Literature DB >> 28543847

Validation of SenseWear Armband in children, adolescents, and adults.

G A Lopez1, J C Brønd2, L B Andersen3,4, M Dencker5, D Arvidsson5,6.   

Abstract

SenseWear Armband (SW) is a multisensor monitor to assess physical activity and energy expenditure. Its prediction algorithms have been updated periodically. The aim was to validate SW in children, adolescents, and adults. The most recent SW algorithm 5.2 (SW5.2) and the previous version 2.2 (SW2.2) were evaluated for estimation of energy expenditure during semi-structured activities in 35 children, 31 adolescents, and 36 adults with indirect calorimetry as reference. Energy expenditure estimated from waist-worn ActiGraph GT3X+ data (AG) was used for comparison. Improvements in measurement errors were demonstrated with SW5.2 compared to SW2.2, especially in children and for biking. The overall mean absolute percent error with SW5.2 was 24% in children, 23% in adolescents, and 20% in adults. The error was larger for sitting and standing (23%-32%) and for basketball and biking (19%-35%), compared to walking and running (8%-20%). The overall mean absolute error with AG was 28% in children, 22% in adolescents, and 28% in adults. The absolute percent error for biking was 32%-74% with AG. In general, SW and AG underestimated energy expenditure. However, both methods demonstrated a proportional bias, with increasing underestimation for increasing energy expenditure level, in addition to the large individual error. SW provides measures of energy expenditure level with similar accuracy in children, adolescents, and adults with the improvements in the updated algorithms. Although SW captures biking better than AG, these methods share remaining measurements errors requiring further improvements for accurate measures of physical activity and energy expenditure in clinical and epidemiological research.
© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  ActiGraph; energy expenditure; indirect calorimetry; multisensor; physical activity

Mesh:

Year:  2017        PMID: 28543847     DOI: 10.1111/sms.12920

Source DB:  PubMed          Journal:  Scand J Med Sci Sports        ISSN: 0905-7188            Impact factor:   4.221


  7 in total

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2.  Physical activity in women with subclinical hypothyroidism.

Authors:  A Tanriverdi; B Ozcan Kahraman; I Ozsoy; F Bayraktar; B Ozgen Saydam; S Acar; E Ozpelit; B Akdeniz; S Savci
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3.  Longitudinal analysis of physical activity, sedentary behaviour and anthropometric measures from ages 6 to 11 years.

Authors:  Phillipp Schwarzfischer; Dariusz Gruszfeld; Piotr Socha; Veronica Luque; Ricardo Closa-Monasterolo; Déborah Rousseaux; Melissa Moretti; Benedetta Mariani; Elvira Verduci; Berthold Koletzko; Veit Grote
Journal:  Int J Behav Nutr Phys Act       Date:  2018-12-07       Impact factor: 6.457

4.  Sleepiness of day workers and watchkeepers on board at high seas: a cross-sectional study.

Authors:  Marcus Oldenburg; Hans-Joachim Jensen
Journal:  BMJ Open       Date:  2019-07-09       Impact factor: 2.692

5.  Agreement between the SHAPES Questionnaire and a Multiple-Sensor Monitor in Assessing Physical Activity of Adolescents Using Categorial Approach: A Cross-Sectional Study.

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6.  Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

Authors:  Fenfen Zhou; Xiaojian Yin; Rui Hu; Aya Houssein; Steven Gastinger; Brice Martin; Shanshan Li; Jacques Prioux
Journal:  Nutrients       Date:  2022-10-08       Impact factor: 6.706

7.  Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth.

Authors:  Stuart J Fairclough; Danielle L Christian; Pedro F Saint-Maurice; Paul R Hibbing; Robert J Noonan; Greg J Welk; Philip M Dixon; Lynne M Boddy
Journal:  Int J Environ Res Public Health       Date:  2019-10-02       Impact factor: 3.390

  7 in total

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