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Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices

Author

Listed:
  • Tony Robinson

    (School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK)

  • Joan Condell

    (School of Computing, Engineering, and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK)

  • Elaine Ramsey

    (Department of Global Business and Enterprise, Ulster University, Magee Campus, Derry/Londonderry BT48 7JL, UK)

  • Gerard Leavey

    (The Bamford Centre for Mental Health and Wellbeing, School of Psychology, Ulster University, Coleraine Campus, Cromore Rd., Coleraine BT52 1SA, UK)

Abstract

Rationale: Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders. Aims: This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system. Methodology: a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018–2022, focusing primarily on mental health management using technology. Results: We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

Suggested Citation

  • Tony Robinson & Joan Condell & Elaine Ramsey & Gerard Leavey, 2023. "Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices," IJERPH, MDPI, vol. 20(3), pages 1-22, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2636-:d:1054183
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    References listed on IDEAS

    as
    1. McDaid, David & Park, A-La & Wahlbeck, Kristian, 2019. "The economic case for the prevention of mental illness," LSE Research Online Documents on Economics 100054, London School of Economics and Political Science, LSE Library.
    2. Sarah J Iribarren & Kenrick Cato & Louise Falzon & Patricia W Stone, 2017. "What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-20, February.
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