Unsupervised Machine Learning inCountermovement Jump andIsometric Mid-Thigh Pull Performance Produces Distinct Combat andPhysical Fitness Clusters inMale andFemale U.S. Marine Corps Recruits.

Abstract

Several challenges face the U.S. Marine Corps (USMC) and other services in their efforts to design recruit training to augment warfighter mobility and resilience in both male and female recruits as part of an integrated model. Strength and power underpin many of the physical competencies required to meet the occupational demands one might face in military. As the military considers adopting force plate technology to assess indices of strength and power, an opportunity presents itself for the use of machine learning on large datasets to deduce the relevance of variables related to performance and injury risk. The primary aim of this study was to determine whether cluster analysis on baseline strength and power data derived from countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) adequately partitions men and women entering recruit training into distinct performance clusters. The secondary aim of this study is then to assess the between-cluster frequencies of musculoskeletal injury (MSKI). Five hundred and sixty-five males (n = 386) and females (n = 179) at the Marine Corps Recruit Depots located at Parris Island and San Diego were enrolled in the study. Recruits performed CMJ and IMTP tests at the onset of training. Injury data were collected via medical chart review. Combat fitness test (CFT) and physical fitness test (PFT) results were provided to the study team by the USMC. A k-means cluster analysis was performed on CMJ relative peak power, IMTP relative peak force, and dynamic strength index. Independent sample t-tests and Cohen's d effect sizes assessed between-cluster differences in CFT and PFT performance. Differences in cumulative incidence of lower extremity %MSKIs were analyzed using Fisher's exact test. Relative risk and 95% confidence intervals (CIs) were also calculated. The overall effects of cluster designation on CMJ and IMTP outcomes ranged from moderate (relative peak power: d = -0.68, 95% CI, -0.85 to -0.51) to large (relative peak force: d = -1.69, 95% CI, -1.88 to -1.49; dynamic strength index: d = 1.20, 95% CI, 1.02-1.38), indicating acceptable k-means cluster partitioning. Independent sample t-tests revealed that both men and women in cluster 2 (C2) significantly outperformed those in cluster 1 (C1) in all events of the CFT and PFT (P < .05). The overall and within-gender effect of cluster designation on both CFT and PFT performance ranged from small (d > 0.2) to moderate (d > 0.5). Men in C2, the high-performing cluster, demonstrated a significantly lower incidence of ankle MSKI (P = .04, RR = 0.2, 95% CI, 0.1-1.0). No other between-cluster differences in MSKI were statistically significant. Our results indicate that strength and power metrics derived from force plate tests effectively partition USMC male and female recruits into distinct performance clusters with relevance to tactical and physical fitness using k-means clustering. These data support the potential for expanded use of force plates in assessing readiness in a cohort of men and women entering USMC recruit training. The ability to pre-emptively identify high and low performers in the CFT and PFT can aid in leadership developing frameworks for tailoring training to enhance combat and physical fitness with benchmark values of strength and power.