Validating the Longitudinal Assessment of Sedentary Behavior in a Randomized Controlled Trial via a Deep Learning Algorithm.

Document Type

Article

Publication Date

2026

Identifier

DOI: 10.1123/jmpb.2025-0086; PMCID: PMC13641217

Abstract

BACKGROUND: Sedentary behavior (SB) is a risk factor for cardiometabolic disease. Existing cut-points of hip-worn accelerometers to quantify SB underestimate sedentary bout durations by ignoring posture. To address this, we previously developed CHAP, Convolutional Neural Network Hip Accelerometer Posture, a deep learning algorithm that predicts sitting and breaks from sitting. In this study, we applied CHAP to a randomized controlled trial to externally validate its accuracy for classifying sitting versus non-sitting and estimating intervention-related changes in SB.

METHODS: CHAP was applied to 30Hz-tri-axial hip-worn ActiGraph-GT3X+ (AG) accelerometer data from free-living postmenopausal women who are overweight (≥55-yrs) in the Rise for Health RCT(n=406). CHAP-predicted SB accuracy (e.g., sensitivity) and daily pattern metrics (e.g., total sedentary time) were compared to a ground-truth device activPAL (AP) and standard cut-point method (AG100), at two timepoints for intervention arms-Healthy Living (control), Reduce Sitting, and Increase Transitions.

RESULTS: CHAP's SB accuracy metrics were above 84.8%. Mean (SE, p) intervention effect estimates (mean change, intervention minus controls) from generalized estimating equation models for (I) total sedentary time were AP 65.1 min/day (16.2, p< 0.001), CHAP 38.2 min/day (15.4, p=0.013), and AG100 0.28 min/day (13.9, p=0.98) (Reduce Sitting); (II) breaks from SB were AP 22/day (4.3, p< 0.001), CHAP 4/day (1.5, p=0.003), and AG100 < 1/day (2.1, p=0.860) (Increase Transitions).

CONCLUSIONS: This study validated CHAP on an independent cohort. CHAP exhibited high prediction accuracy, and although less sensitive than AP, CHAP detected changes in SB more effectively than AG100. Thus, CHAP could make a strong contribution to SB intervention research.

Journal Title

J Meas Phys Behav

Volume

9

Issue

1

PubMed ID

42840830

Keywords

accelerometer data; free-living; healthy aging; intervention-related change; machine learning

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