Published Online: 11 December 2012
Page Count: 10
Pepper, M. Reese
Ph.D., Dept. of Nutritional Sciences at The Univ. of Texas at Austin, Austin, TX
Freeland-Graves, Jeanne H.
Dept. of Nutritional Sciences, The Univ. of Texas at Austin, Austin, TX
Ph.D. student, School of Human Ecology, The Univ. of Texas at Austin, Austin, TX
Stanforth, Phillip R.
Dept. of Kinesiology and Health Education, The Univ. of Texas at Austin, Austin, TX
School of Human Ecology, The Univ. of Texas at Austin, Austin, TX
(Received 15 November 2011; accepted 19 June 2012)
Overweight and obesity status is often categorized by body mass index (BMI), although this is not a measurement of body fat. Adiposity, especially in the abdominal area, is a better predictor of obesity-related diseases. However, current methods for assessment of body composition have limitations of bulkiness and expense. The purpose of this study was to evaluate a stereovision imaging system for analysis of body fat. A sample of 105 subjects was measured for body volume using the stereovision imaging system, as compared to air displacement plethysmography and hydrodensitometry. Body density was calculated from total body volume via stereovision imaging, air displacement plethysmography, and hydrodensitometry with weight. Then fat was computed via the Siri equation, and compared to body fat measurements via dual energy X-ray absorptiometry. Mean volume and fat measurements by stereovision and air displacement plethysmography did not differ significantly (mean differences −0.07 ± 0.17 L, −0.36 ± 0.82 kg, respectively, P > 0.05). Stereovision measurements of regional body volumes, lengths, and circumferences were used to develop a prediction equation via internal cross-validation for improved estimation of fat mass. This prediction equation reduced variation in individuals and improved effectiveness of the stereovision imaging system.
Paper ID: JTE20120169