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Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence
Samia Shahnawaz1, Rhyann Clarke1, Suzanne Bakken2
1Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Extended Abstracts on Human Factors in Computing Systems. CHI Conference
|April 20, 2026
Summary
Consistent mobile Health (mHealth) app use, especially in the evening, improves adherence for chronic pelvic pain disorder (CPPD) patients. Habitual tracking is key for better data quality and outcomes.
Area of Science:
- Digital Health
- Chronic Disease Management
- Patient Adherence
Background:
- Understanding mobile Health (mHealth) user engagement is crucial for chronic disease management.
- mHealth data offers unique insights into under-documented conditions.
- User engagement patterns and adherence in mHealth are not well understood.
Purpose of the Study:
- To investigate user engagement patterns in mHealth.
- To determine the association between engagement and adherence in chronic disease populations.
- To identify predictors of adherence and consistency in app usage.
Main Methods:
- Analysis of 13,997 days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 controls.
- Daily symptom tracking via a research app over 90 days.
- Mixed-effects regression to examine predictors of adherence and engagement consistency, moderated by CPPD burden and temporal accessibility.
Main Results:
- Later-day engagement strongly predicted user consistency, irrespective of pain.
- Habitual evening tracking correlated with better adherence in CPPD patients compared to controls.
- Engagement consistency was the most significant predictor of adherence.
Conclusions:
- Prioritizing user habits and preferences over fixed-time assessments can enhance temporal accessibility.
- Improving temporal accessibility can lead to better data completeness in mHealth studies.
- Findings suggest strategies to optimize mHealth engagement for improved clinical outcomes.
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