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Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test
Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030411/ https://www.ncbi.nlm.nih.gov/pubmed/35447864 http://dx.doi.org/10.3390/sports10040054 |
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author | Turner, Jeffrey Wagner, Torrey Langhals, Brent |
author_facet | Turner, Jeffrey Wagner, Torrey Langhals, Brent |
author_sort | Turner, Jeffrey |
collection | PubMed |
description | Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition. |
format | Online Article Text |
id | pubmed-9030411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90304112022-04-23 Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test Turner, Jeffrey Wagner, Torrey Langhals, Brent Sports (Basel) Article Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition. MDPI 2022-04-06 /pmc/articles/PMC9030411/ /pubmed/35447864 http://dx.doi.org/10.3390/sports10040054 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Turner, Jeffrey Wagner, Torrey Langhals, Brent Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title | Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title_full | Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title_fullStr | Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title_full_unstemmed | Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title_short | Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test |
title_sort | biomechanical and psychological predictors of failure in the air force physical fitness test |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030411/ https://www.ncbi.nlm.nih.gov/pubmed/35447864 http://dx.doi.org/10.3390/sports10040054 |
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