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

doi:10.2196/42866

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

JMIR Ment Health. 2023 Jan 24:10:e42866. doi: 10.2196/42866.

Authors

Valeria de Angel^{1

2}, Fadekemi Adeleye³, Yuezhou Zhang⁴, Nicholas Cummins⁴, Sara Munir⁵, Serena Lewis^{1

6}, Estela Laporta Puyal^{7

8}, Faith Matcham^{1

9}, Shaoxiong Sun⁴, Amos A Folarin^{2

4

10

11

12}, Yatharth Ranjan¹³, Pauline Conde¹³, Zulqarnain Rashid¹³, Richard Dobson^{1

2}, Matthew Hotopf^{1

2}

Affiliations

¹ Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
² NIHR Maudsley Biomedical Research Centre,  South London and Maudsley NHS Foundation Trust, London, United Kingdom.
³ Department of Psychology, King's College London, London, United Kingdom.
⁴ Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
⁵ Lewisham Talking Therapies, South London and Maudsley NHS Foundation Trust, London, United Kingdom.
⁶ Department of Psychology, University of Bath, Bath, United Kingdom.
⁷ Biomedical Signal Interpretation and Computational Simulation Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
⁸ Centro de Investigación Biomédica en Red of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
⁹ School of Psychology, University of Sussex, Brighton, United Kingdom.
¹⁰ Institute of Health Informatics, University College London, London, United Kingdom.
¹¹ Health Data Research UK London, University College London, London, United Kingdom.
¹² NIHR Biomedical Research Centre at University College London Hospitals, University College London Hospitals NHS Foundation Trust, London, United Kingdom.
¹³ Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

PMID: 36692937
PMCID: PMC9906314
DOI: 10.2196/42866

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 (t_56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t_50.4=-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.

Keywords: anxiety; depression; digital health; digital phenotyping; mHealth; mobile health; mobile phone; passive sensing; smartphone; wearable devices.

©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. Originally published in JMIR Mental Health (https://mental.jmir.org), 24.01.2023.

Abstract

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