Adherence to Accelerometer Use in Older Adults Undergoing mHealth Cardiac Rehabilitation: Secondary Analysis of a Randomized Clinical Trial

Sep 30, 2026

Uploaded by Sarah

Wearable accelerometers record physical activity metrics and are widely used in mobile health–enabled cardiac rehabilitation (mHealth-CR), yet how adherence to device use relates to clinical improvements remains insufficiently studied. This study applies an AI clustering framework to characterize distinct behavioral adherence phenotypes to accelerometer use and evaluates their relationship with functional capacity changes in older adults. Data from the RESILIENT randomized trial were analyzed, using k-means clustering and adjusted comparisons of 6-minute walk distance outcomes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Abstract

Background

Objective

Methods

Results

Conclusions

Introduction

Context: mHealth-CR and wearable accelerometers

Prior evidence from randomized trials

JOURNAL OF MEDICAL INTERNET RESEARCH Barua et al Original Paper Adherence to Accelerometer Use in Older Adults Undergoing mHealth Cardiac Rehabilitation: Secondary Analysis of a Randomized Clinical Trial | Souptik Barua1, PhD; Dhairya Upadhyay1, MS; Stephanie Pena2, MS; Riley McConnell2, MPH; Ashwini Varghese2, MPH; Samrachana Adhikari3, PhD; Erik LeRoy4, MS; Antoinette Schoenthaler3, EdD; John A Dodson2,3, MD, MPH | | --- | | 1Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY, United States 2Leon H Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, United States 3Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
4Rusk Department of Rehabilitation Medicine, New York University Grossman School of Medicine, New York, NY, United States
Corresponding Author:
John A Dodson, MD, MPH
Leon H Charney Division of Cardiology Department of Medicine
New York University Grossman School of Medicine
227 East 30th Street New York, NY, 10016 United States
Phone: 1 212 263 7751
Email: [resilient@nyulangone.org](resilient@nyulangone.org)
Abstract | Background: Wearable accelerometers, which continuously record physical activity metrics, are commonly used in mobile health–enabled cardiac rehabilitation (mHealth-CR). The association between adherence to accelerometer use during mHealth-CR and improvement in clinical outcomes, such as functional capacity, is understudied. The emergence of artificial intelligence (AI) technology provides novel opportunities to investigate accelerometry use patterns in relation to mHealth-CR outcomes. Objective: In this study, we sought to use an AI clustering framework to identify distinct behavioral phenotypes of adherence to accelerometer use. We then aimed to quantify the association of these adherence phenotypes with functional capacity improvements in older adults undergoing mHealth-CR. Methods: We analyzed data from the RESILIENT (Rehabilitation at Home Using Mobile Health in Older Adults After Hospitalization for Ischemic Heart Disease) trial, the largest randomized clinical study to date comparing mHealth-CR versus usual care in older adults (aged ≥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) 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 with a mean age of 71 years (SD 8), of whom 198 (73%) were male, accelerometers were worn for an average of 76 days (95% confidence limits 73,78) 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=.08) . Our k-means clustering framework identified adherence phenotypes at two resolutions: low resolution (k=2 clusters) and high resolution (k=8 clusters). The consistently high adherence cluster trended toward a 24.6-meter improvement in 6-MWD compared to the low and declining adherence clusters (n=39; 95% CI 0.7-49.9; P=.06) . The 8-cluster phenotyping revealed a richer set of adherence patterns, with the consistently high adherence cluster in this analysis having a 38.5-meter (95% CI 2.2-74.7; P=.04) improvement in 6-MWD than the low adherence cluster, as well as greater average daily steps over the 3-month intervention (mean 7518, SD 3415 vs mean 4800, SD 2920 steps; P=.008) . Conclusions: A time-series AI clustering framework identified a range of behavioral phenotypes representing different degrees of adherence to accelerometer use. Regression analysis identified a weak association between the higher adherence phe

FAQs

What was the main objective of this study?

To use an AI clustering framework to identify distinct behavioral adherence phenotypes to accelerometer use during mHealth-CR and quantify their association with functional capacity improvements in older adults.

How was accelerometer adherence measured?

Adherence was quantified as overall adherence, defined as the percentage of days the device was worn, and analyzed using k-means clustering AI-derived measures.

Which outcome was used to assess functional capacity changes?

Functional capacity was assessed using changes in 6-minute walk distance (6-MWD), with analyses adjusted for demographic and clinical covariates.

Sep 30, 2026

Uploaded by Sarah