The Army Combat Fitness Test (ACFT) will become the United States Army's mandatory physical fitness test in March of 2022. The purpose of this study was to determine the relationship between ACFT performance and both body composition and velocity profiles, and to determine sex differences for these variables. Data was collected in November 2020 (Fall) and March 2021 (Spring) from male (n = 55) and female (n = 17) Army Reserve Officers' Training Corps (ROTC) cadets. Body composition was assessed with a bioelectrical impedance spectroscopy (BIS) device, and cadets completed a squat jump (SJ) force-velocity profile (FVP) and a hex bar deadlift (DL) load-velocity profile (LVP). Stepwise multiple regressions were used to explain the maximal amount of variance in ACFT total score and individual event performance. Results revealed that body composition and lower body power production may have a strong influence on ACFT performance. In terms of accounting for variance in ACFT total score, skeletal muscle mass and body fat percentage were able to account for 49% of shared variance, SJ height (unloaded) and SJ maximal force for 64% of shared variance, and DL maximal power and maximal velocity for 67% of shared variance. The 3-repetition maximum deadlift, standing power throw, hand-release push-up, and sprint-drag-carry events favor cadets with more muscle mass, while the leg tuck is influenced by body fat percentage and the two-mile run is affected by fat mass. Men outperformed women on all individual events and had a higher total ACFT score. Sex had greater predictive capability for the two-mile run than body composition, and for the sprint-drag-carry than any SJ metric. The greatest sex differences were on the standing power throw and sprint-drag-carry. It is recommended that Army ROTC cadets taking the ACFT maximize power production and increase muscle mass.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1649 |
| Date | 01 January 2021 |
| Creators | Boffey, David |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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