Unsupervised Clustering Techniques Identify Movement Strategies in the Countermovement Jump Associated With Musculoskeletal Injury Risk During US Marine Corps Officer Candidates School

Abstract

Musculoskeletal injuries (MSKI) are a significant burden on the military healthcare system. Movement strategies, genetics, and fitness level have been identified as potential contributors to MSKI risk. Screening measures associated with MSKI risk are emerging, including novel technologies, such as markerless motion capture (mMoCap) and force plates (FP) and allow for field expedient measures in dynamic military settings. The aim of the current study was to evaluate movement strategies (i.e., describe variables) of the countermovement jump (CMJ) in Marine officer candidates (MOCs) via mMoCap and FP technology by clustering variables to create distinct movement strategies associated with MSKI sustained during Officer Candidates School (OCS). 728 MOCs were tested and 668 MOCs (Male MOCs = 547, Female MOCs = 121) were used for analysis. MOCs performed 3 maximal CMJs in a mMoCap space with FP embedded into the system. De-identified MSKI data was acquired from internal OCS reports for those who presented to the OCS Physical Therapy department for MSKI treatment during the 10 weeks of OCS training. Three distinct clusters were formed with variables relating to CMJ kinetics and kinematics from the mMoCap and FPs. Proportions of MOCs with a lower extremity and torso MSKI across clusters were significantly different (p < 0.001), with the high-risk cluster having the highest proportions (30.5%), followed by moderate-risk cluster (22.5%) and low-risk cluster (13.8%). Kinetics, including braking rate of force development (BRFD), braking net impulse and propulsive net impulse, were higher in low-risk cluster compared to the high-risk cluster (p < 0.001). Lesser degrees of flexion and shorter CMJ phase durations (braking phase and propulsive phase) were observed in low-risk cluster compared to both moderate-risk and high-risk clusters. Male MOCs were distributed equally across clusters while female MOCs were primarily distributed in the high-risk cluster. Movement strategies (i.e., clusters), as quantified by mMoCap and FPs, were successfully described with MOCs MSKI risk proportions between clusters. These results provide actionable thresholds of key performance indicators for practitioners to use for screening measures in classifying greater MSKI risk. These tools may add value in creating modifiable strength and conditioning training programs before or during military training.

Predictive utility of commercial grade technologies for assessing musculoskeletal injury risk in US Marine Corps Officer candidates

“…With these considerations a within population specific analysis may be deemed more appropriate to build machine learning models for deployment. Although not a supervised approach, candidates were stratified within the population using unsupervised learning on the CMJ and had a strong association to MSKI risk in low and high performers with overlap of MSKI and noMSKI within these groups ( Bird et al, 2022 ). Lastly, this demonstrates that CMJs on force plates may be a useful screening tool, but MSKI risk is not fully explained by a CMJ on a force plate.…”

“…OCS consists of controlled daily physical and military training, along with graded events that test for aerobic capacity (i.e., 3-mile run), obstacle navigation and loaded ruck marches. All candidates are required to do the same training regardless of job (e.g., attorney, infantry officer, intelligence), and sex, and there are high incidences of lower extremity and torso MSKIs that occur [Male candidates = 23% and Female candidates = 36% ( Bird et al, 2022 )].…”

Active range of motion of the shoulder: a cross-sectional study of 6635 subjects

“…The DARI markerless system has been used in several recent studies to measure active range of motion for a variety of activities. 3 , 5 , 7 , 9 , 10 , 15 One of these studies compared the markerless motion data to a “gold standard” marker-based data and showed similar consistency of joint angle calculations for each system, although the magnitudes of angle measurements differed between the 2 systems. 10 …”

“…One such technology approved by the Food and Drug Administration is the DARI markerless motion capture system, which has been used previously to assess lower extremity and full body movements in relation to injury risk and rehabilitation. 3 , 5 , 7 , 9 This system uses 8 cameras and software-based algorithms to measure active range of motion of each joint.…”

Countermovement Jump Force-Time Curve Analyses: Reliability and Comparability Across Force Plate Systems

“…Countermovement jump (CMJ) variations are most commonly implemented because they are familiar movements, simple and quick to administer, yield low injury risk, exhibit good reliability (4)(5)(6)27,29), and are ecologically valid for many sport and occupational environments (3,14,32,37,40). The tests are generally used as measures of maximal lower-body power output, countermovement strategies, and asymmetries for monitoring neuromuscular performance adaptation or maladaptation (29).…”