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THE JOURNAL OF SPORTS MEDICINE AND PHYSICAL FITNESS
A Journal on Applied Physiology, Biomechanics, Preventive Medicine,
Sports Medicine and Traumatology, Sports Psychology
Indexed/Abstracted in: Chemical Abstracts, CINAHL, Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 1,111
Original Articles BODY COMPOSITION, SPORT NUTRITION AND SUPPLEMENTATION (ergogenics)
The Journal of Sports Medicine and Physical Fitness 2005 December;45(4):518-23
Prediction of segmental percent fat using anthropometric variables
Demura S. 1, Sato S. 2, Noguchi T. 3
1 Faculty of Education Kanazawa University, Kanazawa, Japan
2 Life-long Sports Core Kanazawa Institute of Technology, Kanazawa, Japan
3 Graduate School of Natural Science and Technology Kanazawa University, Kanazawa, Japan
Aim. This study aimed to develop a prediction equation for segmental percent fat from anthropometric measurements.
Methods. The subjects were 107 adults, consisting of 77 males and 30 females, aged from 12 to 82 years. Height, weight, waist circumference, hip circumference, body mass index, waist hip ratio and subcutaneous fat thickness (SFT) were used as anthropometric measurements. The SFTs were measured at 14 sites. Segmental percent fats in both arms (%SFarms), both legs (%SFlegs) and trunk (%SFtrunk) were measured by dual-energy absorptiometry (DXA) method, and these values were used as references. To predict the segmental percent fat measured by DXA, stepwise multiple regression analysis was conducted using sex, age and the anthropometric measurements as predictors. To examine the systematic error between the observed and predicted values, the error and the observed values were plotted based on Bland-Altman technique, and limits of agreement (LA) were also calculated.
Results. The R, SEE and range of LA values in each prediction equation was as follows: %SFarms: R=0.919, SEE=3.333%, LA=6.5%; %SFlegs: R=0.915, SEE=3.468, LA=6.5%; %SFtrunk: R=0.858, SEE=4.944, LA=9.7%. These prediction equations used 5 to 7 predictors and met the necessary standards for predicting body fat. Although the prediction accuracy of %SFtrunk was inferior than those of %SFarms and %SFlegs, it was superior to those found in previous study reports predicting abdominal visceral fat mass and fat mass at the trunk from anthropometric measurements.
Conclusion. These prediction equations can be considered useful and practical for predicting segmental percent fat and assessing body fat distribution.