Perceived acceptability of wearable devices for the treatment of mental health problems
Corresponding Author
Hugh Hunkin
Nutrition and Health Research Program, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
School of Psychology, University of Adelaide, Adelaide, Australia
Correspondence Hugh Hunkin, CSIRO, Gate 13 Kintore Avenue, Adelaide, SA 5000, Australia.
Email: [email protected]
Search for more papers by this authorDaniel L. King
School of Psychology, University of Adelaide, Adelaide, Australia
College of Education, Psychology and Social Work, Flinders University, Adelaide, Australia
Search for more papers by this authorIan T. Zajac
Nutrition and Health Research Program, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
School of Psychology, University of Adelaide, Adelaide, Australia
Search for more papers by this authorCorresponding Author
Hugh Hunkin
Nutrition and Health Research Program, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
School of Psychology, University of Adelaide, Adelaide, Australia
Correspondence Hugh Hunkin, CSIRO, Gate 13 Kintore Avenue, Adelaide, SA 5000, Australia.
Email: [email protected]
Search for more papers by this authorDaniel L. King
School of Psychology, University of Adelaide, Adelaide, Australia
College of Education, Psychology and Social Work, Flinders University, Adelaide, Australia
Search for more papers by this authorIan T. Zajac
Nutrition and Health Research Program, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
School of Psychology, University of Adelaide, Adelaide, Australia
Search for more papers by this authorAbstract
Objective
This study examined the potential acceptability of wearable devices (e.g., smart headbands, wristbands, and watches) aimed at treating mental health disorders, relative to conventional approaches.
Methods
A questionnaire assessed perceptions of wearable and nonwearable treatments, along with demographic and psychological information. Respondents (N = 427) were adults from a community sample (Mage = 44.6, SDage = 15.3) which included current (30.2%) and former (53.9%) mental health help-seekers.
Results
Perceived effectiveness of wearables was a strong predictor of interest in using them as adjuncts to talk therapies, or as an alternative to self-help options (e.g., smartphone applications). Devices were more appealing to those with negative evaluations of psychological therapy and less experience in help-seeking.
Conclusions
Interest in using wearable devices was strong, particularly when devices were seen as effective. Clients with negative attitudes to conventional therapies may be more responsive to using wearable devices as a less directive treatment approach.
Supporting Information
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Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- American Psychiatric Association (2017). App evaluation model. Retrieved from https://www.psychiatry.org/psychiatrists/practice/mental-health-apps/app-evaluation-model
- Anton, M. T., & Jones, D. J. (2017). Adoption of technology-enhanced treatments: Conceptual and practical considerations. Clinical Psychology: Science and Practice, 24(3), 223–240. https://doi.org/10.1111/cpsp.12197
- Arjadi, R., Nauta, M. H., & Bockting, C. L. H. (2018). Acceptability of internet-based interventions for depression in Indonesia. Internet Interventions, 13, 8–15. https://doi.org/10.1016/J.INVENT.2018.04.004
- Australian Bureau of Statistics (2016a). 1270.0.55.005 - Australian Statistical Geography Standard (ASGS): Volume 5 - remoteness structure, July 2016. Retrieved from https://www.abs.gov.au/ausstats/[email protected]/mf/1270.0.55.005
- Australian Bureau of Statistics (2016b). Socio-economic indexes for areas (SEIFA) 2016. Retrieved from http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/2033.0.55.001Main+Features12016?OpenDocument
- Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254.
- Balconi, M., Fronda, G., Venturella, I., & Crivelli, D. (2017). Conscious, pre-conscious and unconscious mechanisms in emotional behaviour. Some applications to the mindfulness approach with wearable devices. Applied Sciences, 7, 1–14. https://doi.org/10.3390/app7121280
10.3390/app7121280 Google Scholar
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.
- Berry, N., Lobban, F., Emsley, R., & Bucci, S. (2016). Acceptability of interventions delivered online and through mobile phones for people who experience severe mental health problems: A systematic review. Journal of Medical Internet Research, 18(5), e121. https://doi.org/10.2196/jmir.5250
- Beutler, L. E., Harwood, T. M., Michelson, A., Song, X., & Holman, J. (2011). Resistance/reactance level. Journal of Clinical Psychology, 67(2), 133–142. https://doi.org/10.1002/jclp.20753
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
10.1191/1478088706qp063oa Google Scholar
- Casey, L. M., Joy, A., & Clough, B. A. (2013). The impact of information on attitudes toward e-mental health services. Cyberpsychology, Behavior and Social Networking, 16(8), 593–598. https://doi.org/10.1089/cyber.2012.0515
- Christensen, H., & Hickie, I. B. (2010). Using e-health applications to deliver new mental health services. The Medical Journal of Australia, 192(11), S53–S56.
- Clough, B. A., Zarean, M., Ruane, I., Mateo, N. J., Aliyeva, T. A., & Casey, L. M. (2017). Going global: Do consumer preferences, attitudes, and barriers to using e-mental health services differ across countries? Journal of Mental Health, (Early Online), 28, 1–8. https://doi.org/10.1080/09638237.2017.1370639
- Coffey, M. J., & Coffey, C. E. (2016). The emerging story of emerging technologies in neuropsychiatry. Dialogues in Clinical Neuroscience, 18(2), 127–134. https://doi.org/10.1097/BOR.0b013e32834b5457
- Cohen, J. (1988). Statistical power analysis for the behavioural sciences ( 2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
- Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155
- Corrigan, P. W., & Rao, D. (2012). On the self-stigma of mental illness: Stages, disclosure, and strategies for change. Canadian Journal of Psychiatry, 57(8), 464–469. https://doi.org/10.1177/070674371205700804
- Demyttenaere, K., Bruffaerts, R., Posada-Villa, J., Gasquet, I., Kovess, V., Lepine, J. P., … Chatterji, S. (2004). Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. Journal of the American Medical Association, 291(21), 2581. https://doi.org/10.1001/jama.291.21.2581
- Fagerland, M. W. (2012). t-tests, non-parametric tests, and large studies – a paradox of statistical practice? BMC Medical Research Methodology, 12(78), 1–7.
- Faul, F., Erdfelder, E., Buchner, A., & Lang, A. -G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
- Gartner (2018). Gartner says worldwide wearable device sales to grow 26 percent in 2019. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2018-11-29-gartner-says-worldwide-wearable-device-sales-to-grow-
- Gun, S. Y., Titov, N., & Andrews, G. (2011). Acceptability of internet treatment of anxiety and depression. Australasian Psychiatry, 19(3), 259–264. https://doi.org/10.3109/10398562.2011.562295
- Harrell, F. E. (2015). Regression modeling strategies. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-19425-7
10.1007/978-3-319-19425-7 Google Scholar
- Heinze, G., Wallisch, C., & Dunkler, D. (2018). Variable selection – A review and recommendations for the practicing statistician. Biometrical Journal, 60(3), 431–449. https://doi.org/10.1002/bimj.201700067
- Hollis, C., Sampson, S., Simons, L., Davies, E. B., Churchill, R., Betton, V., … Tomlin, A. (2018). Identifying research priorities for digital technology in mental health care: Results of the James Lind alliance priority setting partnership. The Lancet Psychiatry, 5, 845–854. https://doi.org/10.1016/S2215-0366(18)30296-7
- van Houwelingen, H. C., & Sauerbrei, W. (2013). Cross-validation, shrinkage and variable selection in linear regression revisited. Open Journal of Statistics, 3, 79–102. https://doi.org/10.4236/ojs.2013.32011
10.4236/ojs.2013.32011 Google Scholar
- Hunkin, H., King, D. L., & Zajac, I. T. (2019). Wearable devices as adjuncts in the treatment of anxiety-related symptoms: A narrative review of five device modalities and implications for clinical practice. Clinical Psychology: Science and Practice, 26(3), https://doi.org/10.1111/cpsp.12290
- Jorm, A., Patten, S. B., Brugha, T. S., & Mojtabai, R. (2017). Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry, 16(1), 90–99. https://doi.org/10.1002/wps.20388
- Klein, B., & Cook, S. (2010). Preferences for e-mental health services amongst an online Australian sample? Electronic. Journal of Applied Psychology, 6(1), 28–39. https://doi.org/10.7790/ejap.v6i1.184
- Korstjens, I., & Moser, A. (2018). Series: Practical guidance to qualitative research. Part 4: Trustworthiness and publishing. European Journal of General Practice, 24(1), 120–124. https://doi.org/10.1080/13814788.2017.1375092
- Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., … Swendeman, D. (2013). Mobile health technology evaluation. American Journal of Preventive Medicine, 45(2), 228–236. https://doi.org/10.1016/j.amepre.2013.03.017
- Labouliere, C. D., Kleinman, M., & Gould, M. S. (2015). When self-reliance is not safe: Associations between reduced help-seeking and subsequent mental health symptoms in suicidal adolescents. International Journal of Environmental Research and Public Health, 12(4), 3741–3755. https://doi.org/10.3390/ijerph120403741
- Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the depression anxiety stress scales. Sydney, Australia: Psychology Foundation.
- Lui, J. H. L., Marcus, D. K., & Barry, C. T. (2017). Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Professional Psychology: Research and Practice, 48(3), 199–210. https://doi.org/10.1037/pro0000122
- March, S., Day, J., Ritchie, G., Rowe, A., Gough, J., Hall, T., … Ireland, M. (2018). Attitudes toward e-mental health services in a community sample of adults: Online survey. Journal of Medical Internet Research, 20(2), e59. https://doi.org/10.2196/jmir.9109
- Mehrotra, S., Kumar, S., Sudhir, P., Rao, G. N., Thirthalli, J., & Gandotra, A. (2017). Unguided mental health self-help apps: Reflections on challenges through a clinician's lens. Indian Journal of Psychological Medicine, 39(4), 52–57. https://doi.org/10.4103/IJPSYM.IJPSYM
- Mohr, D. C., Cheung, K., Schueller, S. M., Hendricks Brown, C., & Duan, N. (2013). Continuous evaluation of evolving behavioral intervention technologies. American Journal of Preventive Medicine, 45(4), 1–11. https://doi.org/10.1016/j.amepre.2013.06.006.Continuous
- Mohr, D. C., Ho, J., Duffecy, J., Baron, K. G., Lehman, K. A., Jin, L., & Reifler, D. (2010). Perceived barriers to psychological treatments and their relationship to depression. Journal of Clinical Psychology, 66(4), 394–409. https://doi.org/10.1002/jclp.20659
- Mohr, D. C., Lyon, A. R., Lattie, E. G., Reddy, M., & Schueller, S. M. (2017). Accelerating digital mental health research from early design and creation to successful implementation and sustainment. Journal of Medical Internet Research, 19(5), 1–14. https://doi.org/10.2196/jmir.7725
- Mojtabai, R., Olfson, M., Sampson, N. A., Druss, B., Wang, P. S., Wells, K. B., … Kessler, R. C. (2012). Barriers to mental health treatment: Results from the national comorbidity survey replication (NCS-R). Psychological Medicine, 41(8), 1751–1761. https://doi.org/10.1017/S0033291710002291.Barriers
- Montague, A. E., Varcin, K. J., Simmons, M. B., & Parker, A. G. (2015). Putting technology into youth mental health practice: Young people's perspectives. SAGE Open, 5, 1–10. https://doi.org/10.1177/2158244015581019
- Musiat, P., Goldstone, P., & Tarrier, N. (2014). Understanding the acceptability of e-mental health - attitudes and expectations towards computerized self-help treatments for mental health problems. BMC Psychiatry, 14(109), 1–7. https://doi.org/10.1186/1471-244X-14-109
- Naslund, J. A., Aschbrenner, K. A., Kim, S. J., McHugo, G. J., Unutzer, J., Bartels, S. J., & Marsch, L. A. (2017). Health behavior models for informing digital technology interventions for individuals with mental illness. Psychiatric Rehabilitation Journal, 40(3), 325–335. https://doi.org/10.1037/prj0000246
- Nicholas, J., Huckvale, K., Larsen, M. E., Basu, A., Batterham, P. J., Shaw, F., & Sendi, S. (2017). Issues for eHealth in psychiatry: Results of an expert survey. Journal of Medical Internet Research, 19(2), e55. https://doi.org/10.2196/jmir.6957
- Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
- R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.r-project.org/
- Rickwood, D. J. (2015). Responding effectively to support the mental health and well-being of young people. In J. Wyn & H. Cahill (Eds.), Handbook of children and youth studies (pp. 139–154). Singapore: Springer Science+Business Media. https://doi.org/10.1007/978-981-4451-15-4_12
10.1007/978-981-4451-15-4_12 Google Scholar
- Rickwood, D. J., Deane, F. P., & Wilson, C. J. (2007). When and how do young people seek professional help for mental health problems? The Medical Journal of Australia, 187(7), S35–S39. https://doi.org/10.5694/j.1326-5377.2007.tb01334.x
- Riley, W. T., Glasgow, R. E., Etheredge, L., & Abernethy, A. P. (2013). Rapid, responsive, relevant (R3) research: A call for a rapid learning health research enterprise. Clinical and Translational Medicine, 2(10), https://doi.org/10.1186/2001-1326-2-10
- Ritterband, L. M., Thorndike, F. P., Cox, D. J., Kovatchev, B. P., & Gonder-Frederick, L. A. (2009). A behavior change model for Internet interventions. Annals of Behavioral Medicine, 38, 18–27. https://doi.org/10.1007/s12160-009-9133-4
- Rojas-Méndez, J. I., Parasuraman, A., & Papadopoulos, N. (2015). Demographics, attitudes and technology readiness. Marketing Intelligence & Planning, 33(7), 1087–1102. https://doi.org/10.1108/MIP-06-2015-0125
- Schmider, E., Ziegler, M., Danay, E., Beyer, L., & Bühner, M. (2010). Is it really robust?: Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption. Methodology, 6(4), 147–151. https://doi.org/10.1027/1614-2241/a000016
10.1027/1614-2241/a000016 Google Scholar
- Streiner, D. L. (2015). Best (but oft-forgotten) practices: The multiple problems of multiplicity-whether and how to correct for many statistical tests. American Journal of Clinical Nutrition, 102(4), 721–728. https://doi.org/10.3945/ajcn.115.113548
- Swift, J. K., Callahan, J. L., Cooper, M., & Parkin, S. R. (2018). The impact of accommodating client preference in psychotherapy: A meta-analysis. Journal of Clinical Psychology, 74(11), 1924–1937. https://doi.org/10.1002/jclp.22680
- Thornton, L., Batterham, P. J., Fassnacht, D. B., Kay-Lambkin, F., Calear, A. L., & Hunt, S. (2016). Recruiting for health, medical or psychosocial research using Facebook: Systematic review. Internet Interventions, 4, 72–81. https://doi.org/10.1016/j.invent.2016.02.001
- Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., … Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World psychiatry, 18(1), 97–98. https://doi.org/10.1002/wps.20592
- Torous, J., & Gualtieri, L. (2016). Wearable devices for mental health: Knowns and unknowns. Psychiatric Times, 33(6).
- Venkatesh, V., Thong, J. Y. L. T., & Xu, X. (2012). Consumer acceptance and use of IT. MIS Quarterly, 36(1), 157–178.
- Wallin, E., Maathz, P., Parling, T., & Hursti, T. (2018). Self-stigma and the intention to seek psychological help online compared to face-to-face. Journal of Clinical Psychology, 74(7), 1207–1218. https://doi.org/10.1002/jclp.22583