Predicting Maximal Military Occupational Task Performance from Physical Fitness Tests Using Machine Learning.
Purpose: Optimal performance in military tasks is crucial for operational success. These tasks are often simulated in training, assessing personnel performance within a military environment. However, these assessments are time-consuming and a potential injury risk. Physical characteristics such as m...
| Published in: | Medicine & Science in Sports & Exercise Vol. 57; no. 9; pp. 1877 - 1886 |
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| Main Authors: | , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Lippincott Williams & Wilkins
Sep2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187345985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187345985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Sep2025 vid: 57 iid: 9 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 187345985 187345985 187345985 10.1249/MSS.0000000000003727 187345985 ppf: 1877 ppct: 9 formats: tig: atl: Predicting Maximal Military Occupational Task Performance from Physical Fitness Tests Using Machine Learning. aug: au: MCCARTHY, AYDEN WILLS, JODIE ANNE FULLER, JOEL THOMAS CASSIDY, STEVE NINDL, BRAD C. DOYLE, TIM L. A. affil: Performance and Expertise Research Centre, Macquarie University, Macquarie Park, NSW, AUSTRALIA sug: subj: Military Services Australia Job Performance Evaluation Task Performance and Analysis Evaluation Physical Fitness Evaluation Exercise Test Machine Learning Prediction Models Evaluation Weight Lifting Human Male Female Adult Nonexperimental Studies Descriptive Statistics Emergency Evacuation Data Science Funding Source Australia Adult: 19-44 years Male Female ab: Purpose: Optimal performance in military tasks is crucial for operational success. These tasks are often simulated in training, assessing personnel performance within a military environment. However, these assessments are time-consuming and a potential injury risk. Physical characteristics such as muscular strength, power, aerobic endurance, and circumferences can be used to predict these dynamic and demanding tasks. Utilizing machine learning models to predict assessment outcomes may lead to optimized management of personnel, time, and interventions in the military. Methods: This study recruited 35 participants to complete two physical sessions assessing multiple physical characteristics and lift-to-place and jerry-can-carry assessments. Machine learning models were developed to predict assessment outcomes based on a down-selection of physical characteristics metrics. Root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of variation of the root mean square error (CVRMSE) were used to evaluate the models' predictive capabilities. Results: The support vector regression (SVR) and ridge models could predict the lift-to-place outcome to an RMSE of ±1.77 kg (NRMSE = 4.44%, CVRMSE = 0.18) and ±2.33 kg (NRMSE = 5.84%; CVRMSE = 0.24) with four and three physical tests, respectively. The multilayer perceptron and SVR models predicted the jerry-can-carry outcome to ±3.36 laps (NRMSE = 23.06%, CVRMSE = 0.39) and ±3.67 laps (NRMSE = 25.20%, CVRMSE = 0.42) with 12 and 8 physical tests, respectively. Conclusions: The lift-to-place outcome can be accurately predicted, showing potential military implementation. The jerry-can-carry outcome shows promise; however, further model optimization and training metrics are required to reduce error. Machine learning models demonstrate their applicability to optimize occupational selection pathways and training interventions for desirable performance in military settings. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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