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

de Angel, Valeria; Adeleye, Fadekemi; Zhang, Yuezhou; Cummins, Nicholas; Munir, Sara; Lewis, Serena; Laporta Puyal, Estela; ... Hotopf, Matthew; + view all (2023) The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement. JMIR Mental Health , 10 , Article e42866. 10.2196/42866. Green open access

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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.

Type: Article
Title: The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement
Location: Canada
Open access status: An open access version is available from UCL Discovery
DOI: 10.2196/42866
Publisher version: https://doi.org/10.2196/42866
Language: English
Additional information: © 2023 the Authors . This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/).
Keywords: anxiety, depression, digital health, digital phenotyping, mHealth, mobile health, mobile phone, passive sensing, smartphone, wearable devices
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10164086
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