Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants

TL;DR

This study analyzes retention factors in over 100,000 participants across eight remote digital health studies, identifying key demographic and behavioral predictors of participant retention and highlighting challenges in achieving representative samples.

Contribution

It provides a comprehensive evaluation of retention indicators and demographic patterns, informing strategies to improve participant engagement in digital health research.

Findings

Clinician referral increases median retention by 40 days

Compensation extends retention by 22 days

Distinct app usage patterns correlate with demographics

Abstract

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of participants over a span of a few months. Unfortunately, these studies are hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of findings from these studies. We report the challenges in retention and recruitment in eight remote digital health studies comprising over 100,000 participants who participated for more than 850,000 days, completing close to 3.5 million remote health evaluations. Survival modeling surfaced several factors significantly associated(P < 1e-16) with increase in median retention time i) Clinician referral(increase of 40 days), ii) Effect of compensation (22 days), iii) Clinical conditions of interest to the study (7 days) and iv) Older adults(4 days). Additionally, four distinct patterns of daily app usage behavior that were also associated(P < 1e-10) with participant demographics were identified. Most studies were not able to recruit a representative sample, either demographically or regionally. Combined together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.