Identifier

etd-03272008-085840

Degree

Master of Science (MS)

Department

Human Ecology

Document Type

Thesis

Abstract

The purpose of this study was to test the hypothesis that self-reported physical activity (PA) levels quantified from the International Physical Activity Questionnaire (IPAQ) could be used to improve the prediction of percent body fat (%BF) measured by dual-energy X-ray absorptiometry (DXA) from body mass index (BMI), gender, and race in White and Black college students. A total of 278 students, aged 18 – 24 yr, volunteered to participate. There were 133 males (85 White and 48 Black) and 145 females (77 White and 68 Black). Total activity levels were quantified in MET-hours per week (MET-hrs•wk-1) using the IPAQ short form. Height and weight were measured and BMI values calculated (kg•m-2). Percent fat was assessed using DXA. Regression analysis was used to determine the impact of MET-hrs•wk-1 on the relationship between %BF and BMI, taking gender and race into account. The prediction sum of squares (PRESS) statistic was used to cross-validate the models. Mean (± SD) values were as follows: MET-hrs•wk-1 37.4 ± 21.9, %BF 24.5 ± 9.3%, and BMI 24.4 ± 4.1 kg•m-2. Percent body fat was significantly correlated with MET-hrs•wk-1 (r = -0.44, p < 0.0001) and BMI (r = 0.38, p < 0.0001). Stepwise regression analysis of a reduced model with BMI, gender and race produced an R2 value of 0.81 (root mean square error [RMSE] = 4.07). The full model with MET-hrs•wk-1 marginally improved the prediction of %BF (R2 = 0.83, RMSE = 3.87). When cross-validated, the corresponding PRESS statistic for the reduced and full model was 4.10 and 3.90, respectively. These results suggest that %BF can be predicted with greater precision and accuracy in a college-aged population when MET-hrs•wk-1 are included in addition to BMI, gender, and race.

Date

2008

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Georgianna Tuuri

DOI

10.31390/gradschool_theses.4003

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Human Ecology Commons

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