JMIR Publications

ABSTRACT

Background:

Declining medication adherence remains a critical healthcare issue, often assessed through unreliable self-reporting methods. Wearable devices (WDs) may offer an objective means to improve adherence monitoring by continuously recording physiological and activity data.

Objective:

This study aimed to develop and internally validate personalized predictive models for identifying missed medication doses using WD-derived data.

Methods:

A 30-day prospective observational study was conducted with 8 participants who wore Apple Watches and used a dedicated iOS application. The application collected demographics, medication details, psychological factors, mealtimes, and daily missed dose events. WDs recorded time-series data (activity, heart rate, sleep) at 3-minute intervals. Data were aggregated into 1-hour segments, and lag (6- and 12-hour) as well as rolling (24-hour) features were generated. Light Gradient Boosting Machine models were constructed for each individual’s dosing regimen if the missed dose rate exceeded 20%. Two modeling approaches were compared: a Group Cross-Validation (CV) model that grouped data by day to avoid data leakage from rolling features, and a Non-rolling Feature model that excluded rolling features and used leave-one-out CV. F1 score, accuracy, recall, and precision were assessed.

Results:

Of the 15 enrolled participants, 8 completed the study; 4 had a missed dose rate above 20%. In these 4 individuals, the Group CV model achieved F1 scores of 0.435 to 0.902, accuracy ranging from 0.711 to 0.911, recall from 0.278 to 0.822, and precision of 1.000 for the most robust regimens. The Non-rolling Feature model yielded F1 scores of 0.667 to 0.910, accuracy ranging from 0.800 to 0.906, recall from 0.500 to 0.835, and precision of 1.000. Morning dosing regimens generally showed higher predictive performance than evening or afternoon. Time-series features, particularly those reflecting 6-, 12-, and 24-hour patterns, emerged as key predictors, indicating that physiological and lifestyle variations prior to dosing strongly influenced missed dose events.

Conclusions:

Personalized predictive models using WD-derived data demonstrated high precision for detecting missed medication doses, especially in morning and evening regimens. These findings underscore the feasibility of employing objective physiological and activity data to forecast nonadherence and highlight the role of lifestyle patterns in determining missed doses. Future work should involve larger populations for external validation, strategies to improve recall—especially for clinically critical medications—and careful consideration of real-world implementation challenges.