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The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners

Introduction: Motor Performance (MP) in children is an important resource for their future active lifestyle and health. Monitoring of MP is crucial to derive information of trends and to implement specific programs on the base of current MP levels. A variety of MP assessment tools exist, making it d...

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Autores principales: Eberhardt, Tanja, Bös, Klaus, Niessner, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702616/
https://www.ncbi.nlm.nih.gov/pubmed/34957000
http://dx.doi.org/10.3389/fpubh.2021.720589
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author Eberhardt, Tanja
Bös, Klaus
Niessner, Claudia
author_facet Eberhardt, Tanja
Bös, Klaus
Niessner, Claudia
author_sort Eberhardt, Tanja
collection PubMed
description Introduction: Motor Performance (MP) in children is an important resource for their future active lifestyle and health. Monitoring of MP is crucial to derive information of trends and to implement specific programs on the base of current MP levels. A variety of MP assessment tools exist, making it difficult to determine a “gold-standard” for assessment and to compare the findings. In Germany, the German Motor Test 6–18 (GMT 6–18) and Kinderturntest Plus 3–10 (KITT+ 3–10) are widely used MP assessment tools. The aim of this paper is to show which key questions can be answered within the context of a best practice example of a MP assessment tool and what can be derived from this for a practical application (the Fitness Barometer). Methods: The raw data of the Fitness Barometer was collected with the MP assessment tools GMT 6–18 and KITT+ 3–10 from 2012 through 2020. Data was pooled anonymously with the e-Research infrastructure MO|REdata and categorized into percentiles for MP and BMI. Overall, we included data of 23,864 children for the statistical analyses. T-tests for independent samples, percentage frequency analysis, descriptive statistics (chi- square-test) and single analysis of variance were conducted. Results and Discussion: Children tested reached a mean value of 57.03 (SD = 18.85). Of the sample, 12.7% children were overweight or obese and there is a significant difference between age groups [[Formula: see text] = 178.62, p < 0.001, Cramer V = 0.09; n = 23.656]. The relationship between BMI category and mean value of MP was significant [F((4,19,523)) = 224.81, p < 0.001]. During 2020, the year of the COVID-19 pandemic, mean value of endurance and speed decreased [Welch's F((1,573)) = 8.08, p = 0.005; Welch's F((1,610)) = 35.92, p < 0.001]. The GMT 6–18 and KITT+ 3–10 are valid, objective, reliable, and economic MP assessment tools for monitoring MP levels and derive added practical value. Specific programs and interventions should focus on the findings of these. The Fitness Barometer is a best practice example how a standardized assessment tool of monitoring MP point to trends on which practical evidence-based suggestions can be derived with many various partners and expertise.
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spelling pubmed-87026162021-12-25 The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners Eberhardt, Tanja Bös, Klaus Niessner, Claudia Front Public Health Public Health Introduction: Motor Performance (MP) in children is an important resource for their future active lifestyle and health. Monitoring of MP is crucial to derive information of trends and to implement specific programs on the base of current MP levels. A variety of MP assessment tools exist, making it difficult to determine a “gold-standard” for assessment and to compare the findings. In Germany, the German Motor Test 6–18 (GMT 6–18) and Kinderturntest Plus 3–10 (KITT+ 3–10) are widely used MP assessment tools. The aim of this paper is to show which key questions can be answered within the context of a best practice example of a MP assessment tool and what can be derived from this for a practical application (the Fitness Barometer). Methods: The raw data of the Fitness Barometer was collected with the MP assessment tools GMT 6–18 and KITT+ 3–10 from 2012 through 2020. Data was pooled anonymously with the e-Research infrastructure MO|REdata and categorized into percentiles for MP and BMI. Overall, we included data of 23,864 children for the statistical analyses. T-tests for independent samples, percentage frequency analysis, descriptive statistics (chi- square-test) and single analysis of variance were conducted. Results and Discussion: Children tested reached a mean value of 57.03 (SD = 18.85). Of the sample, 12.7% children were overweight or obese and there is a significant difference between age groups [[Formula: see text] = 178.62, p < 0.001, Cramer V = 0.09; n = 23.656]. The relationship between BMI category and mean value of MP was significant [F((4,19,523)) = 224.81, p < 0.001]. During 2020, the year of the COVID-19 pandemic, mean value of endurance and speed decreased [Welch's F((1,573)) = 8.08, p = 0.005; Welch's F((1,610)) = 35.92, p < 0.001]. The GMT 6–18 and KITT+ 3–10 are valid, objective, reliable, and economic MP assessment tools for monitoring MP levels and derive added practical value. Specific programs and interventions should focus on the findings of these. The Fitness Barometer is a best practice example how a standardized assessment tool of monitoring MP point to trends on which practical evidence-based suggestions can be derived with many various partners and expertise. Frontiers Media S.A. 2021-12-10 /pmc/articles/PMC8702616/ /pubmed/34957000 http://dx.doi.org/10.3389/fpubh.2021.720589 Text en Copyright © 2021 Eberhardt, Bös and Niessner. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Eberhardt, Tanja
Bös, Klaus
Niessner, Claudia
The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title_full The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title_fullStr The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title_full_unstemmed The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title_short The Fitness Barometer: A Best Practice Example for Monitoring Motor Performance With Pooled Data Collected From Practitioners
title_sort fitness barometer: a best practice example for monitoring motor performance with pooled data collected from practitioners
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702616/
https://www.ncbi.nlm.nih.gov/pubmed/34957000
http://dx.doi.org/10.3389/fpubh.2021.720589
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