Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jul 11, 2025
Date Accepted: Nov 11, 2025
Adherence to Accelerometer Use Correlates with Improved Functional Capacity in Older Adults Undergoing mHealth Cardiac Rehabilitation
ABSTRACT
Background:
Wearable accelerometers are commonly used in mHealth-enabled cardiac rehabilitation (mHealth-CR), but the role of adherence to accelerometer use on clinical outcomes is understudied.
Objective:
In this context, we developed an artificial intelligence (AI) framework to quantify adherence to accelerometer use and its association with functional capacity improvements in older adults undergoing mHealth-CR.
Methods:
We analyzed data from the RESILIENT trial, the largest randomized clinical trial to date comparing mHealth-CR versus usual care in older adults (age ≥65 years). Intervention arm participants were instructed to wear a Fitbit accelerometer for the 3-month study duration. Adherence to accelerometer use was quantified as overall adherence (percentage of days worn) and via k-means clustering AI-derived measures and compared with changes in 6-minute walk distance (6-MWD), adjusted for demographic and clinical covariates.
Results:
Among 271 participants (mean age 71±8 years, 73% male), accelerometers were worn an average 76 days [95% CI: 73,78 days] over 3 months. Adjusted analyses showed a weak association between days of wear and improvement in 6-MWD, with every 30 additional days associated with an 11-meter improvement (p=0.08). Our AI framework identified 8 distinct phenotypes of accelerometry adherence over the 3-month intervention. In adjusted analysis, the three highest AI-derived accelerometry adherence phenotypes trended towards a 20-meter [95% CI: -2,41 meters; p=0.07] higher improvement in 6-MWD than the other five phenotypes.
Conclusions:
Adherence to accelerometer use showed a weak association with functional capacity improvement in older adults undergoing mHealth-CR. Our AI-derived accelerometry adherence phenotypes can enable personalized mHealth-CR regimens for optimal patient benefit.
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