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DOI 10.1249/mss.0000000000003822

Медицинская библиотека · Medicine and science in sports and exercise · 2025

Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.

McCarthy Ayden, Fuller Joel Thomas, Wills Jodie Anne, Cassidy Steve, Lovalekar Mita, Nindl Bradley C, Doyle Tim L A

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Аннотация

PURPOSE: Combat maneuverability is critical for soldier survivability. Military organizations ensure effective combat maneuverability through routine assessments. Advanced statistical analyses may improve combat movement efficiency practices. This study grouped physical qualities (e.g., strength, power, mobility) via an exploratory factor analysis (EFA) and extracted factors to compare high and low performers and develop predictive models.

METHODS

Thirty-four participants completed two sessions assessing physical qualities and combat movement performance. Participants were classified as either "high" or "low" performers (i.e., completed 50 laps of the assessment or completed less than 50 laps, respectively). An EFA was conducted to reduce physical quality dataset dimensions into specific factors. T -test and effect size compared factors between high and low performers. Logistic regression, multilayer perceptron, and random forest models were trained and tested to classify performers based on factor values. Feature importance scores determined factors most influential in classifying participants.

RESULTS

EFA resulted in four factors (81.46% variance explained). Factor 1 represented isometric strength, jumping, and drop landing ability. Factors 2-4 represent isometric strength and rate of force development in the lower and upper body, and overhead squat ability, respectively. All factors significantly differed between groups, with high performers demonstrating higher mean values than low performers ( P < 0.05). Factor 1 demonstrated a very large effect size ( d = 2.15), whereas factors 2-4 were moderate-large ( d = 0.72-0.81). The logistic regression model had 100% accuracy in the testing phase, whereas other models achieved 86%. Factor 1 was the most influential factor across models (approximately six times more than other factors).

CONCLUSIONS

Utilized models show military applicability in classifying high or low performers for combat maneuverability. Physical interventions optimizing factor 1 may enhance combat maneuverability.

Для цитирования: McCarthy Ayden, Fuller Joel Thomas, Wills Jodie Anne, Cassidy Steve, Lovalekar Mita, Nindl Bradley C, Doyle Tim L A. Strength, Jump Height, Landing, and Mobility Metrics Predict High and Low Performers of a Fire and Move Assessment: A Machine Learning Approach.. Medicine and science in sports and exercise. 2025. DOI: 10.1249/mss.0000000000003822.

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