Abstract 21029: Achieving High Retention in Mobile Health Research Using Design Principles Adopted From Widely Popular Consumer Mobile Apps
Bibliographic record
Abstract
Introduction: While mHealth platforms can enable rapid participant recruitment, the first 5 ResearchKit apps retained less than 10% of daily participants after the first 90 days, whereas well-optimized consumer mobile apps like Instagram and Twitter retain 31% and 48%, respectively. We proposed to apply design principles from the world of consumer internet to achieve high engagement and retention in a mHealth study. Methods: We enrolled 14,011 users of Cardiogram for Apple Watch app into the Health eHeart Study, an IRB-approved study at UCSF. We applied 3 key design principles to drive engagement and retention. First, give valuable insights back to user—e.g., notify users of abnormal heart rate spikes, and show when exercise caused a lower resting heart rate trend—using Gottman’s ratio: for every negative insight, give 5 positive ones. Second, minimize latency so users’ data updates many times per day. Third, use simple user interfaces that easily visualize trends and provide insights in small digestible formats. Results: Mean age was 42.3 ± 12.1, 31% were women. Seven days after app install, 64% of participants were active; 63% were active after 30 days, and 54% after 90 days. Retention was consistent across age groups—day 90 retention was 52% for 20-40 y.o., 55% for 40-60, and 49% for above 60. Refreshing data as frequently as possible had the highest impact on user engagement—in A/B testing, where heart rate visualizations were updated less frequently, we saw 20.9% drop in daily active users (51951 to 41094) within 7 days. The ratio of daily to monthly active users, a key measure of engagement, is 69.8% in our study, while average mHealth app is 8%. Conclusions: By applying design techniques from consumer mobile apps, we achieved day 90 retention 5x higher than the best ResearchKit app, showing that mHealth studies can retain large cohorts of participants and collect unique ambulatory health data with high engagement, improving the impact of mobile health interventions.
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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 | 0.041 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".