JMIR Publications

ABSTRACT

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.