ArticleMedicine and science in sports and exercise2018
Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth.
Article in Medicine and science in sports and exercise, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of green space on physical activity and body weight status among Chinese adults: a systematic review.Frontiers in public health · 2023Pooled it
- Early Cardiac Dysfunction in Adolescents With Low Vigorous Physical Activity.Journal of the American Heart Association · 2025Trial
- Longitudinal effects of dog ownership, dog acquisition, and dog loss on children's movement behaviours: findings from the PLAYCE cohort study.The international journal of behavioral nutrition and physical activity · 2024Article
- Environmental, social, and behavioral challenges of the human circadian clock in real-life conditions.Frontiers in physiology · 2024Article
- BAYESIAN HIERARCHICAL MODELING AND ANALYSIS FOR ACTIGRAPH DATA FROM WEARABLE DEVICES.The annals of applied statistics · 2023Article
- Individual light history matters to deal with the Antarctic summer.Scientific reports · 2023Article
- Physical activity and quality of life in children with idiopathic toe walking: a cross sectional study.BMC pediatrics · 2022Article
- Evaluation of environmental, social, and behavioral modulations of the circadian phase of dancers trained in shifts.iScience · 2022Article
- Article
- A Transparent Method for Step Detection using an Acceleration Threshold.Journal for the measurement of physical behaviour · 2021Article
- 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
- Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.Sensors (Basel, Switzerland) · 2020Article
- Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time?The international journal of behavioral nutrition and physical activity · 2020Article
- Article
- An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing.Journal for the measurement of physical behaviour · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
Abstract
purposeThis study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity.
methodsFifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures.
resultsAll cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%).
conclusionWrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.
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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 Socratic graph.