Hostname: page-component-7dd5485656-wlg5v Total loading time: 0 Render date: 2025-10-30T02:54:03.782Z Has data issue: false hasContentIssue false
  • English
  • Français

Preventing depression using a smartphone app: a randomized controlled trial

Published online by Cambridge University Press:  06 July 2020

Mark Deady Open the ORCID record for Mark Deady [Opens in a new window] ,
Nicholas Glozier Open the ORCID record for Nicholas Glozier [Opens in a new window] ,
Rafael Calvo ,
David Johnston ,
Andrew Mackinnon ,
David Milne ,
Isabella Choi ,
Aimee Gayed ,
Dorian Peters  and
Richard Bryant
...Show all authors
Mark Deady*
Affiliation:
Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
Nicholas Glozier
Affiliation:
Central Clinical School, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
Rafael Calvo
Affiliation:
School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia Dyson School of Design Engineering, Imperial College London, London, UK
David Johnston
Affiliation:
Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
Andrew Mackinnon
Affiliation:
Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
David Milne
Affiliation:
School of Systems Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, Australia
Isabella Choi
Affiliation:
Central Clinical School, Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
Aimee Gayed
Affiliation:
School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
Dorian Peters
Affiliation:
School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
Richard Bryant
Affiliation:
School of Psychology, University of New South Wales, Sydney, NSW, Australia
Helen Christensen
Affiliation:
Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
Samuel B. Harvey
Affiliation:
Black Dog Institute, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
*
Author for correspondence: Mark Deady, E-mail: m.deady@unsw.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

There is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.

Methods

A randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.

Results

Those assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p = 0.001, 95% CI 0.26–0.70).

Conclusions

This trial demonstrates that a smartphone app can reduce depression symptoms and potentially prevent incident depression caseness and such interventions may have a role in improving working population mental health. Some caution in interpretation is needed regarding the clinical significance due to small effect size and trial attrition.

Trial Registration Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au/) ACTRN12617000548336

Keywords

Behavioural activationdepressioneHealthmHealthmindfulnessrandomized control trialsmartphoneworkplace mental health

Information

Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 3 , February 2022 , pp. 457 - 466
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Ahern, J., Jones, M. R., Bakshis, E., & Galea, S. (2008). Revisiting rose: Comparing the benefits and costs of population-wide and targeted interventions. The Milbank Quarterly, 86(4), 581–600. doi: 10.1111/j.1468-0009.2008.00535.xCrossRefGoogle ScholarPubMed
Andrews, G., Issakidis, C., Sanderson, K., Corry, J., & Lapsley, H. (2004). Utilising survey data to inform public policy: Comparison of the cost-effectiveness of treatment of ten mental disorders. British Journal of Psychiatry, 184, 526–533.10.1192/bjp.184.6.526CrossRefGoogle ScholarPubMed
Australian Bureau of Statistics. (2008). Australian and New Zealand standard industrial classification. Retrieved from Canberra: https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1292.0.55.0052008?OpenDocument.Google Scholar
Battams, S., Roche, A. M., Fischer, J. A., Lee, N. K., Cameron, J., & Kostadinov, V. (2014). Workplace risk factors for anxiety and depression in male-dominated industries: A systematic review. Health Psychology and Behavioral Medicine, 2(1), 983–1008. doi: 10.1080/21642850.2014.954579CrossRefGoogle ScholarPubMed
Bech, P. (2004). Measuring the dimensions of psychological general well-being by the WHO-5. Quality of Life Newsletter, 32, 15–16. doi: 10.1159/000289073Google Scholar
Connor, K. M., & Davidson, J. R. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18(2), 76–82. doi: 10.1002/da.10113CrossRefGoogle Scholar
Cuijpers, P., Beekman, A. T. F., & Reynolds, C. F. (2012). Preventing depression: A global priority. Journal of the American Medical Association, 307(10), 1033–1034. doi: 10.1001/jama.2012.271CrossRefGoogle ScholarPubMed
Cuijpers, P., Van Straten, A., & Smit, F. (2005). Preventing the incidence of new cases of mental disorders: A meta-analytic review. Journal of Nervous and Mental Disease, 193(2), 119–125. doi: 00005053-200502000-00006 [pii]CrossRefGoogle ScholarPubMed
Deady, M., Choi, I., Calvo, R. A., Glozier, N., Christensen, H., & Harvey, S. B. (2017a). Ehealth interventions for the prevention of depression and anxiety in the general population: A systematic review and meta-analysis. BMC Psychiatry, 17, 310. doi: 10.1186/s12888-017-1473-1CrossRefGoogle Scholar
Deady, M., Johnston, D. A., Glozier, N., Milne, D., Choi, I., Mackinnon, A., … Harvey, S. B. (2018). Smartphone application for preventing depression: Study protocol for a workplace randomised controlled trial. BMJ Open, 8(7), e020510. doi: 10.1136/bmjopen-2017-020510.CrossRefGoogle ScholarPubMed
Deady, M., Peters, D., Lang, H., Calvo, R., Glozier, N., Christensen, H., & Harvey, S. B. (2017b). Designing smartphone mental health applications for emergency service workers. Occupational Medicine, 67(6), 425–428. doi: 10.1093/occmed/kqx056CrossRefGoogle Scholar
Fernandez, A., Salvador-Carulla, L., Choi, I., Calvo, R., Harvey, S. B., & Glozier, N. (2017). Development and validation of a prediction algorithm for the onset of common mental disorders in a working population. Australian & New Zealand Journal of Psychiatry, 52(1), 47–58. doi: 10.1177/0004867417704506CrossRefGoogle Scholar
Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287–298. doi: 10.1002/wps.20472CrossRefGoogle ScholarPubMed
Green, P., & MacLeod, C. J. (2016). SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods in Ecology and Evolution, 7(4), 493–498. doi: 10.1111/2041-210X.12504CrossRefGoogle Scholar
Harvey, S. B., Henderson, M., Lelliott, P., & Hotopf, M. (2009). Mental health and employment: Much work still to be done. British Journal of Psychiatry, 194(3), 201–203. doi: 194/3/201 [pii]10.1192/bjp.bp.108.055111CrossRefGoogle Scholar
Heber, E., Ebert, D. D., Lehr, D., Cuijpers, P., Berking, M., Nobis, S., & Riper, H. (2017). The benefit of web- and computer-based interventions for stress: A systematic review and meta-analysis. Journal of Medical Internet Research, 19(2), e32. doi: 10.2196/jmir.5774CrossRefGoogle ScholarPubMed
IBM Corp. (2015). IBM SPSS Statistics for Windows, Version 23.0. Armonk, NY: IBM Corp.Google Scholar
Kauer, S. D., Reid, S. C., Crooke, A. H. D., Khor, A., Hearps, S. J. C., Jorm, A. F., … Patton, G. (2012). Self-monitoring using mobile phones in the early stages of adolescent depression: Randomized controlled trial. Journal of Medical Internet Research 14(3), e67. doi:10.2196/jmir.1858.CrossRefGoogle ScholarPubMed
Kessler, R. C., Barber, C., Beck, A., Berglund, P., Cleary, P. D., McKenas, D., … Wang, P. (2003). The world health organization health and work performance questionnaire (HPQ). Journal of Occupational and Environmental Medicine, 45(2), 156–174. doi: 10.1097/01.jom.0000052967.43131.51CrossRefGoogle Scholar
Krieger, T., Zimmermann, J., Huffziger, S., Ubl, B., Diener, C., Kuehner, C., & Holtforth, M. G. (2014). Measuring depression with a well-being index: Further evidence for the validity of the WHO Well-Being Index (WHO-5) as a measure of the severity of depression. Journal of Affective Disorders, 156, 240–244. doi: 10.1016/j.jad.2013.12.015CrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9 – validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. doi: 10.1046/j.1525-1497.2001.016009606.xCrossRefGoogle ScholarPubMed
Kroenke, K., Spitzer, R. L., Williams, J. B., Monahan, P. O., & Löwe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146, 317–325.10.7326/0003-4819-146-5-200703060-00004CrossRefGoogle Scholar
Linardon, J., Cuijpers, P., Carlbring, P., Messer, M., & Fuller-Tyszkiewicz, M. (2019). The efficacy of app-supported smartphone interventions for mental health problems: A meta-analysis of randomized controlled trials. World Psychiatry, 18(3), 325–336. doi: 10.1002/wps.20673CrossRefGoogle ScholarPubMed
Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., … Lopez, A. D. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet (London, England), 380(9859), 2197–2223. doi: 10.1016/S0140-6736(12)61689-4CrossRefGoogle ScholarPubMed
Peters, D., Deady, M., Glozier, N., Harvey, S., & Calvo, R. A. (2018). Worker preferences for a mental health app within male-dominated industries: Participatory study. JMIR Mental Health, 5(2), e30. doi: 10.2196/mental.8999CrossRefGoogle ScholarPubMed
Proudfoot, J., Parker, G., Hadzi Pavlovic, D., Manicavasagar, V., Adler, E., & Whitton, A. (2010). Community attitudes to the appropriation of mobile phones for monitoring and managing depression, anxiety, and stress. Journal of Medical Internet Research, 12(5), e64. doi: 10.2196/jmir.1475CrossRefGoogle Scholar
Rathbone, A. L., Clarry, L., & Prescott, J. (2017). Assessing the efficacy of mobile health apps using the basic principles of cognitive behavioral therapy: Systematic review. Journal of Medical Internet Research, 19(11), e399. doi: 10.2196/jmir.8598CrossRefGoogle ScholarPubMed
Richards, D. A., Ekers, D., McMillan, D., Taylor, R. S., Byford, S., Warren, F. C., … Finning, K. (2016). Cost and outcome of behavioural activation versus cognitive behavioural therapy for depression (COBRA): A randomised, controlled, non-inferiority trial. The Lancet, 388(10047), 871–880. doi: 10.1016/S0140-6736(16)31140-0CrossRefGoogle ScholarPubMed
Richards, D., & Richardson, T. (2012). Computer-based psychological treatments for depression: A systematic review and meta-analysis. Clinical Psychology Review, 32(4), 329–342. doi: https://doi.org/10.1016/j.cpr.2012.02.004CrossRefGoogle ScholarPubMed
Rose, G. (1994). The strategy of preventive medicine. New York: Oxford University Press.Google Scholar
Solomon, D., Proudfoot, J., Clarke, J., & Christensen, H. (2015). e-CBT (myCompass), antidepressant medication, and face-to-face psychological treatment for depression in Australia: A cost-effectiveness comparison. Journal of Medical Internet Research, 17(11), e255. doi: 10.2196/jmir.4207CrossRefGoogle ScholarPubMed
Spitzer, R. L., Kroenke, K., & Williams, J. B. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaire. Journal of the American Medical Association, 282(18), 1737–1744.10.1001/jama.282.18.1737CrossRefGoogle ScholarPubMed
Stratton, E., Choi, I., Lampit, A., Calvo, R. A., Harvey, S. B., & Glozier, N. (2017). Effectiveness of E-health interventions for reducing mental health conditions in employees: A systematic review and meta-analysis. PLoS ONE, 12(12), e0189904. doi: https://doi.org/10.1371/journal.pone.0189904CrossRefGoogle Scholar
Tan, L., Wang, M.-J., Modini, M., Joyce, S., Mykletun, A., Christensen, H., & Harvey, S. B. (2014). Preventing the development of depression at work: A systematic review and meta-analysis of universal interventions in the workplace. BMC Medicine, 12, 74. doi: 10.1186/1741-7015-12-74CrossRefGoogle ScholarPubMed
Tennant, C. (2001). Work-related stress and depressive disorders. Journal of Psychosomatic Research, 51(5), 697–704. doi: https://doi.org/10.1016/S0022-3999(01)00255-0CrossRefGoogle ScholarPubMed
Torous, J., & Roberts, L. (2017). Needed innovation in digital health and smartphone applications for mental health: Transparency and trust. JAMA Psychiatry, 74(5), 437–438. doi: 10.1001/jamapsychiatry.2017.0262CrossRefGoogle ScholarPubMed
Van Hooff, M., McFarlane, A. C., Davies, C. E., Searle, A. K., Fairweather-Schmidt, A. K., Verhagen, A., … Hodson, S. E. (2014). The Australian defence force mental health prevalence and wellbeing study: Design and methods. European Journal of Psychotraumatology, 5, e23950. doi:10.3402/ejpt.v5.23950.CrossRefGoogle Scholar
van Zoonen, K., Buntrock, C., Ebert, D. D., Smit, F., Reynolds, F. C. III, Beekman, A. T. F., & Cuijpers, P. (2014). Preventing the onset of major depressive disorder: A meta-analytic review of psychological interventions. International Journal of Epidemiology, 43(2), 318–329. doi:10.1093/ije/dyt175.CrossRefGoogle ScholarPubMed
Wang, P. S., Simon, G. E., Avorn, J., Azocar, F., Ludman, E. J., McCulloch, J., … Kessler, R. C. (2007). Telephone screening, outreach, and care management for depressed workers and impact on clinical and work productivity outcomes: A randomized controlled trial. Journal of the American Medical Association, 298(12), 1401–1411. doi: 10.1001/jama.298.12.1401CrossRefGoogle ScholarPubMed
World Health Organization. (2017). Depression and other common mental disorders: global health estimates. Retrieved from http://www.webcitation.org/6ytV1RlIN.Google Scholar
Supplementary material: File

Deady et al. Supplementary Materials

Deady et al. Supplementary Materials 1

Download Deady et al. Supplementary Materials(File)
File 13.3 KB
Supplementary material: File

Deady et al. Supplementary Materials

Deady et al. Supplementary Materials 2

Download Deady et al. Supplementary Materials(File)
File 60.9 KB
Supplementary material: File

Deady et al. Supplementary Materials

Deady et al. Supplementary Materials 3

Download Deady et al. Supplementary Materials(File)
File 44.5 KB
Supplementary material: File

Deady et al. Supplementary Materials

Deady et al. Supplementary Materials 4

Download Deady et al. Supplementary Materials(File)
File 62.5 KB