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Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children

Background: The objective of this paper is to derive a wrist-placed cut-point threshold for distinguishing sedentary behaviors from light-intensity walking using the ActiGraph GT3X+ in children. Methods: This study employed a cross-sectional study design, typically used in measurement-related studie...

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Autores principales: Chandler, Jessica, Beets, Michael, Saint-Maurice, Pedro, Weaver, Robert, Cliff, Dylan, Drenowatz, Clemens, Moore, Justin B., Sui, Xuemei, Brazendale, Keith
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210293/
https://www.ncbi.nlm.nih.gov/pubmed/30261646
http://dx.doi.org/10.3390/children5100137
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author Chandler, Jessica
Beets, Michael
Saint-Maurice, Pedro
Weaver, Robert
Cliff, Dylan
Drenowatz, Clemens
Moore, Justin B.
Sui, Xuemei
Brazendale, Keith
author_facet Chandler, Jessica
Beets, Michael
Saint-Maurice, Pedro
Weaver, Robert
Cliff, Dylan
Drenowatz, Clemens
Moore, Justin B.
Sui, Xuemei
Brazendale, Keith
author_sort Chandler, Jessica
collection PubMed
description Background: The objective of this paper is to derive a wrist-placed cut-point threshold for distinguishing sedentary behaviors from light-intensity walking using the ActiGraph GT3X+ in children. Methods: This study employed a cross-sectional study design, typically used in measurement-related studies. A sample of 167 children, ages 5–11 years (mean ± SD: 8.0 ± 1.8 years), performed up to eight seated sedentary activities while wearing accelerometers on both wrists. Activities included: reading books, sorting cards, cutting and pasting, playing board games, eating snacks, playing with tablets, watching TV, and writing. Direct observation verified sedentary behavior from light activity. Receiver operator characteristic (ROC) analyses were used to determine optimal cut-point thresholds. Quantile regression models estimated differences between dominant and non-dominant placement. Results: The optimal cut-point threshold for the non-dominant wrist was 203 counts/5 s with sensitivity, specificity, and area under the curve (AUC) of 71.56, 70.83, and 0.72, respectively. A 10-fold cross-validation revealed an average AUC of 0.70. Statistically significant (p ≤ 0.05) differences in median counts ranging from 7 to 46 counts/5 s were found between dominant and non-dominant placement in five out of eight sedentary activities, with the dominant wrist eliciting higher counts/5 s. Conclusion: Results from this study support the recommendation to place accelerometers on the non-dominant wrist to minimize “noise” during seated sedentary behaviors.
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spelling pubmed-62102932018-11-05 Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children Chandler, Jessica Beets, Michael Saint-Maurice, Pedro Weaver, Robert Cliff, Dylan Drenowatz, Clemens Moore, Justin B. Sui, Xuemei Brazendale, Keith Children (Basel) Article Background: The objective of this paper is to derive a wrist-placed cut-point threshold for distinguishing sedentary behaviors from light-intensity walking using the ActiGraph GT3X+ in children. Methods: This study employed a cross-sectional study design, typically used in measurement-related studies. A sample of 167 children, ages 5–11 years (mean ± SD: 8.0 ± 1.8 years), performed up to eight seated sedentary activities while wearing accelerometers on both wrists. Activities included: reading books, sorting cards, cutting and pasting, playing board games, eating snacks, playing with tablets, watching TV, and writing. Direct observation verified sedentary behavior from light activity. Receiver operator characteristic (ROC) analyses were used to determine optimal cut-point thresholds. Quantile regression models estimated differences between dominant and non-dominant placement. Results: The optimal cut-point threshold for the non-dominant wrist was 203 counts/5 s with sensitivity, specificity, and area under the curve (AUC) of 71.56, 70.83, and 0.72, respectively. A 10-fold cross-validation revealed an average AUC of 0.70. Statistically significant (p ≤ 0.05) differences in median counts ranging from 7 to 46 counts/5 s were found between dominant and non-dominant placement in five out of eight sedentary activities, with the dominant wrist eliciting higher counts/5 s. Conclusion: Results from this study support the recommendation to place accelerometers on the non-dominant wrist to minimize “noise” during seated sedentary behaviors. MDPI 2018-09-26 /pmc/articles/PMC6210293/ /pubmed/30261646 http://dx.doi.org/10.3390/children5100137 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chandler, Jessica
Beets, Michael
Saint-Maurice, Pedro
Weaver, Robert
Cliff, Dylan
Drenowatz, Clemens
Moore, Justin B.
Sui, Xuemei
Brazendale, Keith
Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title_full Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title_fullStr Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title_full_unstemmed Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title_short Wrist-Based Accelerometer Cut-Points to Identify Sedentary Time in 5–11-Year-Old Children
title_sort wrist-based accelerometer cut-points to identify sedentary time in 5–11-year-old children
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210293/
https://www.ncbi.nlm.nih.gov/pubmed/30261646
http://dx.doi.org/10.3390/children5100137
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