Adherence remains a challenge for deploying remote digital health solutions, as their utility often depends upon obtaining sufficient data over a period of time. Understanding adherence patterns could enable patient-tailored interventions that boost long-term adherence.

Participants performed sensor-based tests, using a precursor to Floodlight™ MS, assessing their cognition (weekly), upper extremity function (daily), gait and balance (daily); and passive monitoring of gait (≥4 hours daily) for up to 3 years. Unsupervised Hierarchical Agglomerative Clustering was used to identify five adherence clusters. The drop-out date (the first day of the first period of 4 weeks of inactivity) was predicted via a linear model that used the first 2 months of adherence data as predictors.

Adherence during the first 2 months of using Floodlight technology can predict the time of inactivity of participants. Understanding adherence patterns may enable early, adherence-boosting interventions that focus on participants at risk of dropping out of remotely administered digital health studies, leading to an increase in data collection and improved engagement of PwPMS.