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Published on: August 8, 2019
Influence of Body Mass on Fitness Performance in Naval Special Warfare Operators
Dallas E Wood1, David P Swain2
1Department of the Navy, Human Performance Program, Naval Special Warfare, Virginia Beach, Virginia; and.
Abstract:
Wood, DE and Swain, DP. Influence of body mass on fitness performance in Naval Special Warfare operators. J Strength Cond Res 35(11): 3120-3127, 2021-U.S. Naval Special Operations Forces have performed some of the U.S. Military's most rigorous missions. The Human Performance Program (HPP) developed a physical performance testing battery to assess and monitor physical fitness. Testing bias relative to body mass has been noted in the past literature, including military physical fitness tests. This retrospective study looked to determine whether there is body mass bias in the HPP fitness assessment and whether an optimum body mass for each fitness test could be determined. Data from 333 subjects (age: 28.4 ± 5.0 years; height: 178.4 ± 6.2 cm; mass: 86.0 ± 9.2 kg) were analyzed to compare body mass with performance on the 8 tests: standing long jump, Pro-Agility test, weighted pull-up, body weight bench press, 1 repetition maximum (1RM) deadlift, 274-m shuttle run, 4.83-km run, and 800-m swim. Linear regression analysis was used to analyze the relationship of body mass to performance; a second-degree polynomial was used to determine best-fit curves for each of the physical fitness tests; analysis of variance was used to examine differences in performance between body mass groups. Significantly better performance for lighter subjects was found in the Pro-Agility test, weighted pull-up, body weight bench press, 274-m shuttle run, and 4.83-km run. Heavier subjects performed better in the 1RM deadlift. Second-degree polynomial regression revealed optimum body mass for the Pro-Agility test, 274-m shuttle run, and 4.83-km run to be 7-16 kg heavier than the lowest body mass. These findings could help professionals better assess and train operators of varying body size.
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08:36Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments