Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test
Data Understanding and Preparation
- Participants would start with the physical and mental health questionnaires, mental health questionnaires were proctored by a licensed clinical psychologist, immediately followed by a body composition assessment proctored by a registered dietitian.
- The physical questionnaires assessed (1) their six-month history of musculoskeletal injury and whether the member sought medical evaluation for that injury, (2) if the member sustained an injury within the last six months, and if it impacted their participation in physical activities, (3) whether or not they were currently on a duty-limiting medical profile, and (4) on a five-point Likert scale the member indicated their perceived satisfaction with their current fitness level.
- BMI, body fat percentage, and muscle mass percentage were assessed by using the InBody230 (InBody LTD, Seoul, Republic of Korea) bioelectrical impedance analyzer .
- The assessment concluded with the administration of the APFT, which is further described below.
3. Statistical Analysis
3.2. Classical Modeling
3.3. Neural Network Modeling
- A multi-dimensional hyperparameter search (neurons, layers, learning rate, epochs, and batch size) was performed on the training dataset using three-fold cross validation. Total neuron count was limited by the Widrow recommendation .
- Overfitting was monitored by comparing the accuracy of each fold. Models with >5% inter-fold accuracy variance were not considered for selection.
- An optimal set of hyperparameters was determined by the highest mean fold accuracy of the remaining models.
- Using the optimal hyperparameters, the model was then retrained on the entire training dataset.
- The model was validated by measuring metrics on the holdout dataset.
4.1. Model Results
4.2. Classical Modeling
4.3. Neural Network Modeling
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Description of Work||Method Used||Performance||Ref|
|Predict Fitness Assessment Failure in Australian Army||Classification,|
|AUC = 0.70|||
|Predict U.S. Army Fitness Assessment 2-mile run time||Regression,|
|R2 = 0.55–0.59|||
|Predict U.S. Army Fitness Assessment Failure||Classification,|
|AUC = 0.61–0.77 (F)|
AUC = 0.61–0.80 (M)
|Variable||Mean||Max||Std Dev||Data Distribution||Notes/Definition|
|Gender||0.25||1||0.43||Binary||0 = Male (174 members)|
1 = Female (49 members)
|ORS Total *||7.63||10||1.95||~Log-Normal||Outcome Rating Scale |
|PTSD||9.28||68||11.75||Right-skewed||Post-Traumatic Stress Disorder Checklist (PCL-5) |
|InjuryEval||0.30||1||0.46||Binary||1 = Recent injury evaluated by provider|
|InjuryNoEval||0.12||1||0.33||Binary||1 = Recent injury not evaluated by provider|
|DLC||0.09||1||0.29||Binary||1 = Duty Limiting Condition|
|PhysRestr||0.27||1||0.45||Binary||1 = Recent injury resulting in physical activity restriction|
|BMI||27.13||42.3||4.10||~Normal||Body Mass Index |
|BodyFatPerc||0.29||0.49||0.82||~Normal||Body Fat Percentage |
|MusclePerc||0.33||0.47||0.06||~Normal||Muscle Mass Percentage |
|FMS_Shldr||0.12||1||0.33||Binary||1 = Functional Movement Screen (FMS) Shoulder Pain [21,22]|
|FMS_Ext||0.21||1||0.41||Binary||1 = FMS Low Back Pain [21,22]|
|FMS_Flex||0.06||1||0.23||Binary||1 = FMS Hip Pain [21,22]|
|FMS Total||14.28||20||2.60||~Normal||FMS Composite Score [21,22]|
|5-feature p-value 1||<0.01||0.86||0.82||0.82||0.82|
|4-feature p-value 2||<0.01||0.89||0.83||0.84||0.84|
|Recursive feature elimination (RFE) 3||<0.01||0.87||0.75||0.75||0.75|
|Select K Best 4||<0.01||0.86||0.82||0.82||0.82|
|Always Predicts Pass||--||0.50||0.51||0.72||0.72|
|Hyperparameter||Full Model Range||Limited Model Range|
|Neurons||2, 3, 4, 5, 10||1, 2, 3, 4, 5, 7, 9, 12|
|Hidden layers||0, 1, 2||0, 1, 2, 3|
|Batch size||16, 32||16, 32|
|Epochs||15, 20, 60, 100, 140||15, 20, 60, 100, 140|
|Learning rate||0.01, 0.001, 0.0005||0.01, 0.001, 0.0005|
|Epochs||Mean Fold |
|Inter-Fold Variance (%)|
|Full 21-input model||0.97||0.92||0.93||0.93|
|Limited 4-input model||0.96||0.93||0.93||0.93|
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Turner, J.; Wagner, T.; Langhals, B. Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test. Sports 2022, 10, 54. https://doi.org/10.3390/sports10040054
Turner J, Wagner T, Langhals B. Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test. Sports. 2022; 10(4):54. https://doi.org/10.3390/sports10040054Chicago/Turabian Style
Turner, Jeffrey, Torrey Wagner, and Brent Langhals. 2022. "Biomechanical and Psychological Predictors of Failure in the Air Force Physical Fitness Test" Sports 10, no. 4: 54. https://doi.org/10.3390/sports10040054