ArticleMedicine and science in sports and exercise2021
The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.
Article in Medicine and science in sports and exercise, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- A Standardised Core Outcome Set for Measurement and Reporting Sedentary Behaviour Interventional Research: The CROSBI Consensus Study.International journal of environmental research and public health · 2022Pooled it
- Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors.The international journal of behavioral nutrition and physical activity · 2024Trial
- A systematic review of prolonged posture monitoring with accelerometer-based wearable sensors.PLOS digital health · 2026Article
- Associations of Accelerometer-Measured Physical Activity and Sedentary Behavior With Incident Clinical Osteoporotic Fractures Among Older Women: The Objective Physical Activity and Cardiovascular Health Study.Journal of aging and physical activity · 2026Article
- Validating the Longitudinal Assessment of Sedentary Behavior in a Randomized Controlled Trial via a Deep Learning Algorithm.Journal for the measurement of physical behaviour · 2026Article
- Prospective associations between accelerometer-measured physical activity, sedentary behavior, and healthy longevity: the Women's Health Accelerometry Collaboration.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Sitting time and risk of cancer incidence and cancer mortality in postmenopausal women: the Women's Health Accelerometry Collaboration.Cancer causes & control : CCC · 2025Article
- Movement- and Posture-based Measures of Sedentary Patterns and Associations with Metabolic Syndrome in Hispanic/Latino and non-Hispanic Adults.Journal of racial and ethnic health disparities · 2025Article
- Sedentary behaviors and venous thromboembolism risk among older women: the Objective Physical Activity and Cardiovascular Health study.Journal of thrombosis and haemostasis : JTH · 2025Article
- Accelerometer-measured physical activity, sedentary behavior, and mortality among cancer survivors: the Women's Health Accelerometry Collaboration.JNCI cancer spectrum · 2025Article
- Machine learning applications in the analysis of sedentary behavior and associated health risks.Frontiers in artificial intelligence · 2025Review
- Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.BMC medical research methodology · 2024Article
- Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study.Statistics in biosciences · 2024Article
- Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method.Sensors (Basel, Switzerland) · 2024Article
- Prospective Associations of Accelerometer-Measured Machine-Learned Sedentary Behavior With Death Among Older Women: The OPACH Study.Journal of the American Heart Association · 2024Article
- Machine learning in physical activity, sedentary, and sleep behavior research.Journal of activity, sedentary and sleep behaviors · 2024Review
- Measuring nurses' on-shift physical activity and sedentary time by accelerometry or heart rate monitoring: a descriptive case study illustrating the importance of context.Journal of activity, sedentary and sleep behaviors · 2023Article
- Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE).International journal of obesity (2005) · 2023Article
- Associations of Accelerometer-Measured Physical Activity and Sedentary Time With All-Cause Mortality by Genetic Predisposition for Longevity.Journal of aging and physical activity · 2023Article
- Maximizing the Utility and Comparability of Accelerometer Data from Large-Scale Epidemiologic Studies.Journal for the measurement of physical behaviour · 2023Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
introductionSitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.
methodsCHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).
resultsFor minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).
conclusionCHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.