Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

Abstract

Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants’ study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants’ age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.

From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression

“…Focusing on participants with MDD, Matcham, et al (2022) investigated whether depressed mood was associated with data availability, not identifying a difference in data availability between those with no or mild depressive symptoms and more severe symptoms. In a later study from this consortium, Zhang, et al, 2023 also did not find a significant impact of baseline depression score on participant retention for passive data, however did now find differences in data availability.…”

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

“…The most appropriate thresholds for subgroup analysis would necessitate further investigations. Fifth, the RADAR-MDD study's use of an open enrollment strategy [48,72] has resulted in site-speci c variations in age, depression severity, and weather conditions (Table 1 and Fig. 2).…”

Feasibility Study on Menstrual Cycles With Fitbit Device (FEMFIT): Prospective Observational Cohort Study

“…However, none of these studies used wearable devices to monitor daily activity levels, and there is a gap in studies using wearable data to examine sleep patterns across the menstrual cycle. Recognizing that participant retention is a significant challenge in digital cohort studies [ 19 , 40 ], our feasibility study was designed to primarily assess the effectiveness of study participation and adherence to the study protocol, highlighting challenges and opportunities for future large-scale studies in women’s health. Secondarily, we focused on potential biological outcomes.…”

“…Frequent and continuous data collection can provide an understanding of symptom variability, which is likely to be an important contributor to variability in treatment response [ 17 ]. However, traditional clinical trials to date have relied on infrequent in-person assessments and subjective retrospective data, failing to capture the daily changes in physical and mental well-being that occur over the course of the menstrual cycle [ 18 , 19 ]. In this regard, commercially available menstrual tracker apps offer new opportunities for research.…”

“…Commercially available wrist-worn sensor devices, such as the Fitbit Inspire 2 device, are thereby not only much more affordable than research-grade sensor devices, but can be nearly as accurate [ 38 , 39 ]. However, real-world data collection studies conducted exclusively in the home can present difficulties in terms of retention and adherence to the study protocol [ 19 , 40 ]. To explore the feasibility of consistently collecting wearable and questionnaire data across multiple menstrual cycles, we conducted a 12-week feasibility study with 42 participants.…”