@Article{info:doi/10.2196/72113,
author="Iino, Haru
and Kizaki, Hayato
and Imai, Shungo
and Hori, Satoko",
title="Construction of Personalized Predictive Models for Missed Medication Doses Using Wearable Device Data: Prospective Observational Study",
journal="JMIR Form Res",
year="2025",
month="Jun",
day="24",
volume="9",
pages="e72113",
keywords="personalized predictive models; missed medication doses; wearable device data; Light Gradient Boosting Machine; LightGBM",
abstract="Background: Declining medication adherence remains a critical health care 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, utilizing objective physiological and activity data from WDs, for identifying missed medication doses. Methods: A 30-day prospective observational study was conducted with 8 participants who wore Apple Watches and used a dedicated iOS app. The app collected demographics, medication details, psychological factors, mealtimes, and daily missed dose events. WDs recorded time-series data (ie, activity, heart rate, sleep) at 3-minute intervals. Data were aggregated into 1-hour segments, and lag (6 and 12 h) as well as rolling (24 h) 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 nonrolling feature model that excluded rolling features and used leave-one-out CV. F1-score, accuracy, recall, and precision were assessed between the 2 models. 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, with accuracy ranging from 0.711 to 0.911, recall from 0.278 to 0.822, and a precision of 1.000 for the most robust regimens. The nonrolling feature model yielded F1-scores of 0.667 to 0.910, with accuracy ranging from 0.800 to 0.906, recall from 0.500 to 0.835, and a 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 continuous, objective physiological and activity data from WDs to forecast nonadherence events. Although the sample size was limited, restricting the generalizability of the results, this study demonstrates the potential of WD-based personalized prediction of medication adherence. 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. ",
issn="2561-326X",
doi="10.2196/72113",
url="https://formative.jmir.org/2025/1/e72113",
url="https://doi.org/10.2196/72113"
}