UC San Diego

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 Convolutional Neural Network Hip Accelerometer Posture (CHAP), 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 nonsitting and estimating intervention-related changes in SB. Methods : CHAP was applied to 30-Hz triaxial hip-worn ActiGraph-GT3X+ (AG) accelerometer data from free-living postmenopausal women who are overweight (≥55 years) 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 with 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 (standard error, p values) intervention effect estimates (mean change, intervention minus controls) from generalized estimating equation models for (a) total sedentary time were AP 65.1 min/day (16.2, p < .001), CHAP 38.2 min/day (15.4, p = .013), and AG100 0.28 min/day (13.9, p = .98; Reduce Sitting); (b) breaks from SB were AP 22/day (4.3, p < .001), CHAP 4/day (1.5, p = .003), and AG100 <1/day (2.1, p = .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.