Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates
Recently, commercial grade technologies have provided black box algorithms potentially relating to musculoskeletal injury (MSKI) risk and functional movement deficits, in which may add value to a high-performance model. Thus, the purpose of this manuscript was to evaluate composite and component sco...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2023.1088813/full |
| _version_ | 1797951367985233920 |
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| author | Matthew B. Bird Kristen J. Koltun Qi Mi Mita Lovalekar Brian J. Martin Tim L. A. Doyle Bradley C. Nindl |
| author_facet | Matthew B. Bird Kristen J. Koltun Qi Mi Mita Lovalekar Brian J. Martin Tim L. A. Doyle Bradley C. Nindl |
| author_sort | Matthew B. Bird |
| collection | DOAJ |
| description | Recently, commercial grade technologies have provided black box algorithms potentially relating to musculoskeletal injury (MSKI) risk and functional movement deficits, in which may add value to a high-performance model. Thus, the purpose of this manuscript was to evaluate composite and component scores from commercial grade technologies associations to MSKI risk in Marine Officer Candidates. 689 candidates (Male candidates = 566, Female candidates = 123) performed counter movement jumps on SPARTA™ force plates and functional movements (squats, jumps, lunges) in DARI™ markerless motion capture at the start of Officer Candidates School (OCS). De-identified MSKI data was acquired from internal OCS reports for those who presented to the Physical Therapy department for MSKI treatment during the 10 weeks of training. Logistic regression analyses were conducted to validate the utility of the composite scores and supervised machine learning algorithms were deployed to create a population specific model on the normalized component variables in SPARTA™ and DARI™. Common MSKI risk factors (cMSKI) such as older age, slower run times, and females were associated with greater MSKI risk. Composite scores were significantly associated with MSKI, although the area under the curve (AUC) demonstrated poor discrimination (AUC = .55–.57). When supervised machine learning algorithms were trained on the normalized component variables and cMSKI variables, the overall training models performed well, but when the training models were tested on the testing data the models classified MSKI “by chance” (testing AUC avg = .55–.57) across all models. Composite scores and component population specific models were poor predictors of MSKI in candidates. While cMSKI, SPARTA™, and DARI™ models performed similarly, this study does not dismiss the use of commercial technologies but questions the utility of a singular screening task to predict MSKI over 10 weeks. Further investigations should evaluate occupation specific screening, serial measurements, and/or load exposure for creating MSKI risk models. |
| first_indexed | 2024-04-10T22:30:31Z |
| format | Article |
| id | doaj.art-35ca4cf0f64b45eaa366ebcbc42293d1 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-04-10T22:30:31Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-35ca4cf0f64b45eaa366ebcbc42293d12023-01-17T05:38:42ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2023-01-011410.3389/fphys.2023.10888131088813Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidatesMatthew B. Bird0Kristen J. Koltun1Qi Mi2Mita Lovalekar3Brian J. Martin4Tim L. A. Doyle5Bradley C. Nindl6Department of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesDepartment of Health Sciences, Biomechanics, Physical Performance and Exercise Research Group, Macquarie University, Sydney, NSW, AustraliaDepartment of Sports Medicine and Nutrition, Neuromuscular Research Laboratory/Warrior Human Performance Research Center, University of Pittsburgh, Pittsburgh, PA, United StatesRecently, commercial grade technologies have provided black box algorithms potentially relating to musculoskeletal injury (MSKI) risk and functional movement deficits, in which may add value to a high-performance model. Thus, the purpose of this manuscript was to evaluate composite and component scores from commercial grade technologies associations to MSKI risk in Marine Officer Candidates. 689 candidates (Male candidates = 566, Female candidates = 123) performed counter movement jumps on SPARTA™ force plates and functional movements (squats, jumps, lunges) in DARI™ markerless motion capture at the start of Officer Candidates School (OCS). De-identified MSKI data was acquired from internal OCS reports for those who presented to the Physical Therapy department for MSKI treatment during the 10 weeks of training. Logistic regression analyses were conducted to validate the utility of the composite scores and supervised machine learning algorithms were deployed to create a population specific model on the normalized component variables in SPARTA™ and DARI™. Common MSKI risk factors (cMSKI) such as older age, slower run times, and females were associated with greater MSKI risk. Composite scores were significantly associated with MSKI, although the area under the curve (AUC) demonstrated poor discrimination (AUC = .55–.57). When supervised machine learning algorithms were trained on the normalized component variables and cMSKI variables, the overall training models performed well, but when the training models were tested on the testing data the models classified MSKI “by chance” (testing AUC avg = .55–.57) across all models. Composite scores and component population specific models were poor predictors of MSKI in candidates. While cMSKI, SPARTA™, and DARI™ models performed similarly, this study does not dismiss the use of commercial technologies but questions the utility of a singular screening task to predict MSKI over 10 weeks. Further investigations should evaluate occupation specific screening, serial measurements, and/or load exposure for creating MSKI risk models.https://www.frontiersin.org/articles/10.3389/fphys.2023.1088813/fullmachine learningsupervised learningmilitaryrandom forestrecursive partitioning |
| spellingShingle | Matthew B. Bird Kristen J. Koltun Qi Mi Mita Lovalekar Brian J. Martin Tim L. A. Doyle Bradley C. Nindl Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates Frontiers in Physiology machine learning supervised learning military random forest recursive partitioning |
| title | Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates |
| title_full | Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates |
| title_fullStr | Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates |
| title_full_unstemmed | Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates |
| title_short | Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates |
| title_sort | predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in us marine corps officer candidates |
| topic | machine learning supervised learning military random forest recursive partitioning |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2023.1088813/full |
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