The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

Valeria de Angel, Fadekemi Adeleye, Yuezhou Zhang, Nicholas Cummins, Sara Munir, Serena Lewis, Estela Laporta Puyal, Faith Matcham, Shaoxiong Sun, Amos A. Folarin, Yatharth Ranjan, Pauline Conde, Zulqarnain Rashid, Richard Dobson, Matthew Hotopf

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)

Abstract

Background: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. Objective: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. Methods: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. Results: The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=−2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=−0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. Conclusions: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

Original languageEnglish
Article numbere42866
JournalJMIR mental health
Volume10
DOIs
Publication statusPublished - 24 Jan 2023

Bibliographical note

Funding Information:
RD was supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, United Kingdom; (2) Health Data Research United Kingdom, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) The BigData@Heart consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement number 116074 (this joint undertaking receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations and is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and European Society of Cardiology) (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare; and (7) the NIHR Applied Research Collaboration South London (NIHR Applied Research Collaboration South London) at King’s College Hospital NHS Foundation Trust.

Funding Information:
MH is the principal investigator of the Remote Assessment of Disease and Relapse–Central Nervous System program, a precompetitive public-private partnership funded by the Innovative Medicines Initiative and the European Federation of Pharmaceutical Industries and Associations.

Funding Information:
This study represents independent research funded by the National Institute for Health and Care Research Biomedical Research Centre in South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, National Institute for Health and Care Research, or Department of Health and Social Care. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.

Keywords

  • anxiety
  • depression
  • digital health
  • digital phenotyping
  • mHealth
  • mobile health
  • mobile phone
  • passive sensing
  • smartphone
  • wearable devices

ASJC Scopus subject areas

  • Psychiatry and Mental health

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