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Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participa...

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Autores principales: Hartung, Verena, Sarshar, Mustafa, Karle, Viktoria, Shammas, Layal, Rashid, Asarnusch, Roullier, Paul, Eilers, Caroline, Mäurer, Mathias, Flachenecker, Peter, Pfeifer, Klaus, Tallner, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764011/
https://www.ncbi.nlm.nih.gov/pubmed/33322833
http://dx.doi.org/10.3390/ijerph17249314
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author Hartung, Verena
Sarshar, Mustafa
Karle, Viktoria
Shammas, Layal
Rashid, Asarnusch
Roullier, Paul
Eilers, Caroline
Mäurer, Mathias
Flachenecker, Peter
Pfeifer, Klaus
Tallner, Alexander
author_facet Hartung, Verena
Sarshar, Mustafa
Karle, Viktoria
Shammas, Layal
Rashid, Asarnusch
Roullier, Paul
Eilers, Caroline
Mäurer, Mathias
Flachenecker, Peter
Pfeifer, Klaus
Tallner, Alexander
author_sort Hartung, Verena
collection PubMed
description Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.
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spelling pubmed-77640112020-12-27 Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types Hartung, Verena Sarshar, Mustafa Karle, Viktoria Shammas, Layal Rashid, Asarnusch Roullier, Paul Eilers, Caroline Mäurer, Mathias Flachenecker, Peter Pfeifer, Klaus Tallner, Alexander Int J Environ Res Public Health Article Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes. MDPI 2020-12-12 2020-12 /pmc/articles/PMC7764011/ /pubmed/33322833 http://dx.doi.org/10.3390/ijerph17249314 Text en © 2020 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
Hartung, Verena
Sarshar, Mustafa
Karle, Viktoria
Shammas, Layal
Rashid, Asarnusch
Roullier, Paul
Eilers, Caroline
Mäurer, Mathias
Flachenecker, Peter
Pfeifer, Klaus
Tallner, Alexander
Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title_full Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title_fullStr Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title_full_unstemmed Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title_short Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types
title_sort validity of consumer activity monitors and an algorithm using smartphone data for measuring steps during different activity types
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7764011/
https://www.ncbi.nlm.nih.gov/pubmed/33322833
http://dx.doi.org/10.3390/ijerph17249314
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