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.
BACKGROUND Remote Measurement Technologies (RMTs) such as smartphones and wearables, can help improve treatment for depression by providing more objective, continuous, and ecologically valid insight into mood and behavior. Engagement with RMTs is varied and highly context-dependent, yet 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 two 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 engagement differences. METHODS Sixty-six people undergoing psychological therapy for depression were followed up for 7 months. Active data in the form of weekly questionnaires, speech, and cognitive tasks were generated, and passive data were gathered from smartphone sensors and a Fitbit wearable device RESULTS Overall study retention was 60%. Higher-intensity treatment and higher baseline anxiety were associated with increased attrition, but depression severity was not. A trend towards significance was found for the association between longer treatments and increased attrition. Data availability was higher for active data than passive data but declined at a sharper rate (90% to 30% drop in 7 months). Within passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability compared to smartphone-based data, which remained stable at the 20-40% range throughout. Missing data was more prevalent in GPS location data, followed by Bluetooth, than accelerometry. For active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. Participants in treatment provided less Fitbit data but higher active data during treatment 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 as well as clinical characteristics like 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 healthcare settings, as well as for the generalisability and accuracy of the data collected by these methods, feature construction, and the appropriateness of their use in the long-term.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.