ArticleMedicine and science in sports and exercise2017
Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children.
Article in Medicine and science in sports and exercise, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Measurement tools for sedentary behavior in the Arabic-speaking countries in MENA region: a systematic review of validated instruments from the BRIDGE project.Frontiers in public health · 2026Pooled it
- A Head-to-Head Comparison of Three Literature Algorithms for Physical Activity Endpoints from a Wrist Accelerometer in Free-Living Conditions.Sensors (Basel, Switzerland) · 2026Article
- The effectiveness of JU:MP a whole system approach to improve physical activity of children aged 5 to 11 years living in multi-ethnic and socio-economically deprived communities: a non-randomised controlled trial.BMC public health · 2025Article
- Effects of a physical activity-enhanced curriculum on increasing physical activity and improving physical fitness in preschoolers: Study protocol for a cluster randomized controlled trial (KID-FIT study).Journal of exercise science and fitness · 2025Article
- Physical activity and cardiac function in patients with Duchenne muscular dystrophy.Cardiology in the young · 2025Article
- The Performance of Ankle- and Waist-Based Accelerometry in Quantifying Physical Activity Intensity Among 6- to 24-Month-Year-Olds: A Semistructured Laboratory Study.Child: care, health and development · 2025Article
- Supervised Learning of Physical Activity Features From Functional Accelerometer Data.IEEE journal of biomedical and health informatics · 2023Article
- Spirometry correlates with physical activity in patients with Duchenne muscular dystrophy.Pediatric pulmonology · 2023Article
- Evaluation of Wrist Accelerometer Cut-Points for Classifying Physical Activity Intensity in Youth.Frontiers in digital health · 2022Article
- Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach.International journal of environmental research and public health · 2021Article
- Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.Children (Basel, Switzerland) · 2020Article
- Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7-12 years old.Journal of sport and health science · 2020Article
- Assessing Physical Activity Using Accelerometers in Youth with Duchenne Muscular Dystrophy.Journal of neuromuscular diseases · 2020Article
- Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.International journal of environmental research and public health · 2019Article
- Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.Medicine and science in sports and exercise · 2018Article
- Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth.Medicine and science in sports and exercise · 2018Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
Abstract
introductionThis study aimed to examine the validity and accuracy of wrist accelerometers for classifying sedentary behavior (SB) in children.
methodsFifty-seven children (5-8 and 9-12 yr) completed an ~170-min protocol, including 15 semistructured activities and transitions. Nine ActiGraph (GT3X+) and two GENEActiv wrist cut points were evaluated. Direct observation was the criterion measure. The accuracy of wrist cut points was compared with that achieved by the ActiGraph hip cut point (≤25 counts per 15 s) and the thigh-mounted activPAL3. Analyses included equivalence testing, Bland-Altman procedures, and area under the receiver operating curve (ROC-AUC).
resultsThe most accurate ActiGraph wrist cut points (Kim; vector magnitude, ≤3958 counts per 60 s; vertical axis, ≤1756 counts per 60 s) demonstrated good classification accuracy (ROC-AUC = 0.85-0.86) and accurately estimated SB time in 5-8 yr (equivalence P = 0.02; mean bias = 4.1%, limits of agreement = -20.1% to 28.4%) and 9-12 yr (equivalence P < 0.01; -2.5%, -27.9% to 22.9%). The mean bias of SB time estimates from Kim were smaller than ActiGraph hip (5-8 yr: 15.8%, -5.7% to 37.2%; 9-12 yr: 17.8%, -3.9% to 39.5%) and similar to or smaller than activPAL3 (5-8 yr: 12.6%, -39.8% to 14.7%; 9-12 yr: -1.4%, -13.9% to 11.0%), although classification accuracy was similar to ActiGraph hip (ROC-AUC = 0.85) but lower than activPAL3 (ROC-AUC = 0.92-0.97). Mean bias (5-8 yr: 6.5%, -16.1% to 29.1%; 9-12 yr: 10.5%, -13.6% to 34.6%) for the most accurate GENEActiv wrist cut point (Schaefer: ≤0.19 g) was smaller than ActiGraph hip, and activPAL3 in 5-8 yr, but larger than activPAL3 in 9-12 yr. However, SB time estimates from Schaefer were not equivalent to direct observation (equivalence P > 0.05) and classification accuracy (ROC-AUC = 0.79-0.80) was lower than for ActiGraph hip and activPAL3.
conclusionThe most accurate SB ActiGraph (Kim) and GENEActiv (Schaefer) wrist cut points can be applied in children with similar confidence as the ActiGraph hip cut point (≤25 counts per 15 s), although activPAL3 was generally more accurate.
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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.