Tactical Strength and Conditioning
Megan Sax van der Weyden, MS, CSCS, TSAC-F (she/her/hers)
PhD Candidate
George Mason University
Manassas, Virginia, United States
Mike Toczko, MS (he/him/his)
PhD Candidate
George Mason University
Manassas, Virginia, United States
Yosef Shaul, MS
Graduate Student
George Mason University
Fairfax, Virginia, United States
Kayleigh M. Newman (she/her/hers)
Undergraduate Student
George Mason University
Warrenton, Virginia, United States
Mark G. Abel, PhD, CSCS*D, TSAC-F*D
Full Professor
University of Kentucky
Lexington, Kentucky, United States
Nicholas Clark
Lecturer
University of Essex
Colchester, England, United Kingdom
Joel Martin, PhD
Associate Professor
George Mason University
Manassas, Virginia, United States
Reserve Officer Training Corps (ROTC) Cadets train to meet the same Army Combat Fitness Test (ACFT) standards as Active Duty (AD) soldiers. Unlike AD soldiers, ROTC cadets do not have a Holistic Health and Fitness team to support their physical training (PT) needs. A one-size-fits-all PT approach is often utilized within ROTC units, which ignores the inherent diversity in physical fitness (PF) levels among cadets. Understanding sub-groupings of cadets will aid in more targeted and individualized PT.
Purpose: To use cluster analyses to identify subgroups of ROTC cadets based on raw ACFT performance and to use body composition, power, and strength measures to cross-confirm subgroup allocation.
Methods: 125 ROTC cadets (95m, 30f) were assessed. Day 1: body composition (BC) via handheld bioelectrical impedance analysis, 2 countermovement jumps (CMJ) by portable force plates, and handgrip strength (HS) by dynamometer. Day 2: ACFT according to published guidelines. A cluster analysis based on raw ACFT performance was conducted using Ward’s method and visualized with a dendrogram and scree plot to determine the number of distinct clusters. A cluster solution was selected and confirmed by conducting one-way ANOVAs, and post hoc independent t-tests with Bonferroni correction, for ACFT total score, BC, CMJ and HS between clusters. alpha = 0.01
Results: A four cluster (C-) solution was created with C-1 as low performers, C-2 as low-moderate performers, C-3 as moderate-high performers, and C-4 as high performers on raw ACFT scores. There were significant differences between groups in ACFT events & total score, CMJ height, body fat percent, fat free mass, and HS, but not fat mass (Table 1).
Conclusions: The clusters of low (C-1) and high (C-4) performers were significantly different on all variables except fat mass. The majority of cadets in C-1 were females and in C-4 were males, which may partially account for the large differences. 2-mile run differentiates between the moderate performers (C-2 vs C-3), where C-2 may benefit from more metabolic capacity training than their peers. Thus, generic PT for all cadets (one-size-fits-all) or classifying cadets into only ‘low’/’ high’ PF groups may underrepresent the PF levels and individual needs of cadets performing close to the median (C-2 & C-3) who may be over- or under-trained.
PRACTICAL APPLICATIONS: Physical fitness is a key variable in determining the cadet national order of merit lists, where rank increases likelihood of receiving one’s desired job. While unit PT can promote cohesion and morale, the one-size-fits-all approach commonly used may be suboptimal to address cadets’ individual needs and goals. Practitioners can identify sub-groups of cadets, using an unbiased clustering method, to more effectively program PT per sub-group needs.
Acknowledgements: None