinus arrhythmia (s, ms), and “vagal tone” (lnms2) were coded as HF. LF in absolute (ms2) and normalized units (n.u.) was coded. LF:HF ratio was also coded.

Statistical Analysis Effect Size Calculation and Management Fisher’s Z, which ranges from 1 to +1 and can be interpreted similar to a correlation coefficient, was selected as the standardized common effect size. Intra-class correlations (ICC), as well as Pearson and Spearman correlations, were converted to Fisher’s Z using Fisher’s variance stabilizing transformation (Rosenberg, Adams, & Gurevitch, 2000; Rosenthal, 1994). F-ratios and unstandardized beta coefficients were converted to r and then to Fisher’s Z (Rosenberg et al., 2000; Rosenthal, 1994). Exact p values, when no other test statistic was available, were converted to a standard normal deviate (Z-score), then to r, and then to Fisher’s Z (Rosenberg et al., 2000).

Studies reporting effect sizes derived from > 2 follow-up assessments were coded for both the entire study follow-up length (i.e., one effect size per HRV variable, per study) and shorter, nonoverlapping follow-up intervals (e.g., 2–4 weeks, 4–6 weeks; see section Selection of Effect Sizes below).

Selection of Effect Sizes Effect sizes were coded for available data reported, thus yielding multiple effect sizes per study. Multiple effect sizes were reported due to HRV variables (e.g., HF, LF), followup intervals (e.g., 2–4 wks, 4–6 wks, 2–6 wks), and postures (e.g., supine, seated). We employed a conservative approach including only one effect size per HRV variable per study (Total: 93 effect sizes, M = 1.90 effect sizes per study; Infants/Toddlers: 47, M = 1.52; Children/ Adolescents: 46; M = 2.56); the longest follow-up interval, in one posture (selected hierarchically: seated, supine, standing), was retained. To maximize power, redundant Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

effect sizes were permitted only for the corresponding moderator analyses for follow-up interval and recording posture.

Meta-Analytic Strategy Analyses were age-stratified due to noted methodological differences between studies with younger and older children (cf., Williams et al., 2012). Infant/toddler studies were those with a mean sample age less than 5 years (k = 31); child/adolescent studies were those with a mean sample age 5 years or greater, but less than or equal to 18 years (k = 18). A fixed-effects meta-analytic model was chosen because the categorical variables coded essentially capture all possible options represented in the extant HRV literature (e.g., sitting vs. supine vs. standing posture; consistent vs. inconsistent recording durations), rather than categories sampled from a larger population of possible options (Rosenberg et al., 2000). A fixed-effects model is also consistent with an earlier meta-analysis of heart rate and blood pressure reproducibility in adults (Swain & Suls, 1996).

Thus, a random-effects model was not deemed appropriate given the examined data.

An effect size was calculated for each HRV variable separately. An analysis of the heterogeneity statistic (QT), which measured the variation for the included effect sizes, was conducted for each meta-analytic model. Significant QT indicated that the included effect sizes had a heterogeneous distribution and informed whether additional moderator analyses were warranted (Rosenberg et al., 2000). Continuous (i.e., slope) and categorical (i.e., QM) methodological variables were examined in subsequent moderator analyses (Rosenberg et al., 2000). Bootstrap methods (1,000 samples) produced robust nonparametric confidence interval estimates around each effect size (Rosenberg et al., 2000). Orwin’s Fail-Safe numbers addressed possible publication bias by estimating the number of missing, unpublished, or nonsignificant studies needed to make the overall effect size negligible or not different from zero.

Analyses were performed using MetaWin 2 (Sinauer Associates, 2000) and forest plots were graphed using Forest Plot Viewer (Boyles, Harris, Rooney, & Thayer, 2011).

Results Study Participant and Recording Characteristics A total of 49 studies (N = 5,170) were included in the present meta-analysis. Infant/toddler studies (k = 31; N = 3,329) and child/adolescent studies (k = 18; N = 1,841) had sample sizes ranging from 10 to 441 (Mdn = 90, 60, respectively), and about half of participants in both groups were male Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

13

Table 1. Study descriptive characteristics and ECG recording characteristics by age group Infant/toddler (k = 31) k Follow-up interval length (mos) Sample size (N)a Sample mean age (yrs) First study visit Final study visit Percent (%) of male participantsa Percent (%) of sample attrition First to second study visit Second to third study visit Height (in)a,b Weight (lb)a,b Acclimatization duration (min)a ECG recording duration (min)a Total recording duration Baseline recording duration Analyzed recording duration ECG epoch duration (min)a Number of effect sizes per study

N

Child/adolescent (k = 18) M(SD)

k

Continuous Study Variables 17.55 (16.57) 18 107.39 (88.50) 18

N

M(SD)

1,841 1,841

19.39 (26.13) 102.28 (91.93)

31 31

3,329 3,329

31 31 17

3,329 3,329 1,409

1.2 (1.48) 2.77 (2.46) 46.18 (7.78)

17 16 15

1,785 1,773 1,368

10.84 (3.18) 11.78 (3.10) 56.38 (25.67)

12 3 2 9 5

1,865 794 108 465 500

21.67 9.33 20.59 7.81 3.46

(17.14) (2.52) (0.23) (0.54) (2.11)

10 1 3 5 12

1,422 251 65 291 1,542

15.65 (10.37) 16.00 (0.00) 61.38 (6.76) 94.08 (25.45) 9.5 (10.34)

30 30 30 18 31

3,302 3,302 3,123 2,161 3,329

20.01 4.63 4.42 0.51 4.81

(40.39) (4.24) (4.29) (0.22) (8.15)

17 18 18 4 18

1,785 1,841 1,841 179 1,841

16.61 5.00 3.74 0.81 3.56

k

N

k

N

%c

(19.26) (3.56) (1.91) (0.80) (2.79)

%c

Categorical Study Variables Sample exclusion criteria Prescription medications Medical illness/condition Time of ECG recording 06:00-12:00 12:00-18:00 Awake vs. asleep ECG recordinga Exclusively awake data Sleep unrestricted Additional measure of respiration Recording postured Seated posture Supine posture Standing posture Changed recording posture ECG sampling rate 500 Hz <500 Hz Manual editing of ECG data Digital filtering algorithm HRV derivation method Mean of 1 epoch Mean of 2 epochs Recommended frequency band High Frequency (HF) Low Frequency (LF)

3 5

165 359

9.7% 16.1%

3 8

227 978

16.7% 44.4%

2 1

58 20

6.5% 3.2%

5 1

324 64

27.8% 5.6%

27 4 7

3,079 250 723

87.1% 12.9% 22.6%

18 – 7

1,841

100%

1,319

38.9%

20 5 1 5

2,320 614 112 265

64.5% 16.1% 3.2% 16.1%

7 6 2 –

987 307 232

38.9% 33.3% 6.5%

3 4 24 22

157 295 2,744 2,197

9.7% 12.9% 77.4% 71%

10 1 15 8

1,243 57 1,792 433

55.6% 5.6% 83.3% 44.4%

5 18

493 2,161

16.1% 58.1%

7 4

1,154 179

38.9% 22.2%

18 1

2,074 31

58.1% 3.2%

8 5

514 362

44.4% 27.8%

Notes. HRV = Heart Rate Variability; ECG = Electrocardiogram; LF = Low Frequency; HF = High Frequency; aBased on initial study assessment. Unmentioned categories not included (i.e., target variable not reported; % may not total 100%). bLength, birthweight for Infant/toddler group. Of 9 studies reporting birthweight, mean age = 1.43 mos. cPercent of corresponding category group total k. dRedundant effect sizes included (i.e., multiple postures reported per study).

(see Table 1). Overall, few studies reported a priori sample exclusion criteria or participant characteristics (e.g., height, weight, study attrition), which precluded certain planned moderator analyses. Mean ECG recording duration analyzed for HRV in infant/toddler studies was 4.42 min (SD = 4.29), and in child/adolescent studies it was 3.74 min (SD = 1.91; see Table 1). Ó 2016 Hogrefe Publishing

Overall Summary Analyses Baseline HRV exhibited moderate test-retest reliability across both age groups. To compare the overall reliability between the age groups, a mean effect size was calculated across HRV variables to yield one effect size per study.

Overall reliability was significantly lower for infant/toddler Journal of Psychophysiology (2017), 31(1), 6–28

Journal of Psychophysiology (2017), 31(1), 6–28

k

B (SE)

14 0.33 [.15, .49] 2 0.75 [.68, 1.00]

Z [95% CI]

a

22.9**

45.8***

36.5***

45.8***

45.8***

47.5***

QT

45.8** 14.9**

QT

Z [95% CI]

QT

k

Z [95% CI]

a

rMSSD QT

k

a

Z [95% CI]

HF

– –

– .130(.04)***

– .017(.01)

4

5

– 1.088(.35)**

– .018(00)

– .003(.00)

– –.057(.03)*

B (SE)

5

5

5

5

k

k

15.8** 3

15.8** 3

15.8** 3

15.8** 3

14.2**

B (SE)

QT

– –

– .201(.05)***

– –

– 2.085(.57)***

– .173(.05)***

– .051(.03)*

– –.553(.22)*

k

.002(.00) –.005(.00)**

B (SE)

18 4

27 13.7*** 17

6 11

19 13.7*** 16

28 13.7*** 16

.052(.14) .059(.11)

.010(.01) .066(.03)**

.035(.03) .007(.01)

.262(.55) –.344(.17)*

.055(.02)*** .005(.00)

28 .000(.00) 13.7*** 16 –.001(.01)***

28 13.7*** 17

Continuous moderator analyses

15.8** 3

QT

Overall summary analyses 2 0.33 [.15, .91] 14.9** – 28 0.42 [.35, .49] 5 0.94 [.68, 1.25] 15.8** 3 1.00 [0.41, 1.53] 13.7*** 17 0.62 [.51, .78]

k

a

SDNN

48.0*** 3.62

72.1*** 67.7***

2.91 51.0***

62.5** 65.8***

72.1*** 65.8***

72.1*** 67.7***

Z [95% CI]

a

QT

k

Z [95% CI]a

LF:HF QT

k

7

5

7

7

7

– –

– .059(.03)*

– .008(.01)

– –.430(.21)*

– .007(.03)

– –.003(.00)**

– –.031(.01)**

B (SE)

k

13.9* 4

3.6

13.9* 4

13.9* 4

13.9* 4

13.9* 4

QT

– –

– .015(.04)

– –

– .742(.56)

– .018(.05)

– .001(.02)

– .077(.18)

B (SE)

5.5

5.5

5.5

5.5

5.5

QT

– – 7 0.63 [.46, .82] 19.9* 4 0.63 [.30, .89] 5.5

k

101.4*** 84.3*** 7

QT

72.1** 67.7**

QT

LF

Notes. B = slope; SE = standard error; Z = Fisher’s Z; QT = Heterogeneity; NFS = Orwin’s Failsafe N; NN = mean NN interval; SDNN = standard deviation of NN; rMSSD = root mean square of successive differences; HF = high frequency; LF = low frequency. aBootstrap 95% Confidence Interval. bLongest follow-up interval assessment only (i.e., only non-redundant effect sizes). cInitial study assessment. *p < .05. **p .01. ***p .001.

Follow-up interval length (mos)b Infant/Toddler 14 .004(.00) Child/Adolescent – Sample size (N)c Infant/Toddler 14 .001(.00) Child/Adolescent – Mean age (yrs)c Infant/Toddler 14 .153(.03)** Child/Adolescent – Male (percent)c Infant/Toddler 9 2.141(.90)** Child/Adolescent – Acclimatization duration (min)c Infant/Toddler – Child/Adolescent – Analyzed recording duration (min)c Infant/Toddler 14 –.028(.01)* Child/Adolescent – Epoch duration (min)c Infant/Toddler 8 .228(.79) Child/Adolescent –

Infant/Toddler Child/Adolescent

k

NN

Table 2. HRV reliability – summary effect sizes and continuous moderator analyses by age group

14 O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

15

studies compared to child/adolescent studies (Z = 0.42 vs.

0.62; QM = 32.49, p < .001). All effect sizes, except LF:HF, were significantly heterogeneous, indicating that further moderator analyses were warranted. Mean summary effect sizes are presented in Table 2. Reliability forest plots are presented in Figures 2–4.

Study

Moderator Analyses Continuous (Table 2) and categorical (Tables 3 and 4) moderator variables were examined within the following categories: Study protocol (test-retest follow-up interval length; standardized recording protocol; time of day, awake), sample

r

Bar Haim, Marshall, & Fox, 2000 Bornstein & Suess, 2000b Fox, 1983 Fox & Field, 1989 Hsu & Porter, 2004 Izard et al., 1991 Marshall & Stevenson Hinde, 1998 Porges, unpublished Porges et al., 1994 Porter, Bryan, & Hsu, 1995 Propper et al., 2008 Snidman et al., 1995 Stifter & Fox, 1990 Stifter & Jain, 1996 SUMMARY Infant/Toddler SUMMARYNN NN - Infant/Toddler

0.38 0.08 0.53 0.69 0.16 0.26 0.50 0.57 0.68 0.21 0.31 0.13 0.01 0.04 0.32

Doussard Roosevelt et al., 2003 Winegust et al., 2014 SUMMARY NN NN - Child/Adolescent Child/Adolescent SUMMARY

0.59 0.76 0.64

Galland et al., 2006 Snidman et al., 1995 Infant/Toddler SUMMARY SUMMARYSDNN SDNN - Infant/Toddler

0.72 0.15 0.41

Blom et al., 2009 Dietrich et al., 2010 Farahi et al., 2014 Gamelin et al., 2009 Winsley et al., 2003 SUMMARY SUMMARYSDNN SDNN - Child/Adolescent Child/Adolescent

0.66 0.79 0.91 0.39 0.49 0.69

Farahi et al., 2014 Gamelin et al., 2009 Winsley et al., 2003 SUMMARY rMSSD SUMMARY rMSSD - Child/Adolescent

0.91 0.39 0.44 0.76

1.5

1.0

0.5

0.0

0.5

1.0

1.5

2.0

Fisher's Z (+/ 95% CI)

Figure 2. HRV Reliability Forest Plots for NN, SDNN, and rMSSD. Ó 2016 Hogrefe Publishing

Journal of Psychophysiology (2017), 31(1), 6–28

16

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Study

r

Alkon et al., 2011 Alkon et al., 2006 Arendt et al., 1991 Bar Haim, Marshall, & Fox, 2000 Blandon et al., 2010 Bornstein & Suess, 2000a Bornstein & Suess, 2000b Calkins & Keane, 2004 Fox & Field, 1989 Fracasso et al., 1994 Hastings et al., 2008 Hsu & Porter, 2004 Izard et al., 1991 Kennedy et al., 2004 Liew et al., 2011 Marshall & Stevenson Hinde, 1998 Patriquin et al., 2014 Perry et al., 2012 Porges et al., 1994 Porges, unplshd Porter, Bryan, & Hsu, 1995 Propper et al., 2008 Richards, 1989 Rigterink, Kats, & Hessler, 2010 Schuetze, Eiden, & Edwards, 2009 Stifter & Fox, 1990 Stifter & Jain, 1996 Yiallourou et al., 2012 Infant/Toddler SUMMARY SUMMARYHF HF - Infant/Toddler

0.47 0.40 0.06 0.46 0.49 0.55 0.30 0.57 0.89 0.10 0.53 0.24 0.36 0.47 0.32 0.47 0.34 0.34 0.55 0.33 0.21 0.36 0.32 0.16 0.33 0.07 0.15 0.32 0.40

Blom et al., 2009 Cavaliere, 2011 Dietrich et al., 2010 Doussard Roosevelt et al., 2003 Elmore Staton, 2011 El Sheikh, 2005 Farahi et al., 2014 Gamelin et al., 2009 Gentzler et al., 2012 Hinnant & El Sheikh, 2009 Hinnant, Elmore Staton, & El Sheikh, 2011 Keller & El Sheikh, 2009 Keller et al., 2014 Leicht & Allen, 2008 Mezzacappa et al., 1997 Salomon, 2005 Winsley et al., 2003 SUMMARYHF HF - Child/Adolescent Child/Adolescent SUMMARY

0.66 0.70 0.77 0.67 0.36 0.49 0.70 0.39 0.26 0.79 0.56 0.65 0.45 0.84 0.37 0.48 0.44 0.55

1.5

1.0

0.5

0.0

0.5

1.0

1.5

2.0

Fisher's Z (+/ 95% CI)

Figure 3. HRV Reliability Forest Plots for HF.

Journal of Psychophysiology (2017), 31(1), 6–28

Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Study

17

r

Blom et al., 2009 Dietrich et al., 2010 Farahi et al., 2014 Gamelin et al., 2009 Leicht & Allan, 2008 Mezzacappa et al., 1997 Winsley et al., 2003 Child/Adolescent SUMMARY SUMMARY LF LF - Child/Adolescent

0.65 0.66 0.70 0.39 0.85 0.37 0.37 0.56

Farahi et al., 2014 Gamelin et al., 2009 Leicht & Allan, 2008 Winsley et al., 2003 SUMMARY LF:HF LF:HF - Child/Adolescent Child/Adolescent SUMMARY

0.65 0.39 0.83 0.14 0.56 1.5

1.0

0.5

0.0

0.5

1.0

1.5

2.0

Fisher's Z (+/ 95% CI)

Figure 4. HRV Reliability Forest Plots for LF and LF:HF.

characteristics (age, developmental period; participant exclusion criteria; sex, proportion male), ECG signal acquisition and preprocessing (acclimatization period; posture; sampling rate; artifact editing, filtering), and HRV analyses (recording duration analyzed; epoch length; frequency bandwidth selection). Moderator analyses were largely restricted to NN and HF among infant/toddler studies, and HF and LF among child/adolescent studies, due to limited reported data.

Study Protocol Longer study follow-up length was not associated with NN or HF reliability among infant/toddler studies. In contrast, longer study follow-up length was significantly associated with lower SDNN (B = 0.057, p = .052), rMSSD (B = 0.553, p = .011), HF (B = 0.005, p = .008), and LF (B = 0.031, p = .007), but not LF:HF reliability, among child/adolescent studies.

Significant heterogeneity remained for all HRV variables in both age groups, except LF:HF which was homogenous, indicating that further or nested moderator analyses are merited. Moderator results were consistent for conservative and redundant effect size selection approaches; only conservative approach statistics reported for parsimony.

Across both age groups, minimal reporting of ECG recording time precluded an examination of whether time of recording ECG (e.g., morning vs. afternoon) moderated HRV reliability. However, HF reliability was significantly higher among infant/toddler studies that recorded Ă&#x201C; 2016 Hogrefe Publishing

exclusively awake ECG data, compared to recordings that contained some sleep (i.e., napping; Z = 0.44 vs. 0.21; QM = 6.35, p = .012).

Sample Characteristics Significantly higher NN reliability was found among infant/ toddler studies that excluded participants based on prescription medication use (Z = 0.61 vs. 0.23; QM = 14.06, p < .001) and medical illness/condition (Z = 0.47 vs.

0.26; QM = 5.26, p = .022); similar results were not found for HF. Significantly higher HF (Z = 0.67 vs. 0.55; QM = 6.63, p = .010) and LF reliability (Z = 0.80 vs. 0.54; QM = 4.17, p = .041) were found among child/adolescent studies that excluded participants based on medical illness/condition. Nested moderator analyses may further address remaining heterogeneity across most effect sizes.

Larger sample size was significantly associated with higher rMSSD reliability (B = 0.051, p = .043), but lower HF (B = 0.001, p < .001) and LF reliability (B = .003, p < .001) among child/adolescent studies. Older sample age was significantly associated with higher NN (B = 0.153, p < .001) and HF reliability (B = 0.055, p < .001) among infant/toddler studies, and higher rMSSD (B = 0.173, p < .001) among child/adolescent studies.

Having a greater proportion of male participants was significantly associated with higher NN reliability (B = 2.141, p = .017) among infant/toddler studies; no relation was Journal of Psychophysiology (2017), 31(1), 6â&#x20AC;&#x201C;28

18

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Table 3. HRV reliability – infant/toddler studies, categorical moderator variables NN k Sample exclusion criteria Prescription medications Exclusion reported Unmentioned Medical illness/condition Exclusion reported Unmentioned Acclimatization period Yes/included No/unmentioned Awake vs. asleep ECG recordingb Exclusively awake data Sleep unrestricted Recording posturec Sitting Supine Standing Changed recording posture Unmentioned ECG sampling rate 500 Hz <500 Hz Unmentioned Manual editing of ECG data Yes No/unmentioned Digital filtering algorithm Yes No/unmentioned Identical ECG recording durations Yes No/unmentioned HRV derivation method Mean of 1 epoch Mean of 2 epochs Recommended frequency band Yes No/unmentioned

2 12 3 11 2 12 13 1 10 1 1 3

12 2 11 3 11 3

9

Z [95% CI]

a

HF QT

QM = 14.06, p < .001 0.61 [.40, .65] 1.10 0.23 [.11, .40] 30.67*** QM = 5.26, p = .022 0.47 [.04, .65] 11.74** 0.26 [.12, .44] 28.82*** QM = 18.31, p < .001 0.62 [.55, .65] 0.29 0.21 [.10, .38] 27.22** N/A 0.35 [.17, .52] 41.29*** 0.01 [-,-] – QM = 9.42, p = .002 0.39 [.21, .58] 31.55*** 0.16 [-,-] – 0.38 [-,-] – 0.01 [-.21, .59] 3.79 – N/A – – – QM = 4.99, p = .025 0.38 [.16, .55] 40.80*** 0.14 [.13, .16] 0.02 QM = 1.96, p = .161 0.36 [.14, .53] 41.87*** 0.21 [.13, .59] 1.98 QM = 5.75, p = .017 0.38 [.16, .56] 38.43*** 0.12 [.01, .32] 1.67 N/A – 0.37 [.11, .57] 35.34*** N/A – –

NFS

k

120 269

2 26

137 273

4 24

122 243

8 20

446 –

25 3

384 – – 0

19 5 3 1 2 4 22

439 25

22 6

382 59

22 6

404 31

20 8

326

2 18 18 8

Z [95% CI]

a

QT

QM = 0.44, p = .509 0.37 [.34, .50] 0.45 0.42 [.34, .50] 71.25*** QM = 2.82, p = .093 0.32 [.21, .42] 2.00 0.44 [.35, .51] 67.31*** QM <0.01, p = .986 0.42 [.36, .48] 3.48 0.42 [.31, .51] 68.66*** QM = 6.35, p = .012 0.44 [.36, .50] 61.00*** 0.21 [.03, .34] 4.79 QM = 18.69, p < .001 0.49 [.42, .56] 43.28*** 0.32 [.25, .35] 0.65 – 0.19 [-.01, .34] 4.32 0.38 [-,-] – QM = 5.07, p = .079 0.39 [.35, .50] 0.34 0.28 [.15, .34] 1.62 0.44 [.36, .52] 65.11 QM = 1.80, p = .180 0.43 [.35, .51] 64.60*** 0.34 [.21, .48] 6.04 QM = 4.00, p = .045 0.45 [.35, .53] 62.30*** 0.35 [.27, .46] 6.13 QM = 5.35, p = .021 0.45 [.36, .52] 53.71*** 0.34 [.23, .46] 13.08* QM = 5.21, p = .017 0.25 [.16, .34] 1.08 0.46 [.38, .53] 47.97*** QM = 0.58, p = .448 0.43 [.33, .50] 36.86*** 0.48 [.31, .73] 30.84***

NFS

71 1078 124 1020 329 821 1065 60 921 155 53 – 76 106 946 930 200 962 204 886 261 48 811 754 372

Notes. k = number of studies; Z = Fisher’s Z; QM = between-groups heterogeneity; QT = total heterogeneity; NFS = Orwin’s Failsafe N; SDNN = standard deviation of NN intervals; HF = high frequency; LF = low frequency; ECG = electrocardiogram; HRV = heart rate variability. aBootstrap 95% Confidence Interval. bInitial study assessment recording condition. cRedundant effect sizes included (i.e., multiple postures reported per study). *p < .05. **p .01. ***p .001.

observed for HF reliability. Conversely, a greater proportion of males was significantly associated with higher SDNN (B = 1.088, p < .002) and rMSSD reliability (B = 2.085, p < .001), and lower HF (B = 0.334, p = .039) and LF reliability (B = 0.430, p = .036) among child/adolescent studies; no relation was observed for LF:HF reliability.

Further nested analyses are needed to address remaining heterogeneity.

Electrocardiogram Signal Acquisition and Preprocessing Prerecording acclimatization period was associated with significantly higher NN reliability (Z = 0.62 vs. 0.21; QM = 18.31, p < .001) among infant/toddler studies, and higher LF Journal of Psychophysiology (2017), 31(1), 6–28

reliability (Z = 0.79 vs. 0.39; QM = 10.32, p = .001) among child/adolescent studies. No significant relations were observed for HF reliability in either age group. The duration of the acclimatization period was not associated with reliability of any HRV measures. Significant heterogeneity remained for NN reliability among infant/toddler studies and HF reliability in both age groups; LF reliability was homogenous among child/adolescent studies.

Electrocardiogram recording posture was significantly associated with NN (QM = 9.42, p = .002) and HF reliability (QM = 18.69, p < .001) among infant/toddler studies; reliability was highest in seated recordings and lowest when recording posture changed across follow-up assessments.

Recording posture was not associated with HRV reliability Ó 2016 Hogrefe Publishing

Ó 2016 Hogrefe Publishing

1

3 2

3 2

2 2

1 4 1

5

4 1

2 3

5

14.22** –

15.77** = .549 9.61*** 5.80

QT

N/A 0.94[.66, 1.25] 15.77** – N/A 0.79[-, -] – 1.05[.46, 1.36] 13.17** 1.02[-, -] – – QM = 6.53, p = .011 0.73[.41, .79] 1.57 1.26 [.54, 1.53] 6.41* QM = 4.08, p = .043 0.87[.41, 1.07] 5.17 1.26[.54, 1.53] 6.41* QM = 1.75, p = .186 0.89[.54, 1.07] 3.47 1.13[.41, 1.53] 10.54** N/A – 0.79[-, -] – N/A – –

N/A – 0.94[.66, 1.25] QM = 0.36, p 0.98[.79, 1.53] 0.89[.41, 1.07] N/A 0.97[.73, 1.33]

Z [95% CI]

6 4

– 6 5

8 9

14 3

263 224

256 249

9 7

7 6 2 4

– 414 –

143 249

17

12 5

8 9

2 15

k

466

382 –

194 265

466

NFS

HF QT

QM = 0.97, p = .324 0.55[.52, .87] 1.36 0.63[.51, .82] 63.34*** QM = 6.63, p = .010 0.67[.52, .93] 39.50*** 0.55[.46, .73] 21.54** QM = 3.29, p = .070 0.64[.53, .82] 58.14*** 0.49[.38, .79] 6.23 N/A 0.62 [.51, .78] 67.67*** – QM = 4.89, p = .180 0.65[.50, .90] 43.36*** 0.64[.41, .99] 18.41** 0.49[.26, .93] 15.48*** 0.55[.50, .73] 3.34 QM = 3.65, p = .056 0.64[.49, .81] 43.34*** 0.53[.46, .87] 11.99 QM = 1.97, p = .160 0.62[.51, .77] 63.43*** 0.84[.47, 1.22] 2.27 QM = 0.95, p = .330 0.67[.46, .91] 21.12** 0.61[.49, .78] 45.60*** QM = 1.98, p = .371 0.62[.49, .84] 42.27*** 0.74[.40, .83] 3.80 QM = 2.60, p = .106 0.64[.52, .95] 19.02** 0.49[.37, .79] 6.23

Z [95% CI]

a

376 239

364 291

529 537

847 249

569 364

447 378 96 214

1,039

756 239

541 482

107 932

NFS

6 1

1

5 2

4 3

2 4

1 6 2

7

5 2

2 5

7

k

QT

N/A – 0.63[.46, .82] 13.90* QM = 4.17, p = .041 0.80[.78, .87] 0.14 0.54[.39, .81] 9.59* QM = 10.32, p = .001 0.79[.67, .91] 3.58 0.39[.39, .39] 0.00 N/A 0.63[.46, .82] 13.90* – QM = 1.11, p = .293 0.78[-, -] – 0.58[.42, .85] 11.87* 0.47[.32, .75] 6.23* – QM = 2.12, p = .145 0.72[.41, .78] 1.49 0.52[.39, .99] 8.59* QM = 1.90, p = .169 0.59[.39, .78] 8.91* 0.83[.39, 1.26] 3.08 QM = 0.27, p = .605 0.62[.41, .81] 11.85* 0.71[.41, .87] 1.78 N/A – 0.78[-, -] – N/A 0.64[.46, .85].

13.37* 0.39[-, -] –

Z [95% CI]

LF a

375 –

–

302 139

233 246

142 202

– 341 91

432

388 76

158 265

432

NFS

3 1

2 2

1 3

1 3

4

4

3 1

1 3

4

k

QT

N/A – 0.63[.30, .89] 5.52 N/A 0.78[-, -] – 0.51[.14, 1.19] 4.60 N/A 0.74[.41, 1.19] 2.87 0.14[-, -] – N/A 0.63[.30, .89] 5.52 – N/A – 0.63[.30, .89] 5.52 – – N/A 0.41[-, -] – 0.71[.14, 1.19] 4.65 N/A 0.41[-, -] – 0.70[.14, 1.19] 4.65 QM = 0.03, p = .866 0.60[.14, 1.19] 4.34* 0.65[.41, .78] 1.15 N/A – – N/A 0.74[.14, 1.19] 2.87 0.39[-, -] –

Z [95% CI]a

LF:HF

217 –

117 127

– 209

– 209

249

249

217 –

– 151

249

NFS

Notes. k = number of studies; Z = Fisher’s Z; 95% CI = bootstrap 95% confidence interval; QM = between-groups heterogeneity; QT = total heterogeneity; NFS = Orwin’s Failsafe N; SDNN = standard deviation of NN intervals; HF = high frequency; LF = low frequency; ECG = electrocardiogram. aBootstrap 95% Confidence Interval. bInitial study assessment recording condition. cRedundant effect sizes included (i.e., multiple postures per study). *p < .05. **p .01. ***p .001.

Sample exclusion criteria Prescription medications Exclusion reported Unmentioned Medical illness/condition Exclusion reported Unmentioned Acclimatization period Yes/included No/unmentioned Awake vs. asleep ECG recordingb Exclusively awake data Sleep unrestricted ECG recording posturec Sitting Supine Standing Unmentioned ECG sampling rate 500 Hz Unmentioned Manual editing of ECG data Yes No/unmentioned Digital filtering algorithm Yes No/unmentioned HRV derivation method Mean of 1 epoch Mean of 2 epochs Recommended frequency band Yes No/unmentioned

k

a

SDNN

Table 4. HRV reliability – child/adolescent studies, categorical moderator variables

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability 19

Journal of Psychophysiology (2017), 31(1), 6–28

20

among child/adolescent studies; however, HF and LF reliability were relatively lower when recorded in standing posture. Significant heterogeneity among seated posture measures suggests further or nested moderated analyses are warranted in both age groups. Moderator analyses for posture were only conducted using the redundant effect size selection approach, to permit sufficient data for comparison.

The paucity of studies reporting ECG sampling rate precluded adequate examination as a moderator of HRV reliability. Gross binary coding ( 500 Hz vs. unmentioned) was used to salvage available information to conduct cursory categorical moderator analyses. Child/adolescent studies reporting the sampling rate 500 Hz yielded significantly higher HF reliability (Z = 0.64 vs. 0.53; QM = 3.65, p = .056) and lower SDNN reliability (Z = 0.73 vs. 1.26; QM = 6.53, p = .011), than studies that did not report the sampling rate. Reliability did not differ among infant/toddler studies when comparing the sampling rate 500 Hz, versus not reported or < 500 Hz. Infant/toddler studies that reported manual editing of ECG data, versus not reporting this detail, yielded significantly higher NN reliability (Z = 0.38 vs. 0.14; QM = 4.99, p = .025); while those reporting an automated filtering algorithm, versus not reporting this detail, yielded significantly higher HF reliability (Z = 0.45 vs. 0.35; QM = 4.00, p = .045). Reliability did not differ among child/adolescent studies that reported editing and filtering of ECG data, with the exception of SDNN where manual editing was associated with lower reliability (Z = 0.86 vs.

1.26; QM = 4.08, p = .043; see Table 4).

Heart Rate Variability Analyses Longer recording duration analyzed was significantly associated with lower NN reliability (B = 0.028, p = .026), but was not associated with HF reliability among infant/toddler studies. Conversely, longer recording duration analyzed was significantly associated with higher SDNN (B = 0.130, p < .001), rMSSD (B = 0.201, p < .001), HF (B = 0.066, p = .004), and LF reliability (B = 0.059, p = .040) among child/adolescent studies; no relation was observed for LF:HF reliability. Remaining effect size heterogeneity, mostly among measures from child/adolescent studies, suggests that further or nested moderator analyses are required. Analyzing identical ECG recording durations, versus differing durations, across both study assessments was associated with significantly higher NN (Z = 0.38 vs. 0.12; QM = 5.75, p = .017) and HF reliability (Z = 0.45 vs. 0.34; QM = 5.35, p = .021) among infant/ toddler studies. As well, infant/toddler HF reliability was significantly higher when derived using the mean of two or more ECG epochs, compared to a single epoch

Journal of Psychophysiology (2017), 31(1), 6â&#x20AC;&#x201C;28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

(Z = 0.46 vs. 0.25; QM = 5.41, p = .020). These associations were not observed among child/adolescent studies.

Duration of the analytical epoch (e.g., 1-min, 5-min) was not associated with HRV reliability.

Selecting an age-specific HRV frequency bandwidth consistent with guidelines and recommendations, compared to an alternative bandwidth, yielded no significant differences. Effect sizes for HF, LF, and LF:HF reliability were relatively larger among studies that selected an age-specific frequency bandwidth, compared to an alternative bandwidth (ZHF = 0.64 vs. 0.49, ZLF = 0.64 vs. 0.39, ZLF:HF = 0.74 vs. 0.39); yet, significant heterogeneity remained.

Study Quality An index of study quality was coded based on 10 study characteristics: (1) having exclusion criteria for medical illness/condition (26.5% of studies had this characteristic); (2) having exclusion criteria for medication use (12.2%); (3) having sample size 50 (44.9%); (4) specified at least one prerecording participant instruction (e.g., overnight fast, no exercise for 12 hr; 18.4%); (5) included a prerecording acclimatization period (36.7%); (6) analyzed baseline ECG recording duration 3.5 min (51%); (7) analyzed recording duration 3.5 min (50%); (8) reported both ECG recording hardware and HRV analysis software (81.6%); (9) reported both manual editing and digital automated filtering of ECG data (49%); and, (10) reported using recommended HRV frequency bands for sample age (49.0%). Correlation analyses with one effect size per study (i.e., mean effect size averaged across HRV variables) revealed study quality was not associated with HRV reliability among infant/toddler studies (M = 3.97 out of possible 10 characteristics, SD = 2.07; r = .033, p = .861), but was positively associated among child/adolescent studies (M = 4.67, SD = 1.68; r = .489, p = .040).

Discussion The overarching aim of this systematic review was to comprehensively examine test-retest reliability of pediatric HRV using meta-analytic techniques. Effect sizes for reliability of time- and frequency-domain HRV measures were typically in the moderate range across both age groups. Further, overall test-retest reliability was significantly higher in child/adolescent studies, compared to infant/toddler studies. While several moderating variables influenced HRV reliability, limited methodological reporting precluded several a priori planned moderator analyses.

Ă&#x201C; 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Study Protocol Longer study follow-up length was associated with decreased reliability for SDNN among infant/toddler studies, and decreased reliability of SDNN, rMSSD, HF, and LF among child/adolescent studies. This result is not unexpected given basic psychometric principles (cf., Cohen & Swerdlik, 2002). As example, studies lasting 3 years or longer had lower test-retest reliability than those only 2 weeks or shorter in duration. Age-related increases in HRV partly reflect increased autonomic complexity and offer a plausible explanation for decreasing reliability with increased follow-up length. The prominent use of a moving polynomial filter (Porges, 1985) to derive HF among infant/ toddler studies may augment reliability by accounting for variance attributed to normal developmental decreases in heart rate activity over longer test-retest intervals (e.g., newborns studied across several months or years). Conversely, the predominant use of spectral analysis techniques (e.g., Fourier transformation) or the peak-valley method (e.g., El-Sheikh, 2005) to derive HF among child/ adolescent studies, neither of which inherently accounts for heart rate variance, may explain the lower reliability of HF across longer test-retest intervals in this age group.

These results suggest that an examination of long-term reproducibility of HF derived with and without a moving polynomial filter, compared to HF derived with and without statistical control of heart rate, should be conducted.

Due to limited reported data, this meta-analysis could not examine whether consistent ECG recording time of day across follow-up assessments (i.e., to account for potential circadian effects) improved reliability. However, other results from this meta-analysis indicated that HF reliability among infant/toddler studies, which analyzed exclusively awake ECG data, was higher compared to HF derived from daytime data containing some sleep (i.e., naps; unrestricted sleep). It is unknown whether the same infants/toddlers fell asleep at both assessments, which would reduce the replicability of initial recording conditions. Circadian variation in HRV across different age groups is important to consider when interpreting HRV (Ernst, 2014). As such, more research using 24-hr ECG data is necessary to examine whether HRV reliability varies depending on certain times of day (e.g., morning, afternoon, evening).

Sample Characteristics Increased sample homogeneity, attributable to specified participant exclusion criteria, also influenced HRV. Higher NN reliability was observed among infant/toddler studies that reported exclusion criteria for medical illness/ condition and prescription medication use, while higher HF and LF reliability was observed among child/adolescent Ă&#x201C; 2016 Hogrefe Publishing

21

studies that excluded for medical illness/condition. Results suggest that reducing variation attributable to prescription medication use or compromised health improves reliability.

Considering these findings, future studies should examine the cross-sectional and longitudinal effects on HRV attributable to illnesses and medications common in pediatric populations to determine whether employing stringent exclusion criteria (e.g., specific diagnosis or medication) would augment HRV reliability.

Biological sex was a significant moderator of HRV reliability across both age groups, albeit in different ways. Studies with a greater proportion of male participants yielded higher NN reliability among infant/toddler studies, and yielded higher SDNN and rMSSD, but lower HF and LF reliability among child/adolescent studies. These child/adolescent results are similar to adult findings of lower HRV reliability among males relative to females (e.g., Sookan & McKune, 2012). Current thinking is that sex differences in HRV result from greater sympathetic dominance among males relative to females; indeed, females often, but not always (e.g., El-Sheikh, 2005), produce higher mean-level HF and yield greater HF reliability relative to males. Biologically, sex differences in HRV reliability may be associated with hormonal differences related to pubertal development (e.g., sympathetic alterations in response to increased estrogen concentration; Kajantie & Phillips, 2006; Ordaz & Luna, 2012; Shirtcliff, Dahl, & Pollak, 2009). Sex differences in HRV reliability may become more pertinent as children advance toward sexual maturation (e.g., Patton & Vines, 2007; Silvetti et al., 2001). Jarrin and colleagues (2015) demonstrated that measures of gonadarche and adrenarche were negatively associated with rMSSD, pNN50, and HF, and positively associated with LF:HF. As such, more research into the association between pubertal status, related hormone levels, and HRV reliability among youth is needed to address this noted knowledge gap. Sex differences in pediatric HRV reliability could be clarified if authors report sex-stratified analyses.

Electrocardiogram Signal Acquisition and Preprocessing Including a prerecording acclimatization period was associated with significantly higher NN reliability among infant/ toddler studies, and significantly higher LF reliability among child/adolescent studies; further, effect sizes for each measure were homogenous. Studies that allowed participants to habituate yielded some of the largest effect sizes associated with a particular HRV measure, relative to studies without an acclimatization period. Results largely supported HRV guideline recommendations to include a prerecording acclimatization period (Berntson et al., 1997). Infant/toddler studies typically reported < 4 min of acclimatization, Journal of Psychophysiology (2017), 31(1), 6â&#x20AC;&#x201C;28

22

while child/adolescent studies typically reported < 10 min.

No relationship was found between the acclimatization period duration and HRV reliability, suggesting even a brief habituation period prior to data collection may be sufficient to stabilize HRV and augment reliability.

Examination of HRV reliability among groups of younger and older children may determine whether the “optimal” acclimatization duration changes across development.

Recording posture influenced HRV reliability. Specifically, infant/toddler NN and HF reliability were higher for measures obtained in seated postures and lowest when posture changed between study recordings. These observations are consistent with Young and Leicht (2011), who reported higher HF reliability among adults in seated versus standing postures. Child/adolescent HF and LF reliability were relatively lower among studies that recorded ECG in a standing posture, rather than seated or supine. Children who are asked to maintain a standing posture for a baseline ECG recording, even of only 3- to 5-min, may become more restless/fatigued compared to maintaining a seated posture; with increasing restlessness, the stationarity of heart rate signals will decline and compromise reproducibility.

Given the results from blockade studies examining HRV and posture (e.g., standing vs. supine, Cacioppo et al., 1994), HF may be more reliably measured in seated or supine, relative to standing, postures when parasympathetic dominance is more readily observed; while, LF may be more reliably measured in seated or standing postures when greater sympathetic, as opposed to parasympathetic dominance, is more readily observed. Further exploration of head-up-tilt paradigms in pediatric studies may improve our understanding of how postural position is related to HRV and its reliability.

The paucity of reported ECG sampling rates precluded a meaningful examination of its relation with HRV reliability.

Child/adolescent studies that reported a sampling rate 500 Hz had higher HF reliability than studies that did not report this detail; however, only relative differences in reliability were found across the remaining analyses.

Thus, conclusions about these results must be made with caution. Notedly, only 23% (k = 7) of infant/toddler studies reporting HF explicitly detailed the ECG sampling rate.

Lack of reporting ECG sampling rate illustrates a noted limitation in HRV reporting practices among the pediatric, relative to the adult, literature. Published guidelines (Berntson et al., 1997; Task Force, 1996) and recommendations from the wider HRV methodology literature (e.g., Hejjel & Roth, 2004; Merri et al., 1990; Riniolo & Porges, 1997; Singh et al., 2004) emphasize the importance of using an “adequate” ECG sampling rate for both interpretive and replication purposes. It is possible that an adequate sampling rate may be age- (e.g., infant vs. adult) or sample-specific (e.g., nonclinical vs. clinical; Singh et al., Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

2004). Thus, a threshold in R-wave detection may exist across different groups that require the sampling rate to be adjusted for a given characteristic. As newer ECG recording hardware becomes available with assorted sampling rates (e.g., 128 Hz, 500 Hz, 1,000 Hz), it will be pertinent that future studies examine the relation between sampling rate and HRV reliability.

Electrocardiogram signal preprocessing was associated with HRV reliability among infant/toddler studies. Manual artifact editing of ECG data was associated with significantly higher NN reliability, while digital automated filtering was associated with significantly higher HF reliability.

Similar results were not found among child/adolescent studies. Artifacts and recording errors can significantly lengthen or shorten NN-intervals, inflate estimates of variability, and decrease reliability (Berntson & Stowell, 1998; Mulder, 1992). As such, lower reliability of NN with unedited data is not unexpected. The higher reliability of HF associated with filtering ECG data among infant/toddler, but not child/adolescent studies could be attributable to differences in the frequency of heart rate artifacts between the two groups, and/or the prominent use of a moving polynomial filter in infant/toddler studies (Porges, 1985), which may better account for extraneous variance attributed to artifacts (e.g., spectral leakage) or errors from poor data resolution (e.g., inadequate sampling rate). Compared to infant/toddler studies, child/adolescent studies reported using digital filters less frequently, but had greater variability among the filter types used. Results from infant/toddler studies demonstrated that carefully applied editing and filtering is associated with better HRV reliability in pediatric samples, and suggest that further examination of how editing and filtering NN-interval data affects pediatric HRV reliability is warranted.

Heart Rate Variability Analyses Longer analyzed ECG recording duration was associated with lower NN reliability among infant/toddler studies, and higher SDNN, rMSSD, HF, and LF reliability among child/adolescent studies. Infant/toddler studies typically analyzed 2–5 min ECG recordings for HRV, while child/ adolescent studies typically analyzed 3 min recordings.

Although these recording durations are less than the typical 5 min standard, results of this meta-analysis suggest that baseline HF can be reliably measured with recordings as short as 2 min. Increased LF reliability with longer analyzed durations is consistent with findings reported by Marks and Lightfoot (1999). Meta-analytic results also indicated that HF reliability among infant/toddler studies, but not child/ adolescent studies, was relatively higher when measures were derived from the mean of multiple epochs. These results are consistent with earlier findings from infant Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

23

(Richards, 1995) and adult studies (McNames & Aboy, 2006). Multiple 30 s epochs were typically reported across both age groups. HRV reliability may be influenced by the interaction between longer analyzed ECG recording durations and deriving HRV using multiple epochs; this could not be tested using nested moderator analyses due to limited available data. It will be important for pediatric researchers to further examine how HRV reliability is impacted by the direct and interactive effects of analyzed recording duration (e.g., 5 min, 10 min, 15 min) with epoch length (e.g., one 10 min epoch vs. two 5 min epochs). Careful consideration of recording duration and epoch length will help inform HRV analytical decisions for pediatric research and extend recommended HRV guidelines.

Selection of an age-specific HRV frequency bandwidth consistent with available guidelines and recommendations, as opposed to alternative bandwidths or unmentioned, did not alter HF reliability among infant/toddler studies, but was associated with relatively higher HF, LF, and LF:HF reliability, among child/adolescent studies. For example, child/adolescent studies that used the HF frequency bandwidth recommended for adults (i.e., 0.15–0.40 Hz; Berntson et al., 1997; Task Force, 1996) yielded comparatively higher reliability than studies that used wider (e.g., 0.20–1.00 Hz; Gentzler et al., 2012) or narrower (e.g., 0.20–0.30 Hz; Mezzacappa et al., 1997) bandwidths. Recommendations for pediatric HRV bandwidths only exist for HF among infants and toddlers up to age 4 years (i.e., 0.30– 1.30 Hz for infants, Fox & Porges, 1985; 0.24–1.04 Hz for children under 4 years, Bar-Haim et al., 2000); these wider frequency bandwidths capture the influence of faster and more chaotic heart rate and respiratory patterns. Results suggested that the frequency bandwidths recommended for adults may not be unsuitable for deriving reliable measures of HF and LF among children and adolescents.

Importantly, although the use of adult frequency bandwidths was associated with higher reliability, this result does not imply these HRV measures are necessarily valid. The lack of frequency bandwidth guidelines for use among children and adolescents is a noted gap in the literature. Thus, it is recommended that future research compare HRV measures across a range of frequency bands to determine an optimal bandwidth for frequency-domain measures obtained from children and youth. Systematically examining these measurement and methodological nuances will facilitate maximizing the psychometrics and interpretability of pediatric HRV data, and may culminate in the establishment of pediatric HRV recommendation guidelines.

in the pediatric HRV literature. Thus, the results must be interpreted in light of the coding decisions. The initial selection of which moderator variables to code was informed by recommendations in HRV guidelines (Berntson et al., 1997; Task Force, 1996), the wider HRV methodology literature, and decisions used in a similar methodological review (Heathers, 2014). Due to the available data, several moderator variables of interest had to be collapsed into broader categories than initially planned. For example, rather than being able to precisely code the time of the ECG recording, reported information limited the coding to merely “same” versus “different” recording time categories. As another example, exact ECG sampling rates were often not reported, so coding was constrained to the categories of < 500 Hz, 500 Hz, or unmentioned. These reporting limitations precluded examination of several moderators, as well as nested moderator analyses, that may have explained remaining heterogeneity or elucidated potential moderator interactions. Relatedly, the preponderance of studies only reported HF, thus, the reliability of time- versus frequencydomain measures could not be meaningfully compared.

Second, the present meta-analysis focused on short-term, daytime measures of time- and frequency-domain HRV obtained from baseline recordings. Future studies should examine pediatric HRV reliability among measures derived using longer ambulatory recordings (e.g., 24 hr), alternative analytical methods (e.g., geometric, wavelet, entropy), and reactivity protocols, each of which have been associated with health outcomes. Future efforts to replicate these findings using HRV data derived under different analytical contexts are warranted to determine the generalizability of these findings.

Third, examinations using fixed-effects meta-analytic models may limit the generalizability of these results compared to random-effects models, which assume that effect size variability is due to both random variance and sampling error (Rosenberg et al., 2000). Post hoc randomeffects analyses (not shown for parsimony) generally yielded smaller effect sizes, nonsignificant heterogeneity analyses, and fewer significant moderators. However, the difference in effect sizes magnitude was typically small, moderate reliability was still generally observed, and most moderator variables remained significant across fixedand random-effects models. Given that the coded variables captured the range of possible methodological options, a fixed-effects model was considered appropriate.

Limitations

Taken together, this meta-analysis demonstrates that timeand frequency-domain HRV measures exhibit moderate and generally robust reproducibility over time, and provides initial empirical support for the application of certain adult

There are three limitations that merit consideration. First, failure to report key methodological details was pervasive Ó 2016 Hogrefe Publishing

Conclusion

Journal of Psychophysiology (2017), 31(1), 6–28

24

guidelines to pediatric studies. Reliability among child/ adolescent studies was significantly higher compared to infant/toddler studies. Greater reliability among infant/ toddler studies was largely associated with sample exclusion criteria (medical illness/condition, prescription medications); prerecording acclimatization periods; recording ECG while participants are awake and seated; and, analyzing HRV using multiple epochs. Greater reliability among child/adolescent studies was largely associated with sample exclusion criteria (medical illness/condition); smaller proportion of males; longer recording durations; prerecording acclimatization periods; and, age-specific frequency bandwidth selection. Consistent with HRV guidelines, it is recommended that pediatric HRV study authors report precise protocol details (e.g., recording time of day), sample characteristics (e.g., anthropometrics, pubertal status), ECG signal recording and preprocessing details (e.g., ECG sampling rate, artifact editing), and HRV analytical decisions (e.g., length of recording duration analyzed, epoch length). More rigorous reporting would facilitate research standardization, improve the interpretability and replicability of study findings, and permit more comprehensive meta-analytic comparisons in the future. Overall, these meta-analytic results have potential to make important empirical contributions by informing future researchers about the salient factors relevant to pediatric HRV methodology, extending current guidelines to include consideration of changes in HRV across childhood development (e.g., pubertal growth), and ultimately improving HRV psychometrics across studies of adult and pediatric health.

Acknowledgments This work was partly supported by the Canadian Institutes of Health Research (J. McGrath OCO79897, MOP89886, MSH95353). Jinshia Ly independently coded articles to enable estimation of inter-rater agreement; the authors express their sincere gratitude. The authors also wish to thank Jean-Philippe Gouin and Lucie Bonneville for their insightful comments on earlier drafts of this manuscript, as part of O.W.’s Master’s thesis. Preliminary findings were presented during an oral presentation at the 2015 Annual Scientific Meeting of the American Psychosomatic Society in Savannah, Georgia.

Disclosure Statements All authors disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

References Akinci, A., Celiker, A., Baykal, E., & Tezic, T. (1993). Heart rate variability in diabetic children: Sensitivity of the time- and frequency-domain methods. Pediatric Cardiology, 14, 140–146. doi: 10.1007/BF00795641 Akselrod, S., Eliash, S., Oz, O., & Cohen, S. (1985). Hemodynamic regulation in SHR: Investigation by spectral analysis. American Journal of Physiology, 253, H176–83.

Akselrod, S., Gordon, D., Ubel, F., Shannon, D., Barger, A., & Cohen, R. (1981). Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control.

Science, 213, 220–222. doi: 10.1126/science.6166045 Antelmi, I., de Paula, R. S., Shinzato, A. R., Peres, C. A., Mansur, A. J., & Grupi, C. J. (2004). Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. The American Journal of Cardiology, 93, 381–385. doi: 10.1016/j.amjcard.2003.09.065 Armstrong, R. G., Kenny, G. P., Green, G., & Seely, A. J. E. (2011).

Diurnal variation in heart rate variability before and after maximal exercise testing. Chronobiology International, 28, 344–351. doi: 10.3109/07420528.2011.559674 Baharav, A., Kotagal, S., Rubin, B. K., Pratt, J., & Akselrod, S. (1999). Autonomic cardiovascular control in children with obstructive sleep apnea. Clinical Autonomic Research, 9, 345–351. doi: 10.1007/BF02318382 Berntson, G. G., Bigger, J. R. Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., . . . Van Der Molen, M. W. (1997).

Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34, 623–648. doi: 10.1111/j.14698986.1997.tb02140.x Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1991). Autonomic determinism: The modes of autonomic control, the doctrine of autonomic space, and the laws of autonomic constraint.

Psychological Review, 98, 459–487. doi: 10.1037/0033/ 295X.98.4.459 Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1993). Respiratory sinus arrhythmia: Autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology, 30, 183–196. doi: 10.1111/j.1469-8986.1993.tb01731.x Berntson, G. G., & Quigley, K. S. (1990). An approach to artifact identification: Application to heart period data. Psychophysiology, 27, 586–598. doi: 10.1111/j.1469-8986.1990.tb01982.x Berntson, G. G., Quigley, K. S., & Lozano, D. (2007). Cardiovascular Psychophysiology. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), The Handbook of Psychophysiology (3rd ed., pp. 182–210). New York, NY: Cambridge University Press.

Berntson, G. G., & Stowell, J. R. (1998). ECG artifacts and heart period variability: Don’t miss a beat! Psychophysiology, 35, 127–132. doi: 10.1017/S0048577298001541 Boer-Martins, L., Figueiredo, V. N., Demacq, C., Martins, L. C., Consolin-Colombo, F., Figueiredo, M. J., . . . Moreno, H. Jr. (2011). Relationship of autonomic imbalance and circadian disruption with obesity and type 2 diabetes in resistant hypertensive patients. Cardiovascular Diabetology, 10, 1–12. doi: 10.1186/1475-2840-10-24 Boyles, A. L., Harris, S. F., Rooney, A. A., & Thayer, K. A. (2011).

Forest plot viewer: A new graphing tool. Epidemiology, 22, 746–747. doi: 10.1097/EDE.0b013e318225ba48 Buchhorn, R., Conzelmann, A., Willaschek, C., Störk, D., Taurines, R., & Renner, T. J. (2012). Heart rate variability and methylphenidate in children with ADHD. ADHD Attention Deficit Hyperactivity Disorders, 4, 85–91. doi: 10.1007/s12402-012-0072-8

Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Cacioppo, J. T., Berntson, G. G., Binkley, P. F., Quigley, K. S., Uchino, B. N., & Fieldstone, A. (1994). Autonomic cardiac control. II. Noninvasive indices and basal response as revealed by autonomic blockades. Psychophysiology, 31, 586–598. doi: 10.1111/j.1469-8986.1994.tb02351.x Cerutti, S., Goldberger, A. L., & Yamamoto, Y. (2006). Recent advances in heart rate variability signal processing and interpretation. IEEE Transactions on Biomedical Engineering, 53, 1–3. doi: 10.1109/TBME.2005.859777 Chen, X., & Mukkamala, R. (2008). Selective quantification of the cardiac sympathetic and parasympathetic nervous systems by multisignal analysis of cardiorespiratory variability. American Journal of Physiology, 294, 362–371. doi: 10.1152/ajpheart.

01061.2007 Cipryan, L., & Litschmannova, M. (2013). Intra-day and inter-day reliability of heart rate variability measurement. Journal of Sports Sciences, 31, 150–158. doi: 10.1080/02640414.2012.

721931 Cohen, R. J., & Swerdlik, M. E. (2002). Psychological testing and assessment: An introduction to tests and measurement (5th ed.). New York, NY: McGraw-Hill.

Dalla Pozza, R., Fuchs, A., Bechtold, S., Kozlik-Feldmann, R., Daebritz, S., & Netz, H. (2006). Short-term testing of heart rate variability in heart-transplanted children: Equal to 24-h ECG recordings? Clinical Transplantation, 20, 438–442. doi: 10.1111/j.1399-0012.2006.00502.x0 Denver, J., Reed, S., & Porges, S. (2007). Methodological issues in the quantification of respiratory sinus arrhythmia.

Biological Psychology, 74, 286–294. doi: 10.1016/j.biopsycho.

2005.09.005 El-Sheikh, M., & Hinnant, J. B. (2011). Marital conflict, respiratory sinus arrhythmia, and allostatic load: Interrelations and associations with the development of children’s externalizing behavior. Development and Psychopathology, 23, 815–829. doi: 10.1017/S0954579411000320 Ernst, G. (2014). Heart Rate Variability. London, UK: SpringerVerlag.

Faulkner, M. S., Hathaway, D., & Tolley, B. (2003). Cardiovascular autonomic function in healthy adolescents. Heart & Lung: Journal of Critical Care, 32, 10–22. doi: 10.1067/mhl.2003.6 Fox, N. A., & Porges, S. W. (1985). The relation between neonatal heart period patterns and developmental outcome. Child Development, 56, 28–37. doi: 10.1111/j.1467-8624.1985. tb00082.x Guo, Y., & Stein, P. (2003). Circadian rhythm in the cardiovascular system: Chronocardiology. American Heart Journal, 145, 779–786. doi: 10.1016/S0002-8703(02)00088-1 Heathers, J. A. J. (2014). Everything hertz: Methodological issues in short-term frequency-domain HRV. Frontiers in Physiology, 5, 177–177. doi: 10.3389/fphys.2014.00177 Hejjel, L., & Roth, E. (2004). What is the adequate sampling interval of the ECG signal for heart rate variability analysis in the time domain? Physiological Measurement, 25, 1405–1411. doi: 10.1088/0967-3334/25/6/006 Henry, B., Minassian, A., & Paulus, M. (2010). Heart rate variability in bipolar mania and schizophrenia. Journal of Psychiatric Research, 44, 1–19. doi: 10.1016/j.jpsychires.2009.07.011.Heart Hoppenbrouwers, T., Oliveira, F., Sandarupa, S., Khoo, M., Neuman, M., & Ramanathan, R. (2012). The development of the circadian heart rate rhythm (CHR) in Asian infants. Early Human Development, 88, 555–561. doi: 10.1016/j.earlhumdev.2011.12.031 Huikuri, H. V., Kessler, K. M., Terracall, E., Castellanos, A., Linnaluoto, M. K., & Myerburg, R. J. (1990). Reproducibility and circadian rhythm of heart rate variability in healthy subjects. The American Journal of Cardiology, 65, 391–393. doi: 10.1016/0002-9149(90)90308-N

Ó 2016 Hogrefe Publishing

25

Jarrin, D. C., McGrath, J. J., Giovanniello, S., Poirier, P., & Lambert, M. (2012). Measurement fidelity of heart rate variability signal processing: The devil is in the details. International Journal of Psychophysiology, 86, 88–97. doi: 10.1016/j.ijpsycho.2012.07.004 Jarrin, D. C., McGrath, J. J., Poirier, P., Seguin, L., Tremblay, R. E., Montplaisir, J. Y., . . . Seguin, J. R. (2015). Short-term heart rate variability in a population-based sample of 10-year-old children. Pediatric Cardiology, 36, 41–48. doi: 10.1007/s00246014-0962-y Jennings, J. R., Berg, W. K., Hutcheson, J., Obrist, P., Porges, S., & Turpin, G. (1981). Publication guidelines for heart rate studies in man. Psychophysiology, 18, 226–231. doi: 10.1111/j.14698986.1981.tb03023.x Kajantie, E., & Phillips, D. I. W. (2006). The effects of sex and hormonal status on the physiological response to acute psychosocial stress. Psychoneuroendocrinology, 31, 151–178. doi: 10.1016/j.psyneuen.2005.07.002 Kim, K. K., Lim, Y. G., Kim, J. S., & Park, K. S. (2007). Effect of missing RR-interval data on heart rate variability analysis in the time domain. Physiological Measurement, 28, 1485–1494. doi: 10.1088/0967-3334/28/12/003 Kleiger, R. E., Stein, P. K., & Bigger, J. T. (2005). Heart rate variability: Measurement and clinical utility. Annals of Noninvasive Electrocardiology, 10, 88–101. doi: 10.1111/j.1542474X.2005.10101.x Kochiadakis, G. E., Orfanakis, A. E., Rombola, A. T., Chrysostomakis, S. I., Chlouverakis, G. I., & Vardas, P. E. (1997). Reproducibility of time-domain indexes of heart rate variability in patients with vasovagal syncope. The American Journal of Cardiology, 79, 160–165. doi: 10.1016/S0002-9149(96)00704-7 Kowalewski, M. A., & Urban, M. (2004). Short- and long-term reproducibility of autonomic measures in supine and standing positions. Clinical Science, 106, 61–66. doi: 10.1042/ CS20030119 Latchman, P. L., Mathur, M., Bartels, M. N., Axtell, R. S., & De Meersman, R. E. (2011). Impaired autonomic function in normotensive obese children. Clinical Autonomic Research, 21, 319–323. doi: 10.1007/s10286-011-0116-8 Lee, T.-C., & Chiu, H.-W. (2010). Frequency-domain heart rate variability analysis performed by digital filters. Computing in Cardiology, 37, 589–592.

Malik, M. (1997). Time domain measurements of heart rate variability. Cardiac Electrophysiology Review, 3, 329–334. doi: 10.1023/A:1009912905325 Marks, B. L., & Lightfoot, J. T. (1999). Reproducibility of resting heart rate variability with short sampling periods. Canadian Journal of Applied Physiology, 24, 337–348. doi: 10.1139/h99-026 Martini, G., Riva, P., Rabbia, F., Molini, V., Ferrero, G. B., Cerutti, F., . . . Veglio, F. (2001). Heart rate variability in childhood obesity.

Clinical Autonomic Research, 11, 87–91. doi: 10.1007/ BF02322051 Massin, M., Maeyns, K., Withofs, N., Ravet, F., & Gerard, P. (2000).

Circadian rhythm of heart rate and heart rate variability.

Archives of Disease in Childhood, 83, 179–182. doi: 10.1136/ adc.83.2.179 McNames, J., & Aboy, M. (2006). Reliability and accuracy of heart rate variability metrics versus ECG segment duration. Medical and Biological Engineering and Computing, 44, 747–756. doi: 10.1007/s11517-006-0097-2 Merri, M., Farden, D. C., Mottley, J. G., & Titlebaum, E. L. (1990).

Sampling frequency of the electrocardiogram for spectral analysis of the heart rate variability. IEEE Transactions on Bio-Medical Engineering, 37, 99–106. doi: 10.1109/10.43621 Mulder, L. J. (1992). Measurement and analysis methods of heart rate and respiration for use in applied environments. Biological Psychology, 34, 205–236. doi: 10.1016/0301-0511(92)90016-N

Journal of Psychophysiology (2017), 31(1), 6–28

26

Ordaz, S., & Luna, B. (2012). Sex differences in physiological reactivity to acute psychosocial stress in adolescence. Psychoneuroendocrinology, 37, 1135–1157. doi: 10.1016/j.psyneuen.

2012.01.002.Sex Patton, G. C., & Vines, R. (2007). Pubertal transitions in health.

Lancet, 369, 1130–1139. doi: 10.1016/50140-6736(07)60366-3 Penttilä, J., Kuusela, T., & Scheinin, H. (2005). Analysis of rapid heart rate variability in the assessment of anticholinergic drug effects in humans. European Journal of Clinical Pharmacology, 61, 559–565. doi: 10.1007/s00228-005-0953-2 Porges, S. W. (1985). Method and apparatus for evaluating rhythmic oscillations in aperiodic physiological response systems. US Patent 4,510,944.

Porges, S. W., & Byrne, E. A. (1992). Research methods for measurement of heart rate & respiration. Biological Psychology, 34, 93–130. doi: 10.1016/0301-0511(92)90012-J Reyes del Paso, G. A., Langewitz, W., Mulder, L. J. M., van Roon, A., & Duschek, S. (2013). The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: A review with emphasis on a reanalysis of previous studies. Psychophysiology, 50, 477–487. doi: 10.1111/psyp.12027 Richards, J. (1995). Reliability of respiratory sinus arrhythmia in R-R intervals in 14-, 20-, and 26-week-old infants. Infant Behavior and Development, 18, 155–161. doi: 10.1016/01636383(95)90045-4 Riniolo, T., & Porges, S. (1997). Inferential and descriptive influences on measures of respiratory sinus arrhythmia: Sampling rate, R-wave trigger accuracy, and variance estimates. Psychophysiology, 34, 613–621. doi: 10.1111/j.1469-8986.1997.tb01748.x Rosenberg, M. S., Adams, D., & Gurevitch, J. (2000). MetaWin. Statistical software for meta-analysis, version 2.0. Sunderland, MA: Sinauer Associates.

Rosenthal, R. (1994). Parametric Measures of Effect Size. In H. Cooper & L. V. Hedges (Eds.), The Handbook of Research Synthesis (pp. 231–244). New York, NY: Russel Sage Foundation.

Salo, M. A., Huikuri, H. V., & Seppänen, T. (2001). Ectopic beats in heart rate variability analysis: Effects of editing on time and frequency domain measures. Annals of Noninvasive Electrocardiology, 6, 5–17. doi: 10.1111/j.1542-474X.2001. tb00080.x Sandercock, G. R. H., Bromley, P. D., & Brodie, D. A. (2005). The reliability of short-term measurements of heart rate variability.

International Journal of Cardiology, 103, 238–247. doi: 10.1016/ j.ijcard.2004.09.013 Schmid, K., Schönlebe, J., Drexler, H., & Mueck-Weymann, M. (2010). Associations between being overweight, variability in heart rate, and well-being in the young men. Cardiology in the Young, 20, 54–59. doi: 10.1017/S1047951109992009 Shaffer, F., & Combatalade, D. C. (2013). Don’t add or miss a beat: A guide to cleaner heart rate variability recordings. Biofeedback, 41, 121–130. doi: 10.5298/1081-5937-41.3.04 Sharpley, C. F. (1993). Effects of brief rest periods upon heart rate in multiple baseline studies of heart rate reactivity. Biofeedback and Self-Regulation, 18, 225–235. doi: 10.1007/BF00999081 Shirtcliff, E. A., Dahl, R. E., & Pollak, S. D. (2009). Pubertal development: Correspondence between hormonal and physical development. Child Development, 80, 327–337. doi: 10.1111/j.

1467-8624.2009.01263.x

Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Silvetti, M. S., Drago, F., & Ragonese, P. (2001). Heart rate variability in healthy children and adolescents is partially related to age and gender. International Journal of Cardiology, 81, 169–174. doi: 10.1016/S0167-5273(01)00537-X Singh, B., Singh, M., & Banga, V. K. (2014). Sample entropy based HRV: Effect of ECG sampling frequency. Biomedical Science and Engineering, 2, 68–72. doi: 12.12691/bse-2-3-3 Singh, D., Vinod, K., & Saxena, S. C. (2004). Sampling frequency of the RR interval time series for spectral analysis of heart rate variability. Journal of Medical Engineering & Technology, 28, 263–272. doi: 10.1080/03091900410001662350 Sookan, T., & McKune, A. J. (2012). Heart rate variability in physically active individuals: Reliability and gender characteristics. Cardiovascular Journal of Africa, 23, 67–72. doi: 10.5830/ CVJA-2011-018 Spear, L. P. (2000). The adolescent brain and age-related behavioral manifestations. Neuroscience & Biobehavioral Review, 24, 417–463. doi: 10.1016/S0149-7634(00)00014-2 Swain, A., & Suls, J. (1996). Reproducibility of blood pressure and heart rate reactivity: A meta-analysis. Psychophysiology, 33, 162–174. doi: 10.1111/j.1469-8986.1996.tb02120.x Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17, 354–381.

Thayer, J. F., & Sternberg, E. (2006). Beyond heart rate variability: Vagal regulation of allostatic systems. Annals of the New York Academy of Science, 1088, 361–372. doi: 10.1196/ annals.1366.014 Williams, K., Thomson, D., Seto, I., Contopoulos-Ioannidis, D. G., Ioannidis, J. P. A, Curtis, S., . . . Klassen, T. (2012). Standard 6: Age groups for pediatric trials. Pediatrics, 129, S153–S160. doi: 10.1542/peds.2012-0055I Wittling, W., & Wittling, R. A. (2012). Herzschlagvariabilität: Frühwarnsystem, Stress- und Fitnessindikator [Heart rate variability: Early warning system, stress and fitness indicator].

Heiligenstadt: Eichsfeld-Verlag.

Xie, G. L., Wang, J. H., Zhou, Y., Xu, H., Sun, J. H., & Yang, S. R. (2013). Association of high blood pressure with heart rate variability in children. Iranian Journal of Pediatrics, 23, 37–44.

Young, F., & Leicht, A. (2011). Short-term stability of resting heart rate variability: Influence of position and gender. Applied Physiology, Nutrition, and Metabolism, 218, 210–218. doi: 10.1139/H10-103

Received December 19, 2014 Accepted July 26, 2015 Published online July 27, 2016 Jennifer J. McGrath Pediatric Public Health Psychology Laboratory Concordia University 7141 Sherbrooke Street West, SP244 Montreal, Quebec, H4B 1R6 Canada Tel. (514) 848-2424 ext. 5287 E-mail Jennifer.McGrath@concordia.ca

Ó 2016 Hogrefe Publishing

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Appendix References Included in the Meta-Analysis Alkon, A., Boyce, W. T., Davis, N. B., & Eskanazi, B. (2011).

Developmental changes in autonomic nervous system resting and reactivity measures in Latino children from 6 to 60 months of age. Journal of Developmental and Behavioral Pediatrics, 32(9), 668–677. doi: 10.1097/DBP.0b013e3182331fa6 Alkon, A., Lippert, S., Vujan, N., & Boyce, W. T. (2006). The ontogeny of autonomic measures in 6- and 12-month-old infants.

Developmental Psychobiology, 48, 197–208. doi: 10.1002/dev Arendt, R. E., Halpern, L. F., MacLean, W. E., & Youngquist, G. A. (1991). The properties of V in newborns across repeated measures. Developmental Psychobiology, 24(2), 91–101. doi: 10.1002/dev.420240203 Bar-Haim, Y., Marshall, P. J., & Fox, N. A. (2000). Developmental changes in heart period and high-frequency heart period variability from 4 months to 4 years of age. Developmental Psychobiology, 37(1), 44–56. doi: 10.1002/1098-2302(200007) 37:1<44::AID-DEV6>3.0.CO;2-7 Blandon, A. Y., Calkins, S. D., Keane, S. P., & O’Brien, M. (2010).

Contributions of child’s physiology and maternal behavior to children’s trajectories of temperamental reactivity. Developmental Psychology, 46(5), 1089–1102. . doi: 10.1037/a0020678.

Contributions Bornstein, M. H., & Suess, P. E. (2000a). Physiological selfregulation and information processing in infancy: Cardiac vagal tone and habituation. Child Development, 71(2), 273–287. doi: 10.1111/1467-8624.00143 Bornstein, M. H., & Suess, P. E. (2000b). Child and mother cardiac vagal tone: Continuity, stability, and concordance across the first 5 years. Developmental Psychology, 36(1), 54–65. doi: 10.1037/0012-1649.36.1.54 Calkins, S. D., & Keane, S. P. (2004). Cardiac vagal regulation across the preschool period: Stability, continuity, and implications for childhood adjustment. Developmental Psychobiology, 45(3), 101–112. doi: 10.1002/dev.20020 Cavaliere, C. M. (2011). Role of the vagal system in the regulatory sensory processing patterns in children with autistic spectrum disorder: Physiologic underpinnings and reliability of measurement. (Unpublished doctoral dissertation). Seton Hall University, South Orange, New Jersey. Retrieved from http://www. proquest.com Dietrich, A., Rosmalen, J. G. M., Althaus, M., van Roon, A. M., Mulder, L. J. M., Minderaa, R. B., . . . Riese, H. (2010).

Reproducibility of heart rate variability and baroreflex sensitivity measurements in children. Biological Psychology, 85(1), 71–78. doi: 10.1016/j.biopsycho.2010.05.005 Doussard-Roosevelt, J. A, Montgomery, L. A., & Porges, S. W. (2003). Short-term stability of physiological measures in kindergarten children: Respiratory sinus arrhythmia, heart period, and cortisol. Developmental Psychobiology, 43(3), 230–242. . doi: 10.1002/dev.10136 El-Sheikh, M. (2005). Stability of respiratory sinus arrhythmia in children and young adolescents: A longitudinal examination.

Developmental Psychobiology, 46(1), 66–74. doi: 10.1002/ dev.20036 Elmore-Staton, L. D. (2011). Sleep and cognitive functioning: The moderating role of vagal indices. (Unpublished doctoral dissertation). Auburn University, Auburn, Alabama. Retrieved from http://www.proquest.com Farah, B. Q., Lima, A. H. R. D. A., Cavalcante, B. R., Oliveira, L. M. F. T., Brito, A. L. D. S., Barros, M. V. G., & Ritti-Dias, R. M. (2014).

Ó 2016 Hogrefe Publishing

27

Intra-individuals and inter- and intra-observer reliability of short-term heart rate variability in adolescents. Clinical Physiology and Functional Imaging, 36(1), 33-39. doi: 10.1111/ cpf.12190 Fox, N. A. (1983). Maturation of autonomic control in preterm infants. Developmental Psychobiology, 16(6), 495–504. doi: 10.1002/dev.420160605 Fox, N. A., & Field, T. M. (1989). Individual differences in preschool entry behavior. Journal of Applied Developmental Psychology, 10(4), 527–540. doi: 10.1016/0193-3973(89)90025-7 Fracasso, M. P., Porges, S. W., Lamb, M. E., & Rosenberg, A. A. (1994). Cardiac activity in infancy: Reliability and stability of individual differences. Infant Behavior and Development, 284(17), 277–284. doi: 10.1016/0163-6383(94)90006-X Galland, B. C., Taylor, B. J., Bolton, D. P. G., & Sayers, R. M. (2006).

Heart rate variability and cardiac reflexes in small for gestational age infants. Journal of Applied Physiology, 100(3), 933–939. doi: 10.1152/japplphysiol.01275.2005 Gamelin, F.-X., Baquet, G., Berthoin, S., Thevenet, D., Nourry, C., Nottin, S., & Bosquet, L. (2009). Effect of high intensity intermittent training on heart rate variability in prepubescent children. Euro Journal of Applied Physiology, 105(5), 731–738. doi: 10.1007/s00421-008-0955-8 Gentzler, A. L., Rottenberg, J., Kovacs, M., George, C. J., & Morey, J. N. (2012). Atypical development of resting respiratory sinus arrhythmia in children at high risk for depression. Developmental Psychobiology, 54(5), 556–567. doi: 10.1002/dev.20614 Hastings, P. D., Nuselovici, J. N., Utendale, W. T., Coutya, J., McShane, K. E., & Sullivan, C. (2008). Applying the polyvagal theory to children’s emotion regulation: Social context, socialization, and adjustment. Biological Psychology, 79(3), 299–306. doi: 10.1016/j.biopsycho.2008.07.005 Henje Blom, E., Olsson, E. M. G., Serlachius, E., Ericson, M., & Ingvar, M. (2009). Heart rate variability is related to selfreported physical activity in a healthy adolescent population.

European Journal of Applied Physiology, 106(6), 877–883. doi: 10.1007/s00421-009-1089-3 Hinnant, J. B., & El-Sheikh, M. (2009). Children’s externalizing and internalizing symptoms over time: The role of individual differences in patterns of RSA responding. Journal of Abnormal Child Psychology, 37(8), 1049–1061. doi: 10.1007/s10802-0099341-1 Hinnant, J. B., Elmore-Staton, L., & El-Sheikh, M. (2011). Developmental trajectories of respiratory sinus arrhythmia and preejection period in middle childhood. Developmental Psychobiology, 53(1), 59–68. doi: 10.1002/dev.20487 Hsu, H.-C., & Porter, C. L. (2004). Young infants’ behavioral reactivity to mild perturbation: Developmental continuity, stability, and organization. Infancy, 6(1), 95–120. . doi: 10.1207/ s15327078in0601_5 Izard, C. E., Porges, S. W., Simons, R. F., Haynes, O. M., Hyde, C., Parisi, M., & Cohen, B. (1991). Infant cardiac activity: Developmental changes and relations with attachment. Developmental Psychology, 27(3), 432–439. doi: 10.1037/0012-1649.27.3.432 Keller, P. S., & El-Sheikh, M. (2009). Salivary alpha-amylase as a longitudinal predictor of children’s externalizing symptoms: Respiratory sinus arrhythmia as a moderator; of effects.

Psychoneuroendocrinology, 34(5), 633–643. doi: 10.1016/ j.psyneuen.2008.12.016 Keller, P. S., Kouros, C. D., Erath, S. A., Dahl, R. E., & El-Sheikh, M. (2014). Longitudinal relations between maternal depressive symptoms and child sleep problems: The role of parasympathetic nervous system reactivity. Journal of Child Psychology and Psychiatry, 55(2), 172–179. doi: 10.1111/jcpp.12151 Kennedy, A. E., Rubin, K. H., Hastings, P. D., & Maisel, B. (2004).

Longitudinal relations between child vagal tone and parenting

Journal of Psychophysiology (2017), 31(1), 6–28

28

behavior: 2 to 4 years. Developmental Psychobiology, 45(1), 10–21. doi: 10.1002/dev.20013 Leicht, A. S., & Allen, G. D. (2008). Moderate-term reproducibility of heart rate variability during rest and light to moderate exercise in children. Brazilian Journal of Medical and Biological Research, 41(7), 627–633. doi: 10.1590/S0100879X2008000700013 Liew, J., Eisenberg, N., Spinrad, T. L., Eggum, N. D., Haugen, R. G., Kupfer, A., . . . Baham, M. E. (2011). Physiological regulation and fearfulness as predictors of young children’s empathy-related reactions. Social Development, 20(1), 111–113. doi: 10.1111/ j.1467-9507.2010.00575.x Marshall, P. J., & Stevenson-Hinde, J. (1998). Behavioral inhibition, heart period, and respiratory sinus arrhythmia in young children. Developmental Psychobiology, 33(3), 283–292. doi: 10.1002/(SICI)1098-2302(199811)33:3 Mezzacappa, E., Tremblay, R. E., Kindlon, D., Saul, J. P., Arseneault, L., Seguin, J., . . . Earls, F. (1997). Anxiety, antisocial behavior, and heart rate regulation in adolescent males.

Journal of Child Psychology and Psychiatry, 38(4), 457–469. doi: 10.1111/j.1469-7610.1997.tb01531.x Patriquin, M. A., Lorenzi, J., Scarpa, A., & Bell, M. A. (2014).

Developmental trajectories of respiratory sinus arrhythmia: Associations with social responsiveness. Developmental Psychobiology, 56, 317–326. doi: 10.1002/dev.21100 Perry, N. B., Nelson, J. A, Swingler, M. M., Leerkes, E. M., Calkins, S. D., Marcovitch, S., & O’Brien, M. (2013). The relation between maternal emotional support and child physiological regulation across the preschool years. Developmental Psychobiology, 55 (4), 382–394. doi: 10.1002/dev.21042 Porges, S. W. (2014). Unpublished raw data.

Porges, S. W., Doussard-Roosevelt, J. A, Portales, A. L., & Suess, P. E. (1994). Cardiac vagal tone: Stability and relation to difficultness in infants and 3-year-olds. Developmental Psychobiology, 27(5), 289–300. doi: 10.1002/dev.420270504 Porter, C. L., Bryan, Y. E., & Hsu, H.-C. (1995). Physiological markers in early infancy: Stability of 1-to 6-month vagal tone.

Infant Behavior and Development, 18(3), 363–367. doi: 10.1016/ 0163-6383(95)90025-X Propper, C., Moore, G. A., Mills-Koonce, W. R., Halpern, C. T., HillSoderlund, A. L., Calkins, S. D., . . . Cox, M. (2008). Geneenvironment contributions to the development of infant vagal reactivity: The interaction of dopamine and maternal sensitivity.

Child Development, 79(5), 1377–1394. doi: 10.1111/j.14678624.2008.01194.x

Journal of Psychophysiology (2017), 31(1), 6–28

O. M. Weiner & J. J. McGrath, Pediatric HRV Reliability

Richards, J. E. (1989). Development and stability in visual sustained attention in 14, 20, and 26 week old infants.

Psychophysiology, 26(4), 422–430. doi: 10.1111/j.1469-8986.

1989.tb01944.x Rigterink, T., Fainsilber Katz, L., & Hessler, D. M. (2010). Domestic violence and longitudinal associations with children’s physiological regulation abilities. Journal of Interpersonal Violence, 25 (9), 1669–1683. doi: 10.1177/0886260509354589 Salomon, K. (2005). Respiratory sinus arrhythmia during stress predicts resting respiratory sinus arrhythmia 3 years later in a pediatric sample. Health Psychology, 24(1), 68–76. doi: 10.1037/0278-6133.24.1.68 Schuetze, P., Eiden, R. D., & Edwards, E. P. (2009). A longitudinal examination of physiological regulation in cocaine-exposed infants across the first 7 months of life. Infancy, 14(1), 19–43. doi: 10.1080/15250000802569660 Snidman, N., Kagan, J., Riordan, L., & Shannon, D. C. (1995).

Cardiac function and behavioral reactivity during infancy.

Psychophysiology, 32(3), 199–207. Retrieved from http://www. ncbi.nlm.nih.gov/pubmed/7784528 Stifter, C. A., & Jain, A. (1996). Psychophysiological correlates of infant temperament: Stability of behavior and autonomic patterning from 5 to 18 months. Developmental Psychobiology, 29, 379–391. doi: 10.1002/(SICI)1098-2302(199605)29:4<379:: AID-DEV5>3.0.CO;2-N Stifter, C. A., & Fox, N. A. (1990). Infant reactivity: Physiological correlates of newborn and 5-month temperament.

Developmental Psychology, 26(4), 582–588. doi: 10.1037/ 0012-1649.26.4.582 Winegust, A. K., Mathewson, K. J., & Schmidt, L. A. (2014). Testretest reliability of frontal alpha electroencephalogram (EEG) and electrocardiogram (ECG) measures in adolescents: A pilot study. International Journal of Neuroscience, 124(12), 908-911. doi: 10.3109/00207454.2014.895003 Winsley, R. J., Armstrong, N., Bywater, K., & Fawkner, S. G. (2003).

Reliability of heart rate variability measures at rest and during light exercise in children. British Journal of Sports Medicine, 37 (6), 550–552. doi: 10.1136/bjsm.37.6.550 Yiallourou, S. R., Sands, S. A., Walker, A. M., & Horne, R. S. C. (2012). Maturation of heart rate and blood pressure variability during sleep in term-born infants. Sleep, 35(2), 177–186. doi: 10.5665/sleep.1616

Ó 2016 Hogrefe Publishing

Article

Differences in Pre-Attentive Processes of Sound Intensity Change Between High- and Low-Sensation Seekers A Mismatch Negativity Study Siqi He,1 Yao Chai,1 Jinbo He,1,2 Yongyu Guo,1 and Risto Näätänen3,4,5 1

Key Laboratory of Adolescent Cyberpsychology and Behavior of Ministry of Education, School of Psychology, Central China Normal University, Wuhan, PR China

2

Center of Sports Psychology, Wuhan Institute of Physical Education, Wuhan, PR China Department of Psychology, University of Tartu, Estonia

3 4

Center of Functionally Integrative Neuroscience (CFIN), University of Aarhus, Denmark

5

Institute of Behavioral Sciences, University of Helsinki, Finland Abstract: High-sensation seekers are prone to search for changing stimuli. Pre-attentive processes reveal the earliest cortical change detection in response to external stimulus changes. This study recorded the mismatch negativity (MMN) to intensity increments and decrements in a repetitive tone in high- and low-sensation seekers. It was found that the MMN amplitude for intensity-decrement deviants was larger in high- than low-sensation seekers. However, with regard to deviant-increment stimulation, the difference between the two groups was not significant. Consequently, the sensitivity of high-sensitivity seekers to pre-attentively detect a decrease in sound intensity is higher than that of low-sensation seekers.

Keywords: sensation seeking, sound intensity, pre-attentive detection, mismatch negativity (MMN)

Sensation seeking (SS) as a personality trait has drawn the attention of many researchers for its association with several youth behavioral problems. For example, previous research has shown that SS and impulsive control served as significant predictors of delinquency (Peach & Gaultney, 2013). The highest rates of delinquency were associated with high SS, high peer deviance, and low levels of parental monitoring (Mann, Kretsch, Tackett, Harden, & TuckerDrob, 2015). Rahmani and Lavasani (2011) revealed a significant positive relation between Internet dependency with overall seeking and its subscales of disinhibition and boredom susceptibility. LaBrie, Kenney, Napper, and Miller (2014) found that SS can predict drinking behavior. Social disinhibition, as an aspect of SS, also mediated the relationship between engagement in other risk behavior and alcohol use (Wilkinson et al., 2011). Moreover, high levels of SS were associated with increased risk for both alcohol and cannabis dependence (Kaynak et al., 2013). Ó 2016 Hogrefe Publishing

The construct of SS was first proposed by Zuckerman (1971), who defined it as a need to seek changing, novel, and complex stimulus and experiences; his definition indicated that seeking a changing stimulus is one of the primary characteristics of high-sensation seekers. However, the reason why high-sensation seekers are prone to seek changing stimulus remains unclear. Zuckerman (1994) proposed a model with three basic hypotheses: SS is a product of the evolutionary past; an abundant genetic evidence should be available for SS; and physical evidence can be found in the cognitive neural processes associated with SS. The first hypothesis has not been tested by objective experiments yet. The second hypothesis has got support from some behavioral genetic studies, which showed that the genetic effects on SS are ranging from 34% to 69%, accounting for a large part of the variance (Eysenck, 1983; Fulker, Eysenck, & Zuckerman, 1980; Hur & Bouchard, 1997; Koopmans, Boomsma, Heath, & van Doornen, 1995).

Journal of Psychophysiology (2017), 31(1), 29–37 DOI: 10.1027/0269-8803/a000168

30

The third hypothesis has got plenty of evidences from experimental studies.

Earlier studies focused on the differences in evoked potentials between high SS and low SS to investigate the physical mechanism of SS. It has been found that the evoked potential, including N1, P1, or N1/P1, to the same auditory and visual stimuli was larger for high SS compared to low SS (Buchsbaum & Stevens, 1971; Mullins & Lukas, 1987; Von Knorring & Perris, 1981) and this difference increased as the intensity of the stimuli became larger (Zuckerman, Murtaugh, & Siegel, 1974; Zuckerman, Simons, & Como, 1988). Based on these results, some researchers posited that high SS seemed to be augmenters of cortical evoked potentials and low SS tended to be reducers (Brabander, Boone, Gerits, & Witteloostuijn, 1995; Lukas, 1987; Zuckerman, 1984, 1990) according to the “augmenting-reducing” theory of Buchsbaum and Silverman (1968). The “augmenting-reducing” theory divided subjects into “augmenters” and “reducers” according to their sensitivity of evoked potentials to stimulus intensity. The evoked potentials of the former became larger as the stimulus intensity increased whereas those of the latter became smaller (Buchsbaum, 1976). A later study by Brocke, Beauducel, and Tasche (1999) used three experimental paradigms: the continuous performance task (CPT), delayed reaction time task (DRTT), and the augmenting-reducing paradigm, to connect SS trait to behavior and physiology measures. They found a positive correlation between SS (Thrill and Adventure Seeking, TAS subdivision) and the N1/P2 slope and a positive relationship between false alarms on the DRTT and Sensation Seeking Scalp (Form V) (SSS-V) total score, which also strongly supported the explanation of SS according to augmentingreducing theory.

In addition, studies on the neurochemical bases of SS also supported the idea that SS trait has physical bases. Previous research has identified a relatively strong relationship between polymorphisms at dopamine D4 receptor loci and individual differences in self-reported novelty-seeking personality (Munafò, Yalcin, Willis-Owen, & Flint, 2008).

Evidence from genetic and PET (Positron Emission Computed Tomography) radioligand displacement studies suggests that individuals higher in SS personality may exhibit both higher endogenous dopamine (DA) level and greater dopaminergic responses to cues of upcoming reward in striatal regions (Derringer et al., 2010; Gjedde et al., 2010; O’Sullivan et al., 2011; Riccardi et al., 2006; Zuckerman, 1985). Higher sensation-seekers have been reported to show lower platelet levels and carry lower activity isoforms of monoamine oxidase (MAO), an enzyme responsible for the breakdown of DA (Carrasco, Sáiz-Ruiz, Díaz-Marsá, César, & López-Ibor, 1999; Verdejo-García et al., 2013; Zuckerman, 1985). Recently, Norbury, Kurth-Nelson, Journal of Psychophysiology (2017), 31(1), 29–37

S. He et al., Pre-Attention of Sensation Seeking Trait

Winston, Roiser, and Husain (2015) found greater effects of a silent D2 receptor antagonist haloperidol in behaviorally defined higher sensation-seekers, suggesting a greater effect of disrupting signaling by endogenous ligand in these individuals.

In sum, most ERP (event-related potential) studies investigating the physical mechanism of SS have focused on N1 in the auditory channel, which reflected the arousal magnitude of voluntary attention. Although the N1 response indexes the sensitivity to stimulus onset, usually growing in amplitude with increased stimulus intensity (for a review, see Näätänen & Picton 1987), it cannot reflect the sensitivity of SS trait to automatic detection of stimulus change, which can be achieved by another ERP component: mismatch negativity (MMN). MMN reflects sensitivity to automatic detection of change in any repetitive aspect of auditory stimulation (Näätänen, Gaillard, & Mäntysalo, 1978; for a review, see Näätänen, Paavilainen, Rinne, & Alho, 2007) and is usually considered as a marker for pre-attentive change detection (Grimm, Roeber, TrujilloBarreto, & Schröger, 2006; Näätänen & Michie, 1979; Näätänen, Pakarinen, Rinne, & Takegata, 2004). In addition, studies on the neural chemical mechanism of MMN have found that MMN was related to MAO and NMDA (N-methyl-D-aspartate) receptors (Harms, 2015; Smith, Fisher, Blier, Illivitsky, & Knott, 2015), which were also related to SS as reviewed before. This fact suggested that there should be potential relationship between SS and MMN from the perspective of neural chemical mechanism.

Therefore, the present study aims at determining preattentive processing in audition, by using the traditional oddball paradigm, in high- and low-sensation seekers. We assumed that the MMN of high-sensation seekers is larger in amplitude than that of low-sensation seekers.

Methods Participants A total of 245 undergraduates (71 males and 174 females) were tested with a sensation-seeking questionnaire list (SS-IV Chinese Version; Zhang & Chen, 1990). On the basis of the SS-IV scores, 20 participants (14 females, 19.4 years old on average) were randomly selected as the highsensation seeking (HSS) group from the upper limit 27% of the total group and 20 (13 females, 19.6 years old on average) as the low sensation seeking (LSS) group from the lower limit 27% of the total group. The mean scores were 25.46 ± 6.79 and 8.26 ± 2.19 for the HSS and LSS groups, respectively (t = 6.27; p < .01). All participants were right handed, presented normal hearing, and had no history Ó 2016 Hogrefe Publishing

S. He et al., Pre-Attention of Sensation Seeking Trait

of psychiatric or neurological disorders. The participants signed a consent form and were paid for participation. This study was approved by the Institutional Ethical Committee of the Department of Psychology of the Central China Normal University.

Stimuli and Experimental Program A 1,000 Hz, 70 dB, frequent (75%) tone was used as the standard stimulus. The infrequent stimuli included six deviant tones with the same pitch (1,000 Hz) but different intensities (49, 56, 63, 77, 84, and 91 dB; p 4.2% for each deviant). We used Neuroscan Company’s professional sound production instrument (Stim Audio System P/N 1105) to produce and check sound stimuli, and used its supporting software Stim2 and its hardware to present the stimuli. The deviant intensities relative to that of the standard stimulus were 30%, 20%, 10%, +10%, +20%, and +30%, respectively. All stimuli were binaurally presented for 50 ms. A total of 2,700 standard stimuli and 900 deviants (150 for each deviant tone) were presented. The SOA was jittered randomly from 400 ms to 500 ms. After the first 15 standard stimuli were presented, standard and deviant stimuli were delivered in a pseudorandom order to ensure that at least two standard stimuli were presented between each pair of deviant stimuli.

During the experiments, the subjects were asked to watch a self-selected silent film (with no subtitles) and ignore the sound from the headphones.

ERP Recording The electroencephalogram (EEG) was continuously recorded (band pass = 0.05 Hz to 100 Hz (0.05–30 Hz filtered in offline analysis); sampling rate = 1,000 Hz) on a NeuroScan Synamp2 amplifier by using an electrode cap with 64 Ag/AgCl electrodes mounted in accordance with the extended international 10–20 system and referenced to the tip of nose. Vertical and horizontal electrooculograms (EOG) were recorded with two pairs of electrodes, one was placed above and below the right eye, and the other was placed 10 mm from the lateral canthi. Electrode impedance was maintained below 5 kΩ throughout the experiment.

The EEG was segmented into 500 ms epochs with the 100 ms pre-stimulus epoch serving for baseline correction.

The EOG artifacts were corrected using the method proposed by Semlitsch et al. (1986). Epochs including an EEG or EOG change exceeding ±75 uV and the EEG to the first 15 standard stimuli were omitted from averaging.

Ó 2016 Hogrefe Publishing

31

Data Processing and Statistical Analysis One subject was excluded from the analysis because of very many artifacts; thus, 39 participants were analyzed (20 HSS, 19 LSS). The MMN components were calculated by subtracting the ERPs elicited by standard stimuli from those elicited by deviant stimuli. Figure 2 shows the six MMNs elicited for each deviant stimulus, considering the distinct pattern between MMNs to the intensity-decrement (49, 56, and 63 dB) and intensity-increment deviants (77, 84, and 91 dB). The mean number of artifact-free trials of each condition for HSS and LSS group was as follow: HSS group (from 49 dB to 91 dB): 127, 129, 131, 130, 128, and 128; LSS group (from 49 dB to 91 dB): 127, 134, 136, 135, 134, and 131. Based on previous studies and visual inspection, the mean amplitude of the MMN elicited by the intensitydecrement deviants was measured within the time range of 130 ms to 230 ms post-stimulus onset, whereas the mean amplitude of the MMN elicited by the intensity-increment deviants was calculated between 50 and 150 ms poststimulus onset. Therefore, statistical analysis was separately conducted for intensity-increment and intensity-decrement deviants. In the frontocentral area, these measurements were examined by mixed-model ANOVA, with the groups (HSS and LSS) as the between-subject factors and the deviance magnitude (10%, 20%, and 30%), hemisphere (left, middle, and right), and site (AF3/F3/FC3/C3, AFz/ Fz/FCz/Cz, and AF4/F4/FC4/C4) as within-subject factors. For the temporal area, four-way ANOVA of Group (HSS, LSS) Deviant Intensity (10%, 20%, and 30%) Hemisphere (left, right) was conducted. The degrees of freedom for the within-subject factors were corrected for non-sphericity by using Greenhouse-Geisser adjustment.

Results The MMNs elicited by the six deviant stimuli are shown in Figure 2. As shown in Figure 2, each deviant stimulus elicited MMN. The MMN exhibited two characteristics (Figures 2 and 3). First, the amplitude of MMN increased and its peak latency decreased with intensity increment in the deviant stimulus. However, the statistical analysis on peak latency revealed no significant difference among different deviant levels; therefore, latency analysis was not reported in the Results part. Second, the MMN over the right hemisphere was larger than that over the left. Group differences were observed for MMNs elicited by intensitydecrement deviants but not for those elicited by intensityincrement deviants.

Journal of Psychophysiology (2017), 31(1), 29–37

32

Figure 1. Grand-average ERPs to standard stimuli at FCZ electrode.

Standard Stimuli In order to assess whether group differences might also be present in the general response to sounds, we compared ERPs to the standard stimuli between HSS and LSS group.

Mixed-model ANOVA, with the groups (HSS and LSS) as the between-subject factors and deviance magnitude (10%, 20%, and 30%), hemisphere (left, middle, and right), and site (AF3/F3/FC3/C3, AFz/Fz/FCz/Cz, and AF4/F4/ FC4/C4) as within-subject factors, was conducted for mean amplitudes every 0–300 ms after stimulus onset. As shown in Figure 1, none of the main effects or interaction effects involving group were significant (all p > .1), which suggested that HSS and LSS did not respond differently to sound in general.

Intensity-Decrement Deviants As shown in Figure 2, the main effect of the groups (HSS and LSS) was significant, F(1, 37) = 4.67; p < .05,

S. He et al., Pre-Attention of Sensation Seeking Trait

η2 = 0.11, in the frontocentral area, which showed that the mean MMN of the HSS group ( 0.75 ± 0.12 μV) was larger in amplitude than that of the LSS group ( 0.37 ± 0.12 μV). A significant main effect, F(2, 74) = 6.81; ε = 0.95, p < .01, η2 = 0.16 of the deviant stimulus ( 7, 14, and 21 dB) was also observed. The MMN amplitudes were 0.27 ± 0.13, 0.43 ± 0.13, and 0.98 ± 0.16 μV for 7, 14, and 21 dB, respectively, indicating that the MMN amplitude increased with the increment of deviant-stimulus difference. As shown in Figure 3B, the main effect of the hemisphere was also significant, F(2, 74) = 10.36; ε = 0.63, p < .01, η2 = 0.22, but qualified by the interaction of group with hemisphere, F(2, 74) = 8.81; ε = 0.63, p < .05, η2 = 0.13. However, this interaction was not significant, F(2, 74) = 2.15; ε = 0.67, p > .05, η2 = 0.04 when the amplitudes were normalized by the method of McCarthy and Wood (1985) to ensure that the interaction is not just due to a larger MMN in the HSS group. As shown in Figure 3A, a significant main effect of the site was also observed, F(3, 111) = 3.83; ε = 0.48, p < .05, η2 = 0.09, indicating the largest amplitudes at the anterior-frontal electrode sites ( 0.63 ± 0.097 μV).

No other effect reached significance (Fs < 1).

Similar to the pattern over the frontocentral electrodes, as shown in Figures 3A and 3B, all main effects at the temporal sites except for the group effect reached significance, F(2, 74) = 12.06; ε = 0.99, p < .01, η2 = 0.41, F(1, 37) = 9.61; p < .01, η2 = 0.21; significance of Deviant Intensity and Hemisphere, respectively. No significant interactions were found.

Figure 2. Grand-average MMNs elicited by six types of deviant stimuli (49, 56, 63, 77, 84, and 91 dB) at frontocentral area (FCZ).

Journal of Psychophysiology (2017), 31(1), 29–37

Ó 2016 Hogrefe Publishing

S. He et al., Pre-Attention of Sensation Seeking Trait

33

(A)

(B) -2µv

intensity-decrement

400ms

FCZ

M1

M2 LSS HSS

intensity-increment

FCZ

M1

M2

Figure 3. Grand-average MMNs across intensity-increment and intensity-decrement deviants at frontocentral (FCZ) and temporal (M1 and M2) areas. Electrical waves and 2D scalp topography are shown in the left (A) and right (B), respectively.

Intensity-Increment Deviants

Discussion

Similar to the analysis of intensity-decrement deviants, as shown in Figure 2, a significant main effect of the deviant-stimulus difference, F(2, 74) = 56.06, ε = 0.84, p < .01, η2 = 0.6, was observed in the frontocentral area, indicating that the MMN amplitudes were enhanced ( 1.12 ± 0.13, 2.27 ± 0.176, and 4.26 ± 0.34 μV for 77, 84, and 91 dB, respectively) by the increment of deviant intensity. As shown in Figure 2B, the main effect of hemisphere also was significant, F(2, 74) = 14.91, ε = 0.71, p < .01, η2 = 0.23, but qualified by the interaction of the group with the hemisphere, F(2, 74) = 8.62; ε = 0.71, p < .05, η2 = 0.13. However, this interaction was not significant, F(2, 74) = 3.29; ε = 0.69, p > .05, η2 = 0.08, when the amplitudes were normalized by the method of McCarthy and Wood (1985). A significant main effect of site, F(3, 111) = 21.91; ε = 0.54, p < .01, η2 = 0.31, was also observed, indicating the largest amplitude at the anteriorfrontal electrode sites ( 2.51 ± 0.17 μV). No other effect reached significance (Fs < 1).

Similar to the pattern over the frontocentral electrodes, as shown in Figures 3A and 3B, all main effects at the temporal sites except for the group effect were significant, F(2, 74) = 6.30; ε = 0.98, p < .01, η2 = 0.13, F(1, 37) = 13.01; p < .01, η2 = 0.29; main effects of Deviant Intensity and Hemisphere, respectively. No significant interactions were found.

This study generated six kinds of deviant stimuli by changing the intensity of standard stimulation. The characteristics of the MMN in the present study are consistent with previous results (Kujala, Kallio, Tervaniemi, & Näätänen, 2001; Pakarinen, Takegata, Rinne, Huotilainen, & Näätänen, 2007; Titova & Näätänen, 2001). Deviating from most of the previous studies, the present study investigated the MMN in both directions of change by employing intensity-decrement and -increment deviants. The results revealed that the amplitude of MMN generated by the intensity decrements in high-sensation seekers is significantly larger than that of low-sensation seekers. However, with regard to deviant-increment stimulation, no distinctive difference was found, which might be due to a ceiling effect on the MMN amplitude and overlap by the afferent N1 component (cf. Rinne, Särkkä, Degerman, Schröger, & Alho, 2006).

We assume that the present findings are due to the preattentive change-detection ability of high-sensation seekers being more sensitive than that of low-sensation seekers. In conclusion, this study indicated that high-sensation seekers demonstrated a more sensitive pre-attentive changedetection response than low-sensation seekers. This result may be associated with the fact that high-sensation seekers are inclined to prefer continuously changing environments.

Some studies on the pre-attention processing of personality

Ó 2016 Hogrefe Publishing

Journal of Psychophysiology (2017), 31(1), 29–37

34

traits relating to SS have indirectly supported the current results. For example, Sasaki, Campbell, Gordon Bazana, and Stelmack (2000) discovered that the amplitude of MMN elicited by changes in frequency in extrovert subjects was larger than that in introvert ones. Franken, Nijs, and Strien (2005) revealed that the scores of self-report impulsivity and the amplitude of MMN elicited by changes in frequency were positively correlated. Hansenne et al. (2003) disclosed that a harm avoidance personality correlated negatively with the amplitude of MMN elicited by changes in duration. Bar-Haim, Marshall, Fox, Schorr, and GordonSalant (2003) showed that the amplitude of MMN elicited by changes in frequency was smaller in social-withdrawal children than in control subjects. Given that SS traits positively correlate with impulsivity and extroversion but negatively correlate with introversion, harm avoidance, and social withdrawal (Montag & Birenbaum, 1986), our results may reveal that MMN increases in participants with traits positively correlated with SS but decreases in participants with traits negatively correlated with SS. Therefore, high-sensation seekers demonstrate greater ability to automatically detect the auditory intensity change than low-sensation seekers.

The model-adjustment hypothesis provides a possible explanation for these results with respect to the neural mechanism (Garrido, Kilner, Stephan, & Friston, 2009; Winkler, Karmos, & Näätänen, 1996). According to this hypothesis, MMN reflects the updating and adjustment of the memory trace when the deviant stimuli appear. The adjusted memory trace would treat the deviant as stimuli that might appear in future, therefore the MMN would become smaller or even vanish if the deviants continually appear in future. High-SS might be more inclined to be refractory to the stimuli appeared with larger probability (standard stimuli) and be more sensitive to changed stimulus (deviant stimulus), which leads to larger MMN for high SS compared to low SS. The evolution hypothesis proposed by Zuckerman (1994) provided another possible explanation for why the ability to automatic detection of stimulus change was greater for high SS compared to low SS from the perspective of evolution and adaption. According to this hypothesis, when individuals faced sudden changes in their environment in ancient days, they probably demonstrated two types of reactions as follows: some individuals may take changes as signals of new partners, food, and so on. Other individuals may have taken changes as signals of danger, such as enemy, harm, and so on. The former took an approach strategy, and the latter took a withdrawal strategy. Whichever strategy was adopted, the changing environment can be either beneficial (e.g., new partners and food) or detrimental (e.g., unexpected enemies and danger of death). The one who benefited from environmental change will tend to Journal of Psychophysiology (2017), 31(1), 29–37

S. He et al., Pre-Attention of Sensation Seeking Trait

utilize approach strategy, and the other who failed will tend to adopt withdrawal activities. High SS individuals adopted an approach strategy to changes while low SS individuals adopted a withdrawal strategy to changes in their adaption to changes in the environment (Zuckerman, 1990). In the neural level, the neural activities of high SS might be stronger when changes were detected than low SS and lead to larger MMN, which reflects automatic detection of changes in the environment, for high SS groups. But this view is still a hypothesis and needs more support from experimental studies.

However, Wang, Shete, Spitz, and Swann (2001) found that MMN elicited by tone intensity deviance for healthy subjects was negatively correlated with Experience seeking (ES, a subdivision of SS), which was contrary to the results of our study. We assumed that this departure was caused by differences in experimental procedures. First, their study used linked-mastoid as reference whereas we used the tip of nose. As seen in Figure 3A, the amplitude of MMN for LSS in the polarity inversion at temporal sites tended to be more positive than HSS, although this difference was not significant. Thus, the use of linked-mastoid as reference might reduce the difference of frontal MMN between HSS and LSS group when the tip of nose was used as reference, which might cause the departure between their study and the present study. Second, they used correction analysis to explore the relationship between SS and MMN whereas we compared MMN between high SS and low SS groups. In addition, this relationship was not replicated in patients with chronic primary insomnia, which casts doubts on the repeatability of this result. Third, they found correction between MMN and ES but not total score of SS questionnaire, yet we used the total score to split participants into high SS and low SS groups. However, this departure indicated that further studies are still needed to investigate the relationship between SS and MMN, for example to investigate the relationship between MMN and different subdivisions of SS. For the deviant-increment stimulus, the amplitude of MMN showed no difference between high- and low-SS subjects. This study showed that in terms of pre-attention processing of intensity change, the MMN elicited by a deviant-decrement stimulus performed differently from that induced by a deviant-increment stimulus (Rinne et al., 2006). Rinne et al. (2006) suggested that with Oddball paradigm, the MMN reflected from pre-attention processing actually presented two subcomponents, namely, pure MMN and N1. Between the two, N1 serves to be sensitive to the exogenous physical characteristic of the stimulus, whereas pure MMN is mainly related to the stimulus change based on sensory memory. N1 appears at 100 ms or higher, and pure MMN appears at 150 ms or higher (Näätänen et al., 2007). Consistent with previous studies, Ó 2016 Hogrefe Publishing

S. He et al., Pre-Attention of Sensation Seeking Trait

we also used the classic Oddball paradigm in our experiment. The MMN induced by the deviant-increment stimulus appears during 50–160 ms, and the wave peak is at 120 ms, but the MMN induced by the deviant-decrement stimulus appears at 80–230 ms, in which the wave peak is at 180 ms. Although both conditions can elicit N1 and pure MMN components, the MMN induced by the deviantincrement stimulus is mainly N1 but attributed to the deviant-decrement stimulus, MMN is mainly pure MMN. Sussman (2007) showed that elicitation of MMN involves two distinct but interrelated processes: standard formation and deviance detection. The latter is fully dependent upon the former. While high SS subjects produce higher amplitude responses, we cannot be sure whether this stems from creating a stronger representation of the standard or a larger error signal when detecting a deviant signal. Further experiment should be conducted using the procedure proposed

by Ruhnau, Herrmann, and Schröger (2012), in which a regular cascadic sequence is used as a control to the deviant, to exclude the effects of the physical attributes of the stimulus (N1) and elicit pure sensory memory based MMN. Additionally, considering the specificity of auditory information processing, the results may be accurate only in the auditory channel. Some researchers (De Pascalis, Valerio, Santoro, & Cacace, 2007) have focused on the autonomic responses to somatosensory stimuli and found that high Impulsive-Sensation Seeking (Imp-SS) participants had a lower pre-stimulus skin conductance level (SCL) and smaller skin conductance responses (SCRs) to deviant stimuli compared to low Imp-SS participants. Additionally, their heart rate (HR) acceleration was smaller in anticipation of the first and the deviant tones whereas their decelerator response was larger relative to the HR changes observed for the low Imp-SS participants. However, there was no study on visual and somatosensory modality using ERP approach. Thus, further research is needed to use ERP to determine whether the same change exists in the visual and somatosensory modality, verifying whether this characteristic of pre-attention information processing of the sensation-seeking trait is universal. Another limitation of the present study is that we only investigated the MMN elicited by changes in intensity feature. It is not clear whether the results of this study can be extended to MMN elicited by changes in other sound features, for example, duration.

Further studies should be done to investigate the relationship between SS and MMN elicited by other sound features.

Conclusion This study indicated that based on pre-attention reflected by the MMN, high-sensation seekers demonstrate a more sensitive change-detection processing of auditory stimulus Ó 2016 Hogrefe Publishing

35

than low-sensation seekers. This result may be attributed to the fact that high-sensation seekers are inclined to pursue an ever-changing behavior.

Acknowledgments This study was supported by National Natural Science Foundation of China (31571139) and Open fund of Sports Psychology Research Center of Wuhan Sports University.

Ethics and Disclosure Statements All participants of the study provided written informed consent and the study was approved by the Ethics Committee of the Human Research Ethics Committee of Central China Normal University.

The authors disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

References Bar-Haim, Y., Marshall, P. J., Fox, N. A., Schorr, E. A., & GordonSalant, S. (2003). Mismatch negativity in socially withdrawn children. Biological Psychiatry, 54, 17–24.

Brabander, B. D., Boone, C., Gerits, P., & Witteloostuijn, A. V. (1995).

Relationship between arousal and activation, and sensation seeking. Personality and Individual Differences, 18, 373–384.

Brocke, B., Beauducel, A., & Tasche, K. G. (1999). Biopsychological bases and behavioral correlates of sensation seeking: Contributions to a multilevel validation. Personality and Individual Differences, 26, 1103–1123.

Buchsbaum, M. (1976). Self-Regulation of stimulus intensity: Augmenting/reducing and the average evoked response. In G. E. Schwartz & D. Shapiro (Eds.), Consciousness and self-regulation (pp. 101–135). New York: Plenum Press.

Buchsbaum, M., & Silverman, J. (1968). Stimulus intensity control and the cortical evoked response. Psychosomatic Medicine, 30, 12–22.

Buchsbaum, M., & Stevens, S. S. (1971). Neural events and the psychophysical law. Science, 172, 502.

Carrasco, J. L., Sáiz-Ruiz, J., Díaz-Marsá, M., César, J., & LópezIbor, J. J. (1999). Low platelet monoamine oxidase activity in sensation-seeking bullfighters. CNS Spectrums, 4, 21–24.

De Pascalis, V., Valerio, E., Santoro, M., & Cacace, I. (2007).

Neuroticism-anxiety, impulsive-sensation seeking and autonomic responses to somatosensory stimuli. International Journal of Psychophysiology, 63, 16–24.

Derringer, J., Krueger, R. F., Dick, D. M., Saccone, S., Grucza, R. A., Agrawal, A., . . . Bierut, L. J. (2010). Predicting sensation seeking from dopamine genes. A candidate-system approach. Psychological Science, 21, 1282–1290.

Eysenck, H. J. (1983). A biometrical-genetical analysis of impulsive and sensation seeking behavior. In M. Zuckerman (Ed.), Biological bases of sensation seeking, impulsivity, and anxiety (pp. 1–27). Hillside, NJ: Erlbaum.

Franken, I. H. A., Nijs, I., & Strien, J. W. V. (2005). Impulsivity affects mismatch negativity (MMN) measures of preattentive auditory processing. Biological Psychology, 70, 161–167.

Journal of Psychophysiology (2017), 31(1), 29–37

36

Fulker, D. W., Eysenck, S. B. G., & Zuckerman, M. (1980). A genetic and environmental analysis of sensation seeking. Journal of Research in Personality, 14, 261–282.

Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009).

The mismatch negativity: A review of underlying mechanisms.

Clinical Neurophysiology, 120, 453–463.

Gjedde, A., Kumakura, Y., Cumming, P., Linnet, J., Møller, A., & Bloom, F. (2010). Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking. Proceedings of National Academy of Sciences of the United States of America, 107, 3870–3875.

Grimm, S., Roeber, U., Trujillo-Barreto, N. J., & Schröger, E. (2006).

Mechanisms for detecting auditory temporal and spectral deviations operate over similar time windows but are divided differently between the two hemispheres. NeuroImage, 32, 275–282.

Hansenne, M., Pinto, E., Scantamburlo, G., Renard, B., Reggers, J., Fuchs, S., . . . Ansseau, M. (2003). Harm avoidance is related to mismatch negativity (MMN) amplitude in healthy subjects.

Personality and Individual Differences, 34, 1039–1048.

Harms, L. (2016). Mismatch responses and deviance detection in N-methyl-D-aspartate (NMDA) receptor hypofunction and developmental models of schizophrenia. Biological Psychology, 116, 75–81.

Hur, Y. M., & Bouchard, T. J. (1997). The genetic correlation between impulsivity and sensation seeking traits. Behavior Genetics, 27, 455–463.

Kaynak, O., Meyers, K., Caldeira, K. M., Vincent, K. B., Winters, K. C., & Arria, A. M. (2013). Relationships among parental monitoring and sensation seeking on the development of substance use disorder among college students. Addictive Behaviors, 38, 1457–1463.

Koopmans, J. R., Boomsma, D. I., Heath, A. C., & van Doornen, L. J. (1995). A multivariate genetic analysis of sensation seeking. Behavior Genetics, 25, 349–356.

Kujala, T., Kallio, J., Tervaniemi, M., & Näätänen, R. (2001). The mismatch negativity as an index of temporal processing in audition. Clinical Neurophysiology, 112, 1712–1719.

LaBrie, J. W., Kenney, S. R., Napper, L. E., & Miller, K. (2014).

Impulsivity and alcohol-related risk among college students: Examining urgency, sensation seeking and the moderating influence of beliefs about alcohol’s role in the college experience. Addictive Behaviors, 39, 159–164.

Lukas, J. H. (1987). Visual evoked potential augmenting-reducing and personality: The vertex augmenter is a sensation seeker.

Personality and Individual Differences, 8, 385–395.

Mann, F. D., Kretsch, N., Tackett, J. L., Harden, K. P., & TuckerDrob, E. M. (2015). Person environment interactions on adolescent delinquency: Sensation seeking, peer deviance and parental monitoring. Personality and Individual Differences, 76, 129–134.

McCarthy, G., & Wood, C. C. (1985). Scalp distribution of eventrelated potentials: An ambiguity associated with analysis of variance models. Electroencephalography and Clinical Neurophysiology, 62, 203–208.

Montag, I., & Birenbaum, M. (1986). Psychopathological factors and sensation seeking. Journal of Research in Personality, 20, 338–348.

Mullins, L. F., & Lukas, J. H. (1987). Brain potentials and personality: A new look at stress susceptibility. (Technical Memorandum 20–87). Aberdeen Proving Ground, MD: US Army Human Engineering Laboratory. Retrieved from http:// www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc. pdf&AD=ADA186931 Munafò, M. R., Yalcin, B., Willis-Owen, S. A., & Flint, J. (2008).

Association of the dopamine D4 receptor (DRD4) gene and

Journal of Psychophysiology (2017), 31(1), 29–37

S. He et al., Pre-Attention of Sensation Seeking Trait

approach-related personality traits: Meta-analysis and new data. Biol Psychiatry, 63, 197–206.

Näätänen, R., Gaillard, A. W., & Mäntysalo, S. (1978). Early selective-attention effects on evoked potential reinterpreted.

Acta Psychologica, 42, 313–329.

Näätänen, R., & Michie, P. T. (1979). Early selective-attention effects on the evoked potential: A critical review and reinterpretation. Biological Psychology, 8, 81–136.

Näätänen, R., Paavilainen, P., Rinne, T., & Alho, K. (2007). The mismatch negativity (MMN) in basic research of central auditory processing: A review. Clinical Neurophysiology, 118, 2544–2590.

Näätänen, R., Pakarinen, S., Rinne, T., & Takegata, R. (2004). The mismatch negativity (MMN): Towards the optimal paradigm.

Clinical Neurophysiology, 115, 140–144.

Näätänen, R., & Picton, T. (1987). The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure. Psychophysiology, 24, 375–425.

Norbury, A., Kurth-Nelson, Z., Winston, J. S., Roiser, J. P., & Husain, M. (2015). Dopamine regulates approach-avoidance in human sensation-seeking. International Journal of Neuropsychopharmacology, 18, 1–10.

O’Sullivan, S. S., Wu, K., Politis, M., Lawrence, A. D., Evans, A. H., Bose, S. K., . . . Piccini, P. (2011). Cue-induced striatal dopamine release in Parkinson’s disease-associated impulsivecompulsive behaviours. Brain: A Journal of Neurology, 134, 969–978.

Pakarinen, S., Takegata, R., Rinne, T., Huotilainen, M., & Näätänen, R. (2007). Measurement of extensive auditory discrimination profiles using the mismatch negativity (MMN) of the auditory eventrelated potential (ERP). Clinical Neurophysiology, 118, 177–185.

Peach, H. D., & Gaultney, J. F. (2013). Sleep, impulse control, and sensation seeking predict delinquent behavior in adolescents, emerging adults, and adults. Journal of Adolescent Health, 53, 293–299.

Rahmani, S., & Lavasani, M. G. (2011). The relationship between Internet dependency with sensation seeking and personality.

Procedia-Social and Behavioral Sciences, 30, 272–277.

Riccardi, P., Zald, D., Li, R., Park, S., Ansari, M. S., Dawant, B., . . .

Schmidt, D. (2006). Sex differences in amphetamine-induced displacement of [18F] fallypride in striatal and extrastriatal regions: A PET study. The American Journal of Psychiatry, 163, 1639–1641.

Rinne, T., Särkkä, A., Degerman, A., Schröger, E., & Alho, K. (2006).

Two separate mechanisms underlie auditory change detection and involuntary control of attention. Brain Research, 1077, 135–143.

Ruhnau, P., Herrmann, B., & Schröger, E. (2012). Finding the right control: The mismatch negativity under investigation. Clinical Neurophysiology, 123, 507–512.

Sasaki, T., Campbell, K. B., Gordon Bazana, P., & Stelmack, R. M. (2000). Individual differences in mismatch negativity measures of involuntary attention shift. Clinical Neurophysiology, 111, 1553–1560.

Semlitsch, H. V., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23, 695–703.

Smith, D. M., Fisher, D., Blier, P., Illivitsky, V., & Knott, V. (2015).

The separate and combined effects of monoamine oxidase A inhibition and nicotine on the mismatch negativity event related potential. Pharmacology, Biochemistry and Behavior, 137, 44–52.

Sussman, E. S. (2007). A new view on the MMN and attention debate: The role of context in processing auditory events.

Journal of Psychophysiology, 21, 164–175.

Ó 2016 Hogrefe Publishing

S. He et al., Pre-Attention of Sensation Seeking Trait

Titova, N., & Näätänen, R. (2001). Preattentive voice discrimination by the human brain as indexed by the mismatch negativity.

Neuroscience Letters, 308, 63–65.

Verdejo-García, A., Albein-Urios, N., Molina, E., Ching-López, A., Martínez-González, J. M., & Gutiérrez, B. (2013). A MAOA gene cocaine severity interaction on impulsivity and neuropsychological measures of orbitofrontal dysfunction: Preliminary results. Drug and Alcohol Dependence, 133, 287–290.

Von Knorring, L., & Perris, C. (1981). Biochemistry of the augmenting-reducing response in visual evoked potentials.

Neuropsychobiology, 7, 1–8.

Wang, W., Zhu, S. Z., Pan, L. C., Hu, A. H., & Wang, Y. H. (2001).

Mismatch negativity and personality traits in chronic primary insomniacs. Functional Neurology, 16, 3–10.

Wilkinson, A. V., Shete, S., Spitz, M. R., & Swann, A. C. (2011).

Sensation seeking, risk behaviors, and alcohol consumption among Mexican origin youth. Journal of Adolescent Health, 48, 65–72.

Winkler, I., Karmos, G., & Näätänen, R. (1996). Adaptive modeling of the unattended acoustic environment reflected in the mismatch negativity event-related potential. Brain Research, 742, 239–252.

Zhang, Y. C., & Chen, Z. G. (1990). An experimental study on the sensation-seeking trait of special teenagers. Acta Psychologica Sinica, 22, 368–376.

Zuckerman, M. (1971). Physiological measures of sexual arousal in the human. Psychological Bulletin, 75, 297–329.

Zuckerman, M. (1984). Sensation seeking: A comparative approach to a human trait. The Behavioral and Brain Sciences, 7, 413–471.

Zuckerman, M. (1985). Sensation seeking, mania, and monoamines. Neuropsychobiology, 13, 121–128.

Ó 2016 Hogrefe Publishing

37

Zuckerman, M. (1990). The psychophysiology of sensation seeking. Journal of Personality, 58, 313–345.

Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New York, NY: Cambridge University Press.

Zuckerman, M., Murtaugh, T., & Siegel, J. (1974). Sensation seeking and cortical augmenting-reducing. Psychophysiology, 11, 535–542.

Zuckerman, M., Simons, R. F., & Como, P. G. (1988). Sensation seeking and stimulus intensity as modulators of cortical, cardiovascular and electrodermal response: A cross-modality study. Personality and Individual Differences, 9, 361–372.

Received May 27, 2015 Accepted November 4, 2015 Published online July 27, 2016

Jinbo He Key Laboratory of Adolescent Cyberpsychology and Behavior of Ministry of Education School of Psychology Central China Normal University No. 152, Luoyu Road Wuhan, Hubei Province 430079 PR China Tel. +86 27 137 2020-0296 Fax +86 27 6786-8617 E-mail hjb@mail.ccnu.edu.cn

Journal of Psychophysiology (2017), 31(1), 29–37

Article

Brief Report on the Psychophysiological Effects of a Yoga Intervention for Chronic Stress Preliminary Findings Kaitlin N. Harkess,1 Paul Delfabbro,2 Jane Mortimer,2 Zara Hannaford,2 and Sarah Cohen-Woods3 1

Department of Psychology, University of Adelaide, Australia

2

School of Psychology, University of Adelaide, Australia

3

Discipline of Psychiatry, School of Medicine, University of Adelaide, Australia Abstract: This paper evaluates the results of a longitudinal investigation of the potential benefits of yoga in a nonclinical sample of chronically stressed women (N = 116). Women undertook a twice weekly, hour-long yoga class for a period of 2 months, measuring psychological and physical indicators of health periodically. Changes in both areas were compared against a wait-list control group. The reported energy expenditure between groups was estimated to be similar, which suggests that the control group engaged in physical activities other than yoga.

Of the six psychological outcomes measured, we found improvements in three. Specifically, those in the practicing yoga group experienced increases in positive affect, decreases in levels of distress and stress, as well as a decrease in waist circumference and increased flexibility.

No between-group differences were found in mindfulness, well-being, and negative affect. These findings are generally consistent with an emerging literature, suggesting that yoga may provide both psychological and physiological effects that extend beyond its more obvious physical benefits, and are discussed in terms of the body’s allostatic load. These results should be considered in light of this study’s limitations, which include its small sample size, lack of an “active” control group, and female-only participants.

Keywords: yoga, well-being, stress, affect, psychophysiological effects

Psychological stress occurs when individuals perceive that the demands placed on them exceed their ability to cope (Cohen, Janicki-Deverts, & Miller, 2007; McEwen, 2006).

Prolonged stress can contribute to anxiety and depression, as well as behavioral and physiological changes (Cohen, Kessler, & Gordon, 1995; Glaser & Kiecolt-Glaser, 2005; Kiecolt-Glaser, McGuire, Robles, & Glaser, 2002; Mazure, 1998; Monroe, Simons, & Thase, 1991). Although some reactions may be adaptive in the context of short-term stress, an accumulation of stress over time has been found to contribute to disease pathogenesis and poorer health.

Often conceptualized in terms of a body’s “Allostatic Load” (McEwen, Flier, & Underhill, 1998; McEwen & Stellar, 1993), this process refers to the cumulative “wear and tear” on the body, resulting from the body’s adaptive response to stress (McEwen, 2002; Seeman, Singer, Rowe, Horwitz, & McEwen, 1997).

Such accumulative harm arises because of the frequent activation of the autonomic nervous system (ANS), which results in elevated heart rate, respiratory rate, blood Journal of Psychophysiology (2017), 31(1), 38–48 DOI: 10.1027/0269-8803/a000169

pressure, and cardiac output. This is in addition to activation of the hypothalamic-pituitary-adrenocortical (HPA) axis, which increases cortisol levels in the body (Rice, 2012). Ongoing stress may lead to cortisol/endocrine system imbalances, which cause psychological and physiological pathology: anxiety, depression, cardiovascular phenomenon, immune system dysregulation, and metabolic syndromes such as “central obesity” (Chrousos, 2009; Cohen et al., 1995, 2007; Kiecolt-Glaser et al., 2002; McEwen & Stellar, 1993; McEwen et al., 1998; Seeman et al., 1997; Segerstrom & Miller, 2004).

Given the prevalence of stress and its ability to affect both psychological and physical health, there is an increasing interest in the value of engaging interventions that potentially address both the physical and psychological consequences of chronic stress. One such intervention is yoga (Cohen, Penman, Pirotta, & Da Costa, 2005; Penman, Cohen, Stevens, & Jackson, 2012). Yoga is a holistic approach that encompasses physical, spiritual, psychological, and social dimensions. Yoga, as practiced in the Ó 2016 Hogrefe Publishing

K. N. Harkess et al., Psychological Effects of Yoga

Western world, combines elements directed towards these different dimensions, and it is achieved through postures (asanas) that focus on strength, flexibility, and balance, coordinated with breathing (pranayama) and meditation (Amin & Goodman, 2014). These components allow for exercise to be integrated with self-awareness or mindfulness-based elements, and yoga has, therefore, been referred to as “meditation in motion” (Gard, Noggle, Park, Vago, & Wilson, 2014; Khalsa, Shorter, Cope, Wyshak, & Sklar, 2009; La Forge, 2005).

Benefits of Yoga Studies spanning a period of over a decade show that general stress reductions can occur as a result of mindfulnessmeditation based practices and physical exercise, with antidepressant and anxiolytic effects (Grossman, Niemann, Schmidt, & Walach, 2004; Marchand, 2012; Salmon, 2001). Furthermore, levels of mindfulness have been found to be higher in advanced yoga practitioners relative to beginners, and levels of percived stress are negatively associated with mindfulness (Brisbon & Lowery, 2011). Yoga has been associated with enhanced emotional well-being, resilience to stress, positive affect, and decreased negative affect in workplace trials (Hartfiel, Havenhand, Khalsa, Clarke, & Krayer, 2011; Michalsen et al., 2005; Narasimhan, Nagarathna, & Nagendra, 2011), as well as enhanced overall well-being and quality of life (Woodyard, 2011). Moreover, these effects are superior to physical activity alone (Chattha, Raghuram, Venkatram, & Hongasandra, 2008; Duraiswamy, Thirthalli, Nagendra, & Gangadhar, 2007; Oken et al., 2006). It has been suggested that a minimum practice of once weekly is needed to observe a decrease in stress and distress, with durations of 6 weeks plus demonstrating an effect (Banerjee et al., 2007; Cowen & Adams, 2005; Michalsen et al., 2005; Moadel et al., 2007; Satyapriya, Nagendra, Nagarathna, & Padmalatha, 2009; Sujatha & Judie, 2014), while a reduction in stress was not demonstrated when adherence to weekly practice was not controlled for in analysis (Smith, Hancock, Blake-Mortimer, & Eckert, 2007). Other studies support the value of yoga as a useful intervention for a wide range of conditions, from psychological and cognitive ailments (Balasubramaniam, Telles, & Doraiswamy, 2012; Cabral, Meyer, & Ames, 2011; Derry et al., 2014; Elwy et al., 2014; Field, 2011; Kuntsevich, Bushell, & Theise, 2010; Li & Goldsmith, 2012; Pilkington, Kirkwood, Rampes, & Richardson, 2005) to biological ailments such as pain syndromes (Tilbrook et al., 2011), cardiovascular conditions (Damodaran et al., 2002; Hagins, States, Selfe, & Innes, 2013), and immune conditions (Field, 2011; Kiecolt-Glaser et al., 2010, 2014, 2012). Yoga has been shown to enhance balance and flexibility (Oken et al., 2006) and decrease Ó 2016 Hogrefe Publishing

39

muscle stiffness. The benefit of this has been found to be a reduction in the likelihood of muscle injuries (Witvrouw, Danneels, Asselman, D’Have, & Cambier, 2003) or lower back pain (Tilbrook et al., 2011), a condition which has been shown to be the most significant contributor to Years Lived with Disability (Vos et al., 2012). Yoga can be used as an additional lower-impact treatment in situations where currently medication is the most common treatment (Duquesnoy, Allaert, & Verdoncq, 1998).

The Present Study Most evaluation studies investigating potential benefits of yoga have been in clinical populations ranging from breast cancer to psychiatric disorders (e.g., Balasubramaniam et al., 2012; Cabral et al., 2011; Harder, Parlour, & Jenkins, 2012; Pilkington et al., 2005; Sadja & Mills, 2013; Yang, 2007). However, given that few randomized trials with substantial sample sizes have been conducted (i.e., more than 10–25 per group), less is known about the extent to which yoga might be beneficial in more normative populations affected by more common physical or psychological conditions (Patel, Newstead, & Ferrer, 2012). Accordingly, relatively little is known about the extent yoga can be used to counter the high prevalence of stress observed in the general population (Li & Goldsmith, 2012). In this paper, we report the study findings from a subpopulation known to commonly experience higher levels of chronic stress (namely, middleaged women working in largely professional occupations; Birdee et al., 2008; Diener, Suh, Lucas, & Smith, 1999; Nolen-Hoeksema, Larson, & Grayson, 1999; Penman et al., 2012). The intervention was an 8-week, moderate-intensity yoga intervention with pre- and post- measures. Our hypotheses were that yoga would decrease levels of perceived stress and psychological distress, increase mindfulness, and improve well-being (measured by an increase in subjective well-being and positive affect and a decrease in negative affect). We also hypothesized a decrease in resting heart rate, blood pressure, waist-to-height ratio (WHtR), and increased flexibility.

Method Study Design A longitudinal stratified randomized waitlist-control trial design was used for this study. Participants were randomly allocated to the intervention group or to the control group using Research Randomizer (Urbaniak & Plous, 2013).

Stratification was based on Psychological Distress Categories (Moderate, High, and Very High), as measured by the K10 (Andrews & Slade, 2001).

Journal of Psychophysiology (2017), 31(1), 38–48

40

The protocol defined completion of the yoga intervention as attendance at 8 classes, which was met by 43 women.

While participants were encouraged to attend all yoga classes, protocol completion of the intervention was defined as an average of 1 class each week (8 classes), as this frequency has been found to be sufficient to impact stress and distress positively (e.g., Cowen & Adams, 2005; Moadel et al., 2007; Sujatha & Judie, 2014), and is more reflective of what the population can fit into its training schedules (Amin & Goodman, 2014). The durations of yoga interventions demonstrating stress reductions are quite heterogeneous.

For example, common durations have included 6 weeks (e.g., Banerjee et al., 2007; Cowen & Adams, 2005), 8 weeks (Huang, Chien, & Chung, 2013; Kinser, Bourguignon, Whaley, Hauenstein, & Taylor, 2013), and 12 weeks (Michalsen et al., 2005; Moadel et al., 2007). To the best of the authors’ knowledge, there are, as yet, no standardized duration protocols for yoga interventions that have considered clinical effectiveness in conjunction with costeffectivnesss in relation to the minimum number of sessions needed for a successful outcome. In this study, an 8-week intervention was chosen because this appeared to fall within the mid-range of existing durations and appeared most likely to meet the goal of being practical and, therefore, potentially replicable. As this field becomes more established, evaluation of durational effects will likely follow, as is the case for the standardized 8-week MBCT (Mindfulness-Based Cognitive Therapy) program (Carmody & Baer, 2009; Klatt, Buckworth, & Malarkey, 2008). Posttest measures were taken at the conclusion of the 8-week intervention period.

Participants The CONSORT flow diagram (Figure 1) illustrates our recruitment and retention for this study. Our initial intention was to have a minimum of 96 participants. Eligible participants were females between the ages of 35 and 65 years, non-obese (as measured by BMI), who had been experiencing moderate to very high levels of psychological distress for at least 1 month (Andrews & Slade, 2001; Australian Bureau of Statisics [ABS], 2003; Kessler & Mroczek, 1994). Potential participants who had undertaken regular yoga practice over the previous year were excluded. The average age of participants was 48.14 years (SD = 8.22), and the average yearly income for participants was $63,752 (SD = 16,359).

K. N. Harkess et al., Psychological Effects of Yoga

psychological distress based on questions about anxiety and depression symptoms; Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983), which measures the degree to which situations in one’s life are appraised as stressful; The Mindfulness Attention Awareness Scale (MAAS; Brown & Ryan, 2003), which measures people’s tendency to be mindful of moment-to-moment experience; The Psychological Wellbeing Index – Adult (PWI-A; International Wellbeing Group, 2006), which measures subjective well-being, focusing on cognitive evaluations in different areas of life (standard of living, health, achieving in life, relationships, safety, community-connectedness, future security, and spirituality/religion); Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), which consists of two mood scales that measure people’s positive and negative affect; The International Physical Activity Questionnaire (IPAQ; Craig et al., 2003), which measures physical activity taken over the past week in a number of domains.

From the IPAQ, the energy cost of participants’ weekly physical activities is calculated as the Metabolic Equivalents of Task (MET) from the IPAQ (IPAQ Web site, 2005).

Measures collected in person at the University of Adelaide: The WHtR (Cox & Whichelow, 1996; Janssen, Katzmarzyk, & Ross, 2002; Savva, Lamnisos, & Kafatos, 2013), which is a measure of, central obesity, in particular, visceral fat and the health risks associated with it; Blood pressure and heart rate, both of which were measured by the OMRON HEM7121 Standard Upper Arm Blood Pressure Monitor; The Flex-TesterÒ “sit-and-reach” test, which is a measure of lower back and hamstring flexibility (Lopez-Minarro, Muyor, & Alacid, 2011).

To control for circadian fluctuations, we ensured consistency in the time of day that participants’ measures were collected.

Measures The following psychological outcome measures were used and collected via an online survey: Kessler Psychological Distress Scale (K10; Kessler & Mroczek, 1994), which gives a global measure of Journal of Psychophysiology (2017), 31(1), 38–48

Procedure The yoga intervention comprised a total of 16 one-hr yoga classes that took place twice a week over a period of Ó 2016 Hogrefe Publishing

K. N. Harkess et al., Psychological Effects of Yoga

41

Potential participants were recruited through advertisement in the local media (television, radio and newsletters)

Assessed f or eligibility (n = 207)

Excluded (n = 207)

Analysis

Follow-up

Allocation

Enrollment

Not meeting inclusion criteria (n = 87) Ref used to participate (n = 4) Other reasons (n = 0)

Randomized (n = 116)

Allocated to intervention (n = 60) Received allocated control (n = 46) Did not receive allocated intervention (n = 14) Lost to f ollow-up (n = 6) Discontinued intervention (n = 3) Analyzed ITT (n = 51) Analyzed PP (n = 43) Excluded f rom ITT analysis (n = 0) Excluded f rom PP analysis (n =8)

Allocated to control (n = 56) Received allocated control (n = 56) Did not receive allocated control (n = 0) Lost to f ollow-up (n = 6) Discontinued intervention (n = 1) Analyzed ITT (n = 49) Analyzed PP (n = 49) Excluded f rom ITT analysis (n = 0) Excluded f rom PP analysis (n = 0)

Figure 1. CONSORT flow diagram of the study process, based on sample size of psychological outcomes.

8 weeks. Yoga classes were conducted at a local community center by the first author, a certified yoga instructor with 7 years’ teaching experience (Yoga Australia – Level 2 Member). The classes were Ashtanga-based yoga and followed a standardized structure, commencing with a guided meditation, followed by the Sun Salutations (Surya Namaskara; a series of postures that flow together), standing postures, floor postures, and a relaxation posture to conclude the class. Ashtanga is a dynamic yoga practice, which has been found to demonstrate a significant increase in heart rate, when compared with yoga classes that are gentle and relaxation based (Cowen & Adams, 2007). The Sun Salutations, which comprised 20 min of each class, have demonstrated an energy cost of 6.7 METs (Carroll, Blansit, Otto, & Wygand, 2003), while other postures, such as those considered more suitable for the comparatively less physically fit, have been found to give rise to METs less than 2.19 (Ray, Pathak, & Tomer, 2011). One participant reported two adverse events: During attendance at the first and second class, this participant reported developing a Ó 2016 Hogrefe Publishing

headache and aches throughout her body, which she described as a “shock reaction.” In both cases, she recovered within that day and chose not to participate in further classes.

Statistical Analysis A SPSS-v.22 statistical software package was used to conduct all statistical analyses, with an alpha level of .05. In order to determine if there is a difference in outcome variables between the yoga intervention and control groups, a posttest only analysis was undertaken. Measuring the simple difference from baseline scores is widely considered to be an inappropriate method for measuring change across two time points (Cronbach & Furby, 1970; Dugard, 1995).

Consequently, to assess the effectiveness of the yoga intervention at posttest, the shared variance of participants’ baseline scores on psychological and physiological measures was partialled out using an ANCOVA, where pretest scores were included as the covariate. This method ensured Journal of Psychophysiology (2017), 31(1), 38–48

42

that the baseline measures of each group were matched, maximizing power (Van Breukelen, 2006). Two-tailed tests were used for all analyses (Moyé & Tita, 2002). Following the recommendations of Perneger (1998), the results were interpreted with reference to both statistical significance as well as effect size. We did not apply familywise alpha corrections, considering each hypothesis individually (Perneger, 1998).

In order to account for attrition bias in estimating treatment effect, an intention to treat (ITT) analysis was run on all outcome variables, in addition to per-protocol analysis, which was conducted to estimate maximum treatment efficacy (Armijo-Olivo, Warren, & Magee, 2009; Gupta, 2011; Lesaffre & Verbeke, 2005). While a number of participants did discontinue attendance at the yoga classes, they still attended the posttest (see Figure 1), so it was possible to ascertain the practical value of being able to offer yoga in this population (Lesaffre & Verbeke, 2005).

Results The primary end points were changes in psychological stress and well-being measures. The secondary end points were physical and physiological improvements in central obesity, blood pressure, heart rate, and flexibility.

We hypothesized that yoga would benefit practitioners in all primary and secondary markers.

Intent-to-Treat Analysis All participants were included in the ITT analysis based on the original randomisation, regardless of protocol adherence. We found that positive affect was greater in the yoga group (ηp2 = .05), and a marginal decrease in the perceived stress of the yoga group (ηp2 = .04, p = .053). We did not find between-group differences for perceived stress, psychological distress, mindfulness, subjective well-being, or negative affect. Descriptive statistics and ANCOVA outcomes are detailed in Table 1. In the physiological measures, WHtR was decreased in the yoga group (ηp2 = .08) and sit-and-reach scores (flexibility) were increased (ηp2 = .23). No between-group differences were found for blood pressure, pulse, or MET. For descriptive statistics and ANCOVA outcomes see Table 1.

Per-Protocol Analysis Of the yoga group, only participants who adhered to a minimum attendance of eight classes (once a week) were included in this analysis. We found that psychological Journal of Psychophysiology (2017), 31(1), 38–48

K. N. Harkess et al., Psychological Effects of Yoga

distress (ηp2 = .05) and perceived stress (ηp2 = .05) decreased in the yoga group, while positive affect increased (ηp2 = .05). No between-group differences were found for mindfulness, subjective well-being, or negative affect.

Descriptive statistics and ANCOVA outcomes are detailed in Table 2. In the physiological measures, we found that WHtR was decreased (ηp2 = .11) in the yoga group and sit-and-reach scores were increased (ηp2 = .26). No between-group differences were found for blood pressure, pulse, or MET. For descriptive statistics and ANCOVA outcomes see Table 2.

Discussion The aim of this study was to determine if an 8-week, moderate-intensity yoga intervention would benefit chronically stressed women’s levels of stress and improve their well-being. Self-reported energy expenditure in each group was similar, as estimated by the well-validated IPAQ (Craig et al., 2003), also used in studies with comparable sample sizes (Belem da Silva, Schuch, Costa, Hirakata, & Manfro, 2014; Ryan, White, Roydhouse, & Fethney, 2011). This finding indicates the control group engaged in physical activity, excluding yoga.

Our results demonstrate that participants in the yoga intervention had overall improvements in positive affect, as well as decreased levels of psychological distress and perceived stress, when practicing a minimum of once a week. The per-protocol (PP) analyses were better powered than the intent-to-treat (ITT), as only women who participated in a minimum of 1 class per week were included. This is reflected by a reduction in three psychological measures, relative to only one with the ITT analyses. However, effect sizes were generally small and should be interpreted with caution. Further, only positive affect would maintain significance if correction for multiple testing was applied. Yoga also changed body composition, decreasing abdominal obesity, and improving flexibility, although levels of mindfulness, well-being, negative affect, blood pressure, and heart rate were unaffected.

Accompanying the reduction in perceived stress reported in the PP analysis, a marginally significant trend was seen for the yoga group as a whole. Prior studies have also reported small to moderate effect sizes where statistical power was insufficient to demonstrate significance (Cohen, Chang, Grady, & Kanaya, 2008), similar to these findings.

A failure to find a reduction in stress with ITT analysis, as opposed to PP, has also been reported in an hour-long, 10 week, yoga intervention (Smith et al., 2007), indicating a minimum of weekly participation in an hour-long yoga class over a period of 8 weeks or more is necessary for a nonclinical population. Ó 2016 Hogrefe Publishing

Ó 2016 Hogrefe Publishing

24.2 (5.5)

21.4 (6.5)

26.5 (5.1)

23.41 (6.5)

3.51 (0.79)

3.67 (0.81)

ηp2 = .01 (small)

3.72 (0.80)

ηp2 = .04 (small)

3.45 (0.79)

ηp2 = .03 (small)

22.53 (7.2) F(1, 97) < 1

27.0 (5.1) F(1, 97) = 3.82, p = .053

20.4 (6.8)

F(1, 97) = 2.88, p = .093

24.3 (5.3)

53.2 (12.2)

52.6 (10.1)

Post M (SD)

53.3 (12.3)

47.9 (12.1)

52.9 (11.2)

ηp2 = .01 (small)

F(1, 74) < 1

46.0 (12.6)

ηp2 = .01 (small)

F(1, 67) < 1

46.5 (12.7)

45.0 (11.3)

Pre M (SD)

PWI-A Post M (SD)

32.6 (6.9)

29.7 (8.1)

32.67 (7.0)

31.8 (7.0)

31.2 (7.7)

ηp2 = .05 (small-medium)

F(1, 91) = 4.29, p = .041

31.8 (6.7)

ηp2 = .05 (small-medium)

F(1, 84) = 4.32, p = .041

32.0 (6.7)

31.8 (7.5)

Pre M (SD)

POS affect

Post M (SD)

12.0 (2.9)

13.3 (4.3)

12.5 (4.8)

13.8 (4.6)

12.9 (4.5)

ηp2 = .01 (small)

F(1, 91) < 1

13.6 (4.8)

ηp2 = .03 (small)

F(1, 84) = 2.90, p = .092

13.9 (5.4)

13.9 (4.3)

Pre M (SD)

NEG affect

Ò

27.0 (7.9)

126.6 (15.4)

129.0 (16.7)

82.2 (11.3)

78.7 (11.8)

Post M (SD)

81.8 (11.0)

81.3 (11.9)

80.4 (11.4)

ηp2 = .01 (small)

F(1, 95) = 1.36, p = .247

81.9 (13.1)

ηp2 = .02 (small)

F(1, 88) = 1.85, p = .177

81.7 (13.1)

80.7 (10.6)

Pre M (SD)

Diastolic BP

81.2 (11.4)

81.2 (10.2)

Post M (SD)

81.0 (10.8)

76.9 (12.1)

80.8 (10.6)

ηp2 = .00

F(1, 91) < 1

76.6 (12.9)

ηp2 = .00

F(1, 94) < 1

77.1 (13.3)

77.2 (11.3)

Pre M (SD)

Resting Pulse

1,434 (991)

1,377 (1016)

Post M (SD)

1,388 (1,016)

1,442 (1,253)

1,393 (1,411)

ηp2 < .00

F(1, 94) < 1

1,387 (1,269)

ηp2 < .00

F(1, 92) < 1

1,654 (1,973)

1,500 (1,245)

Pre M (SD)

MET

Notes. WHtR = Waist-to-height ratio; Sit-Reach = Flex-Tester “sit-and-reach” test; BP = Blood pressure; MET = Metabolic Equivalents, PP = Per-Protocol Analysis, ITT = Intent-to-treat Analysis ηp2 = partial eta squared, an = between 46 and 55, bn = between 43 and 46, cn = between 49 and 60, dN = between 96 and 115. Field (2013) suggests that small, medium, and large effect sizes correspond to: small = 0.01; medium = 0.06; and large = 0.14.

25.15 (8.7)

ηp2 < .00

.534 (.084)

ηp2 = .23 (large)

.526 (.077)

128.3 (14.7)

ηp2 = .08 (medium)

Totald

127.1 (15.5)

F(1, 95) < 1

28.3 (7.4)

F(1, 94) = 28.10, p < .001

24.2 (8.5)

F(1, 92) = 8.17, p = .005

(ITT)c

.520 (.082)

.526 (.076)

Yoga

ηp2 < .00

ηp2 = .26 (large)

ηp2 = .11 (medium)

129.0 (15.5)

129.8 (18.7)

Post M (SD)

F(1, 88) < 1

126.4 (14.9)

126.1 (9.0)

Pre M (SD)

F(1, 87) = 29.98, p < .001

29.1 (7.1)

25.6 (8.2)

Post M (SD)

F(1, 85) = 9.99, p = .002

24.8 (8.2)

26.1 (9.0)

Pre M (SD)

(PP)b

.517 (.071)

.550 (.081)

Post M (SD)

Systolic BP

.521 (.070)

.525 (.078)

Pre M (SD)

Sit-Reach

Yoga

Control

a

WHtR

Table 2. Raw Mean, SD, and ANCOVA of physiological outcome variables of total sample and groups

Notes. K10 = Kessler Psychological Distress Scale; PSS = Perceived Stress Scale; MAAS = Mindfulness Attention Awareness Scale; PWI-A = The Psychological Wellbeing Index – Adult; POS Affect = Positive; NEG Affect = Negative Affect; PP = Per-Protocol Analysis; ITT = Intent-to-treat Analysis ηp2 = partial eta squared; an = between 44 and 56, bn = between 41 and 56, cn = between 47, and 60, dN = between 91 and 116. Field (2013) suggests that small, medium, and large effect sizes correspond to: small = 0.01; medium = 0.06; and large = 0.14.

Total

d

(ITT)c

Yoga

ηp2 < .01 (small)

3.71 (0.79)

3.63 (0.84)

Post M (SD)

ηp2 = .05 (small-medium)

3.52 (0.80)

3.57 (0.75)

Pre M (SD)

ηp2 = .05 (small-medium)

22.4 (7.3)

24.3 (5.6)

Post M (SD)

F(1, 89) < 1

27.0 (5.3)

26.1 (5.1)

Pre M (SD)

MAAS

F(1, 89) = 4.16, p = .044

19.6 (6.3)

22.4 (5.9)

Post M (SD)

PSS

F(1, 89) = 4.84, p = .030

23.9 (5.2)

Yoga

(PP)b

24.1 (5.8)

Pre M (SD)

Controla

K10

Table 1. Raw Mean, SD, and ANCOVA of psychological outcome variables of total sample and groups

K. N. Harkess et al., Psychological Effects of Yoga 43

Journal of Psychophysiology (2017), 31(1), 38–48

44

Consistent with previous studies, positive affect increased in the yoga group (Danhauer et al., 2009; Narasimhan et al., 2011; Tolbanos Roche & Mas Hesse, 2014; Vadiraja et al., 2009), regardless of adherence. Positive affect is considered to be the “hallmark of well-being” (Lyubomirsky, King, & Diener, 2005), protective of illness regardless of negative affect (Cohen, Doyle, Turner, Alper, & Skoner, 2003), and diminished in depressed individuals (Folkman & Moskowitz, 2000). Our study suggests that even minimal engagement with yoga practice may yield a positive psychological state, as the increase was irrespective of level of adherence, as per previous literature (Vadiraja et al., 2009). Considering the control group also engaged in physical activity, our results indicate that yoga provides benefits extending beyond physical activity (Craft & Perna, 2004; Jagdishdua & Hargreaves, 1992; Lox, Burns, Treasure, & Wasley, 1999). While most trials have reported a decrease in negative affect alongside an increase in positive affect, this was not demonstrated in the current study, nor in a previous yoga trial of a similar duration (Danhauer et al., 2009). This indicates that decreasing the negative effects of daily stressors may require longer, or more frequent, yoga classes. However, Danhauer et al. (2009) report decreased negative affect in participants with high baseline levels ( 15); it is plausible that baseline levels of negative affect in our nonclinical population were not sufficiently high to be significantly altered in this brief intervention (M = 13.84, SD = 4.6). Alternatively, given that PP analysis demonstrated a trend of reduced negative affect, a larger cohort may have detected significant changes.

The lack of some between-group differences found was unexpected; in particular, the lack of differentiation in mindfulness and well-being (Brisbon & Lowery, 2011; Hartfiel et al., 2011). Yoga is broadly found to promote well-being (Bonura, 2011; Littman et al., 2012; Woodyard, 2011), conceptualized as an accumulation of positive affect (Fredrickson, 2001; Fredrickson, Losada, & Anderson, 2005). However, this is a diffuse construct (i.e., covers cognitive factors in addition to affective). Potentially a greater number of people are necessary to detect a small effect in the cognitive domain, or a longer study is required for a significant aggregation of positive affect. It is also feasible that the cognitive domain of subjective well-being was not affected by a yoga practice, while positive affect was.

Despite some surprising null findings, we do demonstrate that weekly attendance at a yoga class has some small benefits for normative female populations even within a short period of time. The effect is smaller than that observed in clinical populations who took part in lengthier interventions (e.g., 12 weeks: Moadel et al., 2007; Valoriani et al., 2014).

Journal of Psychophysiology (2017), 31(1), 38–48

K. N. Harkess et al., Psychological Effects of Yoga

Physiological Outcomes The secondary aim of this study was to determine if yoga was related to changes in physiological indicators related to allostatic load (Cohen et al., 2007). Yoga intervention was associated with changes in body fat distribution and improved flexibility, with no significant changes in blood pressure or heart rate. Physical activity is known to produce favorable changes to body composition (Wilmore et al., 1999) with reduced body fat previously demonstrated following yoga in a controlled trial (Littman et al., 2012). However, the relatively equal between-group METs of our study suggest that something other than energy expenditure contributed to the decreased abdominal obesity seen in the yoga group. Elevated cortisol is linked to an accumulation of fat in visceral adipose tissues (Björntorp, 2001), thus it is plausible that the neuroendocrine system may be positively influenced by the yoga practice (via improved positive affect potentially), thereby reducing fat accumulation.

The lack of differences for blood pressure and heart rate may be due to the fact that the type of yoga provided was a dynamic style, indicating relaxation-based yoga practice, such as the style used by Seeman et al. (1997), may be more effective for heart-rate reductions. Yoga demonstrated a large positive effect on practitioner flexibility and should be further investigated as an adjunct treatment for back pain rehabilitation (Sager & Grenier, 2014; Tekur, Singphow, Nagendra, & Raghuram, 2008).

In sum, these findings support yoga to be as efficacious as other physical activity for improving elements of practitioners’ health (Ross & Thomas, 2010), although more research is needed to clearly define its scope and specific effects.

Limitations A number of factors need to be taken into account when interpreting the results of this study. First, only women participated in this study, limiting generalizability to men.

Second, the women were self-selected and highly motivated to engage in an intervention to counter the psychological distress they were experiencing. Third, as with all exercise interventions, full blinding is not possible, as participants were aware of what treatment they were getting.

Fourth, this intervention was brief (8 weeks) and the minimum intervention necessary to produce change (MINC) may not have been administered in all domains (i.e., changes indicated over 12 weeks; e.g., Michalsen et al., 2005; Moadel et al., 2007). Fifth, although we measured energy expenditure to control for exercise effects, we used the IPAQ which relies on self-report and the possibility of attentional effects was not addressed due to no active control group. Finally, our study was powered for large effects; Ó 2016 Hogrefe Publishing

K. N. Harkess et al., Psychological Effects of Yoga

however, we were underpowered for smaller effects. Only 5% (6) of participants completed all 16 yoga classes, and 23% failed to attend the minimum 8 classes required for protocol adherence. While this study’s sample size is substantially larger than the 10–25 per group generally seen in randomized trials (Patel et al., 2012), it is suggested that future studies increase sample size in the intervention group to account for dropouts and small effect sizes. Further follow-up research will be conducted using this sample to examine the extent to which changes were sustained over time.

Conclusion In conclusion, our findings suggest that in 2 months, a weekly 60-min yoga class has demonstrated positive psychophysiological benefits for women, which may be protective of developing stress-related psychopathology.

However, the effect sizes were small, and a number of predictor variables were not found to differ significantly between the two groups. Future larger studies with active controls should seek to validate these findings in a nonclinical population and determine if increased exposure impacts on broader outcome measures or yield larger effects that may translate into clinical significance (e.g., 5 years’ practice enhances mindfulness; Brisbon & Lowery, 2011). Further investigation is needed to determine pathways of these psychological and physiological effects. Understanding the biological processes by which these effects are occurring (i.e., through reduction of stress and/or immunological factors) will also be important.

Together, these findings provide support for the conduct of larger studies with biochemical measures to establish the biomechanical role of yoga in psychophysiological stress management. Due to the low cost nature of conducting yoga classes, they have potential to be used to provide a fiscally responsible intervention aimed at decreasing the negative impact of stress many individuals experience.

Acknowledgments We thank Nick Burns for his valuable advice and discussion surrounding the data assessment. We would also like to thank all of the study’s participants for their generous contribution.

Ethics and Disclosure Statements All participants of the study provided written informed consent and the study was approved by the Human Research Ethics Committee of the University of Adelaide. Ó 2016 Hogrefe Publishing

45

The authors disclose no actual or potential conflicts of interest including any financial, personal, or other relationships with other people or organizations that could inappropriately influence (bias) their work.

References Amin, D. J., & Goodman, M. (2014). The effects of selected asanas in Iyengar yoga on flexibility: Pilot study. Journal of Bodywork and Movement Therapies, 18, 399–404. doi: 10.1016/j. jbmt.2013.11.008 Andrews, G., & Slade, T. (2001). Interpreting scores on the Kessler Psychological Distress Scale (K10). Australian and New Zealand Journal of Public Health, 25, 494–497. doi: 10.1111/j.1467842X.2001.tb00310.x Armijo-Olivo, S., Warren, S., & Magee, D. (2009). Intention to treat analysis, compliance, drop-outs and how to deal with missing data in clinical research: A review. Physical Therapy Reviews, 14, 36–49. doi: 10.1179/174328809X405928 Australian Bureau of Statisics (ABS). (2003). Information paper: Use of the Kessler Psychological Distress Scale in ABS Health Surveys. Canberra, Australia: Australian Bureau of Statistics.

Balasubramaniam, M., Telles, S., & Doraiswamy, P. M. (2012).

Yoga on our minds: A systematic review of yoga for neuropsychiatric disorders. Front Psychiatry, 3, 117. doi: 10.3389/ fpsyt.2012.00117 Banerjee, B., Vadiraj, H. S., Ram, A., Rao, R., Jayapal, M., Gopinath, K. S., . . . Prakash Hande, M. (2007). Effects of an integrated yoga program in modulating psychological stress and radiation-induced genotoxic stress in breast cancer patients undergoing radiotherapy. Integrative Cancer Therapies, 6, 242–250. doi: 10.1177/1534735407306214 Belem da Silva, C. T., Schuch, F., Costa, M., Hirakata, V., & Manfro, G. G. (2014). Somatic, but not cognitive, symptoms of anxiety predict lower levels of physical activity in panic disorder patients. Journal of Affective Disorders, 164, 63–68.

Birdee, G. S., Legedza, A. T., Saper, R. B., Bertisch, S. M., Eisenberg, D. M., & Phillips, R. S. (2008). Characteristics of yoga users: Results of a national survey. Journal of General Internal Medicine, 23, 1653–1658.

Björntorp, P. (2001). Do stress reactions cause abdominal obesity and comorbidities? Obesity Reviews, 2, 73–86. doi: 10.1046/ j.1467-789x.2001.00027.x Bonura, K. B. (2011). The psychological benefits of yoga practice for older adults: Evidence and guidelines. International Journal of Yoga Therapy, 21, 129–142.

Brisbon, N. M., & Lowery, G. A. (2011). Mindfulness and levels of stress: A comparison of beginner and advanced hatha yoga practitioners. Journal of Religion and Health, 50, 931–941. doi: 10.1007/s10943-009-9305-3 Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology, 84, 822.

Cabral, P., Meyer, H. B., & Ames, D. (2011). Effectiveness of yoga therapy as a complementary treatment for Major Psychiatric Disorders: A meta-analysis. The Primary Care Companion to CNS Disorders, 13, 7. doi: 10.4088/PCC.10r01068 Carmody, J., & Baer, R. A. (2009). How long does a mindfulnessbased stress reduction program need to be? A review of class contact hours and effect sizes for psychological distress.

Journal of Clinical Psychology, 65, 627–638.

Carroll, J., Blansit, A., Otto, R. M., & Wygand, J. W. (2003). The metabolic requirements of Vinyasa Yoga. Medicine & Science in Sports & Exercise, 35, S155.

Journal of Psychophysiology (2017), 31(1), 38–48

46

Chattha, R., Raghuram, N., Venkatram, P., & Hongasandra, N. R. (2008). Treating the climacteric symptoms in Indian women with an integrated approach to yoga therapy: A randomized control study. Menopause, 15, 862–870. doi: 10.1097/ gme.0b013e318167b902 Chrousos, G. P. (2009). Stress and disorders of the stress system.

Nature Reviews Endocrinology, 5, 374–381. doi: 10.1038/ nrendo.2009.106 Cohen, B. E., Chang, A. A., Grady, D., & Kanaya, A. M. (2008).

Restorative yoga in adults with metabolic syndrome: A randomized, controlled pilot trial. Metabolic Syndrome and Related Disorders, 6, 223–229. doi: 10.1089/met.2008.0016 Cohen, M. M., Penman, S., Pirotta, M., & Da Costa, C. (2005). The integration of complementary therapies in Australian general practice: Results of a national survey. Journal of Alternative and Complementary Medicine, 11, 995–1004. doi: 10.1089/ acm.2005.11.995 Cohen, S., Doyle, W. J., Turner, R. B., Alper, C. M., & Skoner, D. P. (2003). Emotional style and susceptibility to the common cold.

Psychosomatic Medicine, 65, 652–657. 610.1097/1001.

PSY.0000077508.0000057784.DA. Cohen, S., Janicki-Deverts, D., & Miller, G. E. (2007). Psychological stress and disease. Journal of the American Medical Association, 298, 1685–1687. doi: 10.1001/jama.298.14.1685 Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. [Research Support, U.S. Gov’t, Non-P.H.S.]. Journal of Health and Social Behavior, 24, 385–396.

Cohen, S., Kessler, R. C., & Gordon, L. U. (1995). Measuring stress: A guide for health and social scientists. New York, NY: Oxford University Press.

Cowen, V. S., & Adams, T. B. (2005). Physical and perceptual benefits of yoga asana practice: Results of a pilot study.

Journal of Bodywork and Movement, 9, 211–219. doi: 10.1016/ j.jbmt.2004.08.001 Cowen, V. S., & Adams, T. B. (2007). Heart rate in yoga asana practice: A comparison of styles. Journal of Bodywork & Movement Therapies, 11, 91–95.

Cox, B. D., & Whichelow, M. (1996). Ratio of waist circumference to height is better predictor of death than body mass index.

British Medical Journal, 313, 1487.

Craft, L., & Perna, F. (2004). The Benefits of Exercise for the Clinically Depressed: (CME). The Primary Care Companion to the Journal of Clinical Psychiatry, 06, 104–111.

Craig, C. L., Marshall, A. L., Sjostrom, M., Bauman, A. E., Booth, M. L., Ainsworth, B. E., . . . Oja, P. (2003). International physical activity questionnaire: 12-country reliability and validity. Medicine & Science in Sports & Exercise, 35, 1381–1395. doi: 10.1249/01.mss.0000078924.61453.fb Cronbach, L. J., & Furby, L. (1970). How should we measure “change” – Or should we? Psychological Bulletin, 74, 68–80.

Damodaran, A., Malathi, A., Patil, N., Shah, N., Suryavansihi, & Marathe, S. (2002). Therapeutic potential of yoga practices in modifying cardiovascular risk profile in middle aged men and women. Journal of the Association of Physicians of India, 50, 633–640.

Danhauer, S. C., Mihalko, S. L., Russell, G. B., Campbell, C. R., Felder, L., Daley, K., & Levine, E. A. (2009). Restorative yoga for women with breast cancer: Findings from a randomized pilot study. Psychooncology, 18, 360–368. doi: 10.1002/pon.1503 Derry, H. M., Jaremka, L. M., Bennett, J. M., Peng, J., Andridge, R., Shapiro, C., . . . Kiecolt-Glaser, J. K. (2014). Yoga and selfreported cognitive problems in breast cancer survivors: A randomized controlled trial. Psychooncology, 24, 959–966. doi: 10.1002/pon.3707

Journal of Psychophysiology (2017), 31(1), 38–48

K. N. Harkess et al., Psychological Effects of Yoga

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999).

Subjective well-being: Three decades of progress. Psychological Bulletin, 125, 276.

Dua, J., & Hargreaves, L. (1992). Effect of aerobic exercise on negative affect, positive affect, stress, and depression. Perceptual and Motor Skills, 75, 355–361.

Dugard, P. T. J. (1995). Analysis of pre-test-post-test control group designs in educational research. Educational Psychology, 15, 181–197.

Duquesnoy, B., Allaert, F. A., & Verdoncq, B. (1998). Psychosocial and occupational impact of chronic low back pain. Revu de Rhumatism (English Edition), 65, 33–40.

Duraiswamy, G., Thirthalli, J., Nagendra, H. R., & Gangadhar, B. N. (2007). Yoga therapy as an add-on treatment in the management of patients with schizophrenia – a randomized controlled trial. Acta Psychiatrica Scandinavica, 116, 226–232. doi: 10.1111/j.1600-0447.2007.01032.x Elwy, A. R., Groessl, E. J., Eisen, S. V., Riley, K. E., Maiya, M., Lee, J. P., . . . Park, C. L. (2014). A systematic scoping review of yoga intervention components and study quality. American Journal of Preventive Medicine, 47, 220–232. doi: 10.1016/j. amepre.2014.03.012 Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). London, UK: Sage.

Field, T. (2011). Yoga clinical research review. Complementary Therapies in Clinical Practice, 17, 1–8. doi: 10.1016/ j.ctcp.2010.09.007 Folkman, S., & Moskowitz, J. T. (2000). Positive affect and the other side of coping. American Psychologist, 55, 647–654. doi: 10.1037/0003-066x.55.6.647 Fredrickson, B., Losada, M., & Anderson, Norman. B. (2005).

Positive affect and the complex dynamics of human flourishing.

American Psychologist, 60, 678–686.

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The Broaden-and-Build Theory of positive emotions. American Psychologist, 56, 218–226.

Gard, T., Noggle, J. J., Park, C. L., Vago, D. R., & Wilson, A. (2014).

Potential self-regulatory mechanisms of yoga for psychological health. Frontiers in Human Neuroscience, 8, 1–20. doi: 10.3389/ fnhum.2014.00770 Glaser, R., & Kiecolt-Glaser, J. K. (2005). Stress-induced immune dysfunction: Implications for health. Nature Reviews Immunology, 5, 243–251.

Grossman, P., Niemann, L., Schmidt, S., & Walach, H. (2004).

Mindfulness-based stress reduction and health benefits. A meta-analysis. Journal of Psychosomatic Research, 57, 35–43. doi: 10.1016/s0022-3999(03)00573-7 Gupta, S. K. (2011). Intention-to-treat concept: A review. Perspectives in Clinical Research, 2, 109–112. doi: 10.4103/22293485.83221 Hagins, M., States, R., Selfe, T., & Innes, K. (2013). Effectiveness of yoga for hypertension: Systematic review and meta-analysis.

Evidence-Based Complementary and Alternative Medicine, 2013. doi: 10.1155/2013/649836 Harder, H., Parlour, L., & Jenkins, V. (2012). Randomised controlled trials of yoga interventions for women with breast cancer: A systematic literature review. Support Care Cancer, 20, 3055–3064. doi: 10.1007/s00520-012-1611-8 Hartfiel, N., Havenhand, J., Khalsa, S. B., Clarke, G., & Krayer, A. (2011). The effectiveness of yoga for the improvement of wellbeing and resilience to stress in the workplace. Scandinavian Journal of Work, Environment & Health, 37, 70–76.

Huang, F. J., Chien, D. K., & Chung, U. L. (2013). Effects of Hatha yoga on stress in middle-aged women. Journal of Nursing Research, 21, 59–66.

Ó 2016 Hogrefe Publishing

K. N. Harkess et al., Psychological Effects of Yoga

International Wellbeing Group. (2006). Personal Wellbeing Index.

Melbourne, Australia: Deakin University. Retrieved from http:// www.deakin.edu.au/research/acqol/instruments/wellbeing-index/ pwi-a-english.pdf IPAQ Web site. (2005). Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ) – Short and Long Forms. Retrieved from http://www.ipaq.ki.se. ipaddress.com/ Janssen, I., Katzmarzyk, P. T., & Ross, R. (2002). Body mass index, waist circumference, and health risk: Evidence in support of current national institutes of health guidelines. Archives of Internal Medicine, 162, 2074–2079. doi: 10.1001/archinte.162.18.2074 Kessler, R., & Mroczek, D. (1994). Final versions of our non-specific psychological distress scale, 10, 1994. [Written communication.

Memo dated March 10, 1994].

Khalsa, S. B., Shorter, S. M., Cope, S., Wyshak, G., & Sklar, E. (2009). Yoga ameliorates performance anxiety and mood disturbance in young professional musicians. Applied Psychophysiology and Biofeedback, 34, 279–289. doi: 10.1007/ s10484-009-9103-4 Kiecolt-Glaser, J. K., Bennett, J. M., Andridge, R., Peng, J., Shapiro, C. L., Malarkey, W. B., . . . Glaser, R. (2014). Yoga’s impact on inflammation, mood, and fatigue in breast cancer survivors: A randomized controlled trial. Journal of Clinical Oncology, 1040–1049. doi: 10.1200/jco.2013.51.8860 Kiecolt-Glaser, J. K., Christian, L., Preston, H., Houts, C. R., Malarkey, W. B., Emery, C. F., & Glaser, R. (2010). Stress, inflammation, and yoga practice [Comparative Study Research Support, N.I.H., Extramural]. Psychosomatic Medicine, 72, 113–121. doi: 10.1097/PSY.0b013e3181cb9377 Kiecolt-Glaser, J. K., Christian, L. M., Andridge, R., Hwang, B. S., Malarkey, W. B., Belury, M. A., . . . Glaser, R. (2012). Adiponectin, leptin, and yoga practice. [Research Support, N.I.H., Extramural]. Physiology & Behavior, 107, 809–813. doi: 10.1016/j.physbeh.2012.01.016 Kiecolt-Glaser, J. K., McGuire, L., Robles, T. F., & Glaser, R. (2002).

Emotions, morbidity, and mortality: New perspectives from psychoneuroimmunology. Annual Review of Psychology, 53, 83–107.

Kinser, P. A., Bourguignon, C., Whaley, D., Hauenstein, E., & Taylor, A. G. (2013). Feasibility, acceptability, and effects of gentle Hatha yoga for women with major depression: Findings from a randomized controlled mixed-methods study. Archives of Psychiatric Nursing, 27, 137–147. doi: 10.1016/j.apnu.2013.01.003 Klatt, M. D., Buckworth, J., & Malarkey, W. B. (2008). Effects of low-dose mindfulness-based stress reduction (MBSR-ld) on working adults. Health Education & Behavior, 36, 601–614.

Kuntsevich, V., Bushell, W. C., & Theise, N. D. (2010). Mechanisms of yogic practices in health, aging, and disease. Mount Sinai Journal of Medicine, 77, 559–569. doi: 10.1002/msj.20214 La Forge, R. (2005). Aligning mind and body: Exploring the disciplines of mindful exercise. ACSM’s Health & Fitness Journal, 9, 7–14.

Lesaffre, E., & Verbeke, G. (2005). Clinical Trials and Intervention Studies. In B. S. Everitt & D.C. Howell (Eds.), Encyclopedia of Statistics in Behavioral Science (Vol. 1, pp. 301–305). New York: Wiley.

Li, A. W., & Goldsmith, C. A. (2012). The effects of yoga on anxiety and stress. Alternative Medicine Review, 17, 21–35.

Littman, A. J., Bertram, L. C., Ceballos, R., Ulrich, C. M., Ramaprasad, J., McGregor, B., & McTiernan, A. (2012). Randomized controlled pilot trial of yoga in overweight and obese breast cancer survivors: Effects on quality of life and anthropometric measures. Support Care Cancer, 20, 267–277. doi: 10.1007/ s00520-010-1066-8

Ó 2016 Hogrefe Publishing

47

Lopez-Minarro, P. A., Muyor, J. M., & Alacid, F. (2011). Validity of sit-and-reach tests as measures of hamstring extensibility in older women. Revista Internacional De Medicina Y Ciencias De La Actividad Fisca Y Del Deporte, 11, 564–572.

Lox, C. L., Burns, S. P., Treasure, D. C., & Wasley, D. A. (1999).

Physical and psychological predictors of exercise dosage in healthy adults. Medicine & Science in Sports & Exercise, 31, 1060–1064.

Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success?

Psychological Bulletin, 131, 803–855. doi: 10.1037/00332909.131.6.803 Marchand, W. R. (2012). Mindfulness-based stress reduction, mindfulness-based cognitive therapy, and Zen meditation for depression, anxiety, pain, and psychological distress. Journal of Psychiatric Practice, 18, 233–252.

Mazure, C. M. (1998). Life stressors as risk factors in depression.

Clinical Psychology: Science and Practice, 5, 291–313.

McEwen, B. S. (2002). The neurobiology and neuroendocrinology of stress. Implications for post-traumatic stress disorder from a basic science perspective [Review]. Psychiatric Clinics of North America, 25, 469–494.

McEwen, B. S. (2006). Protective and damaging effects of stress mediators: Central role of the brain. Dialogues in Clinical Neuroscience, 8, 367.

McEwen, B. S., Flier, J. S., & Underhill, L. H. (1998). Protective and damaging effects of stress mediators. New England Journal of Medicine, 338, 171–179.

McEwen, B. S., & Stellar, E. (1993). Stress and the individual Mechanisms leading to disease. Archives of Internal Medicine, 153, 2093–2101.

Michalsen, A., Grossman, P., Acil, A., Langhorst, J., Ludtke, R., Esch, T., . . . Dobos, G. J. (2005). Rapid stress reduction and anxiolysis among distressed women as a consequence of a three-month intensive yoga program. Medical Science Monitor, 11, CR555–CR561.

Moadel, A. B., Shah, C., Wylie-Rosett, J., Harris, M. S., Patel, S. R., Hall, C. B., & Sparano, J. A. (2007). Randomized controlled trial of yoga among a multiethnic sample of breast cancer patients: Effects on quality of life. Journal of Clinical Oncology, 25, 4387–4395. doi: 10.1200/jco.2006.06.6027 Monroe, S. M., Simons, A. D., & Thase, M. E. (1991). Onset of depression and time to treatment entry: Roles of life stress [Research Support, U.S. Gov’t, P.H.S.]. Journal of Consulting and Clinical Psychology, 59, 566–573.

Moyé, L. A., & Tita, A. T. N. (2002). Defending the rationale for the two-tailed test in clinical research. Circulation, 105, 3062–3065. doi: 10.1161/01.cir.0000018283.15527.97 Narasimhan, L., Nagarathna, R., & Nagendra, H. (2011). Effect of integrated yogic practices on positive and negative emotions in healthy adults. International Journal of Yoga, 4, 13–19. doi: 10.4103/0973-6131.78174 Nolen-Hoeksema, S., Larson, J., & Grayson, C. (1999). Explaining the gender difference in depressive symptoms [Research Support, U.S. Gov’t, P.H.S.]. Journal of Personality and Social Psychology, 77, 1061–1072.

Oken, B. S., Zajdel, D., Kishiyama, S., Flegal, K., Dehen, C., Haas, M., . . . Leyva, J. (2006). Randomized, controlled, six-month trial of yoga in healthy seniors: Effects on cognition and quality of life.

Alternative Therapies, Health and Medicine, 12, 40–47.

Patel, N. K., Newstead, A. H., & Ferrer, R. L. (2012). The effects of yoga on physical functioning and health related quality of life in older adults: A systematic review and meta-analysis. Journal of Alternative and Complementary Medicine, 18, 902–917. doi: 10.1089/acm.2011.0473

Journal of Psychophysiology (2017), 31(1), 38–48

48

Penman, S., Cohen, M., Stevens, P., & Jackson, S. (2012). Yoga in Australia: Results of a national survey. International Journal of Yoga, 5, 92–101. doi: 10.4103/0973-6131.98217 Perneger, T. V. (1998). What’s wrong with Bonferroni corrections?

British Medical Journal, 316, 1236–1238.

Pilkington, K., Kirkwood, G., Rampes, H., & Richardson, J. (2005).

Yoga for depression: The research evidence. Journal of Affective Disorders, 89, 13–24. doi: 10.1016/j.jad.2005.08.013 Ray, U. S., Pathak, A., & Tomer, O. S. (2011). Hatha yoga practices: Energy expenditure, respiratory changes and intensity of exercise. Evidence-Based Complementary and Alternative Medicine, 2011, 241–294.

Rice, V. H. (2012). Handbook of stress, coping, and health: Implications for nursing research, theory, and practice. London, UK: Sage.

Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: A review of comparison studies. Journal of Alternative and Complementary Medicine, 16, 3–12. doi: 10.1089/ acm.2009.0044 Ryan, M., White, K., Roydhouse, J. K., & Fethney, J. (2011). A description of the nutritional status and quality of life of Australian gynaecological cancer patients over time. European Journal of Oncology Nursing, 16, 453–459.

Sadja, J., & Mills, P. J. (2013). Effects of yoga interventions on fatigue in cancer patients and survivors: A systematic review of randomized controlled trials. Explore (NY), 9, 232–243. doi: 10.1016/j.explore.2013.04.005 Sager, M., & Grenier, S. (2014). Comparison of yoga versus static stretching for increasing hip and shoulder range of motion.

International Journal of Physical Medicine & Rehabilitation, 2, 208. doi: 10.4172/2329-9096.1000208 Salmon, P. (2001). Effects of physical exercise on anxiety, depression, and sensitivity to stress: A unifying theory. Clinical Psychology Review, 21, 33–61. doi: 10.1016/s0272-7358(99) 00032-x Satyapriya, M., Nagendra, H. R., Nagarathna, R., & Padmalatha, V. (2009). Effect of integrated yoga on stress and heart rate variability in pregnant women. International Journal of Gynecology and Obstetrics, 104, 218–222. doi: 10.1016/j.ijgo.2008.11.013 Savva, S. C., Lamnisos, D., & Kafatos, A. G. (2013). Predicting cardiometabolic risk: Waist-to-height ratio or BMI. A metaanalysis. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 6, 403–419. doi: 10.2147/dmso.s34220 Seeman, T. E., Singer, B. H., Rowe, J. W., Horwitz, R. I., & McEwen, B. S. (1997). Price of adaptation-allostatic load and its health consequences. MacArthur studies of successful aging.

[Research Support, Non-U.S. Gov’t, U.S. Gov’t, P.H.S.]. Archives of Internal Medicine, 157, 2259–2268.

Segerstrom, S. C., & Miller, G. E. (2004). Psychological stress and the human immune system: A meta-analytic study of 30 years of inquiry. Psychological Bulletin, 130, 601.

Smith, C., Hancock, H., Blake-Mortimer, J., & Eckert, K. (2007). A randomised comparative trial of yoga and relaxation to reduce stress and anxiety. Complementary Therapies in Medicine, 15, 77–83. doi: 10.1016/j.ctim.2006.05.001 Sujatha, T., & Judie, A. (2014). Effectiveness of a 12-week yoga program on physiopsychological parameters in patients with hypertension. International Journal of Pharmaceutical and Clinical Research, 6, 329–335.

Tekur, P., Singphow, C., Nagendra, H. R., & Raghuram, N. (2008).

Effect of short-term intensive yoga program on pain, functional disability and spinal flexibility in chronic low back pain: A randomized control study. Journal of Alternative and Complementary Medicine, 14, 637–644. doi: 10.1089/acm.2007.0815 Tilbrook, H. E., Cox, H., Hewitt, C. E., Kang’ombe, A. R., Chuang, L. H., Jayakody, S., . . . Torgerson, D. J. (2011). Yoga for chronic

Journal of Psychophysiology (2017), 31(1), 38–48

K. N. Harkess et al., Psychological Effects of Yoga

low back pain: A randomized trial. Annals of Internal Medicine, 155, 569–578.

Tolbanos Roche, L., & Mas Hesse, B. (2014). Application of an integrative yoga therapy programme in cases of essential arterial hypertension in public healthcare. Complementary Therapies in Clinical Practice, 20, 285–290. doi: 10.1016/ j.ctcp.2014.10.004 Urbaniak, G. C., & Plous, S. (2013). Research randomizer (Version 4.0) [Computer software]. Retrieved from http://www. randomizer.org/ Vadiraja, H. S., Rao, M. R., Nagarathna, R., Nagendra, H. R., Rekha, M., Vanitha, N., . . . Rao, N. (2009). Effects of yoga program on quality of life and affect in early breast cancer patients undergoing adjuvant radiotherapy: A randomized controlled trial. Complementary Therapies in Medicine, 17, 274–280. doi: 10.1016/j.ctim.2009.06.004 Valoriani, V., Lotti, F., Vanni, C., Noci, M.-C., Fontanarosa, N., Ferrari, G., . . . Noci, I. (2014). Hatha-yoga as a psychological adjuvant for women undergoing IVF: A pilot study. European Journal of Obstetrics & Gynecology and Reproductive Biology, 176, 158–162. doi: 10.1016/j.ejogrb.2014.02.007 Van Breukelen, G. J. (2006). ANCOVA versus change from baseline: More power in randomized studies, more bias in nonrandomized studies [corrected]. Journal of Clinical Epidemiology, 59, 920–925. doi: 10.1016/j.jclinepi.2006.02.007 Vos, T., Flaxman, A. D., Naghavi, M., Lozano, R., Michaud, C., Ezzati, M., . . . Memish, Z. A. (2012). Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet, 380, 2163–2196. doi: 10.1016/ s0140-6736(12)61729-2 Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063.

Wilmore, J. H., Despres, J. P., Stanforth, P. R., Mandel, S., Rice, T., Gagnon, J., . . . Bouchard, C. (1999). Alterations in body weight and composition consequent to 20 wk of endurance training: The HERITAGE Family Study. American Journal of Clinical Nutrition, 70, 346–352.

Witvrouw, E., Danneels, L., Asselman, P., D’Have, T., & Cambier, D. (2003). Muscle flexibility as a risk factor for developing muscle injuries in male professional soccer players. A prospective study. American Journal of Sports Medicine, 31, 41–46.

Woodyard, C. (2011). Exploring the therapeutic effects of yoga and its ability to increase quality of life. International Journal of Yoga, 4, 49–54. doi: 10.4103/0973-6131.85485 Yang, K. (2007). A review of yoga programs for four leading risk factors of chronic diseases. Evidence-Based Complementary and Alternative Medicine, 4, 487–491. doi: 10.1093/ecam/nem154

Received February 27, 2015 Accepted November 4, 2015 Published online July 27, 2016

Kaitlin Harkess Department of Psychology Level 4, Hughes Building The University of Adelaide Adelaide, SA 5000 Australia Tel. +61 8 8313-7402 Fax +61 8 8313-3770 E-mail kaitlin.harkess@adelaide.edu.au

Ó 2016 Hogrefe Publishing