BACKGROUND
Digital health studies using electronic patient reported outcomes (ePROs) and wearables bring new challenges, including activation of participants to consistently provide trial data.
OBJECTIVE
To characterize engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis (RA) enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study.
METHODS
Participants were invited to join this app-based study, which included 14-day run-in and 84-day main study. In the run-in period, data was collected via the ArthritisPower mobile application (app). Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants reminding them to complete app input or regularly wear and sync devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, text, and phone to resolve adherence issues per a priori rules in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data more than 70% of 84 days (daily ePRO, ≥ 80% daily smartwatch data), or at least 9 of 12 weeks (weekly ePRO).
RESULTS
Of 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study, 87% of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57% did so for daily ePRO. For smartwatch data, 82% of participants adhered to the protocol-specified data collection. A total of 147 (53%) participants met composite adherence.
CONCLUSIONS
Compared with other digital health RA studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via mobile app and wearables, and gives participants time to acclimate to study requirements. Automated or manual prompts may be necessary to optimize adherence, and adherence varies by data collection type.
INTERNATIONAL REGISTERED REPORT
RR2-10.2196/14665