Dynamic Indicators of Adherence and Retention in Adults Using a Digital Mental Health App: Longitudinal Observational Analysis From the Brighten Study
Bibliographic record
Abstract
Background: Making optimal use of mobile health technologies requires the validation of digital biomarkers, which in turn demands high levels of participant adherence and retention. However, current remote digital health studies have high attrition rates and low participant adherence, which may introduce bias and limit the generalizability of their findings. Objective: This study aimed to identify longitudinal indicators of participant retention and adherence, which may serve to develop strategies to improve data collection in digital health studies and improve understanding of how study cohorts are shaped by participant withdrawal and nonadherence. Methods: We performed secondary analyses on the Brighten study, which consisted of 2 remote, smartphone-based randomized controlled trials evaluating mobile apps for depression treatment, enrolling 2193 participants in total. Participants were asked, after baseline assessment, to complete 7 digital questionnaires regularly. We assessed adherence to digital questionnaires, engagement (postbaseline participation), and retention rates (the proportion of participants who continued completing questionnaires over time) as outcomes. We investigated the relationship between these outcomes and both static measures (eg, demographics and average questionnaire scores) and dynamic measures (eg, changes in questionnaire scores over time). Results: The study included 2201 participants, of whom 1093 completed at least 1 nonbaseline questionnaire, with a median completion rate of 37.6% (IQR 15.5%-67.9%). We found significantly higher adherence rates in participants who were less depressed on average over the course of the study (t752=-5.63; P<.001) and in those who perceived clinical improvement (t744=3.78; P=.001). There were significant demographic differences in adherence and engagement, including differences by gender, race, education, income, and income satisfaction. Participants who were more depressed at baseline were more likely to withdraw before completing any nonbaseline questionnaire (t1917=-2.53; P=.01). However, participants who showed improvement in depressive symptoms during the study showed better adherence (Mann-Whitney U=127,084; P<.001) and retention (hazard ratio 0.78, 95% CI 0.67-0.91; P=.002), despite showing greater depressive symptoms at baseline. Conclusions: We show that participants' clinical trajectory of depressive symptoms, as well as their perception of improvement, are important indicators of engagement, adherence, and retention. Expanding knowledge regarding these longitudinal indicators may improve interpretation of outcomes and help build strategies to improve retention and adherence in future clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".