Body fat standards and individual physical readiness in a randomized army sample: screening weights, methods of fat assessment, and linkage to physical fitness.

摘要

Body fat standards have been used by the military services since the early 1980s to prevent obesity and motivate good fitness habits. The Army Weight Control Program has continued to undergo evaluation and incorporate improvements based on emerging scientific findings. Recently drafted revisions of Department of Defense-wide procedures address issues of consistency and validity raised by external oversight groups. This study evaluated the impact of three proposed refinements of the Army Weight Control Program. Anthropometric measurements and fitness test performance were obtained in a randomized sample of 1,038 male and 347 nonpregnant female soldiers at three Army posts. Of this sample, 11% of men and 17% of women were overweight and overfat; 6.3 and 9.8%, respectively, were currently on the Army Weight Control Program. Screening weight tables that ensure women are not inappropriately striving to meet weights more stringent than "healthy" weight (i.e., body mass index < 25 kg/m2) still correctly identified all women for evaluation for the age-specific body fat standards. Body fat estimation using more valid DoD body fat equations that include an abdominal circumference for women reduced the number of female soldiers currently classified as exceeding fat standards, coincidentally resulting in a comparable prevalence of male and female soldiers over the fat standards (12%). A body fat allowance for young soldiers who scored very well on the physical fitness test could have benefited one-fourth of the soldiers exceeding fat standards and acknowledges biological variability in body fat thresholds. Whereas this linkage may motivate fitness habits, it complicates enforcement of reasonably achievable body fat standards. The proposed changes in fat screening and measurement methods are appropriate, but the impact to health and physical readiness of the Force cannot be accurately predicted or measured because of the absence of comprehensive baseline data and tracking mechanisms.