CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+

Abstract

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

Feasibility of a Health Coach Intervention to Reduce Sitting Time and Improve Physical Functioning Among Breast Cancer Survivors: Pilot Intervention Study

“…Event files from the activPAL were extracted via the CREA classification algorithm (version 8; PALanalysis), which was set to require ≥4 second for a new posture to be registered and generated sleeping time for removal from analysis. Minutes spent in various sedentary behaviors (ie, sitting, standing, sit to stand transitions, and stepping time) were derived from continuously recorded data [ 38 , 39 ]. The activPAL has been validated with good reliability and validity [ 40 - 42 ] for measuring sedentary behavior and stepping pattern in community-dwelling older adults [ 43 ].…”

“…RenderX [38,39]. The activPAL has been validated with good reliability and validity [40][41][42] for measuring sedentary behavior and stepping pattern in community-dwelling older adults [43].…”

Measurement of Sedentary Behavior—The Outcomes of the Angle for Posture Estimation (APE) Method

“…The recent developments in the field of hip-worn accelerometer algorithms have made it possible to quantify SB as per the proper definition. Both the angle for postural estimation (APE) method [20] and convolutional neural network hip accelerometer posture (CHAP) method have shown over 90% accuracy in classifying body postures [21,22]. The APE method is based on two concepts: the Earth's gravity vector is constant, and the body posture during walking is upright.…”

Performance of Different Accelerometry-Based Metrics to Estimate Oxygen Consumption during Track and Treadmill Locomotion over a Wide Intensity Range

“…Chastin et al concluded recently that the data from hip-worn accelerometry can provide accurate and meaningful estimates of PA [2]. Further, recent advances in data analysis have made it possible to quantify also the sedentary time spent in standing, sitting, and reclining postures [3,4].…”