TheScientificWorldJournal · 2017
Relationship between Height-Weight Difference Index and Body-Fat Percentage Estimated by Bioelectrical Impedance Analysis in Thai Adults.
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Juntaping Kanokkarn, Chittawatanarat Kaweesak, Prasitwattanaseree Sukon, Chaijaruwanich Jeerayut, Traisathit Patrinee
Аннотация
INTRODUCTION: The height-weight difference index (HWDI) is a new indicator for evaluating obesity status. While body-fat percentage (BF%) is considered to be the most accurate obesity evaluation tool, it is a more expensive method and more difficult to measure than the others.
OBJECTIVE: Our objectives were to find the relationship between HWDI and BF% and to find a BF% prediction model from HWDI in relation to age and gender.
METHOD: Bioelectrical impedance analysis was used to measure BF% in 2,771 healthy adult Thais. HWDI was calculated as the difference between height and weight. Pearson's correlation coefficient was used to assess the relationship between HWDI and BF%. Multiple linear and nonlinear regression analysis were used to construct the BF% prediction model.
RESULTS: HWDI and BF% were found to be inverse which related to a tendency toward a linear relationship. Results of a multivariate linear regression analysis, which included HWDI and age as variables in the model, predicted BF% to be 34.508 - 0.159 (HWDI) + 0.161 (age) for men and 53.35 - 0.265 (HWDI) + 0.132 (age) for women.
CONCLUSIONS: The prediction model provides an easy-to-use obesity evaluation tool that should help awareness of underweight and obesity conditions.
OBJECTIVE: Our objectives were to find the relationship between HWDI and BF% and to find a BF% prediction model from HWDI in relation to age and gender.
METHOD: Bioelectrical impedance analysis was used to measure BF% in 2,771 healthy adult Thais. HWDI was calculated as the difference between height and weight. Pearson's correlation coefficient was used to assess the relationship between HWDI and BF%. Multiple linear and nonlinear regression analysis were used to construct the BF% prediction model.
RESULTS: HWDI and BF% were found to be inverse which related to a tendency toward a linear relationship. Results of a multivariate linear regression analysis, which included HWDI and age as variables in the model, predicted BF% to be 34.508 - 0.159 (HWDI) + 0.161 (age) for men and 53.35 - 0.265 (HWDI) + 0.132 (age) for women.
CONCLUSIONS: The prediction model provides an easy-to-use obesity evaluation tool that should help awareness of underweight and obesity conditions.