We used regression analysis to examine the relationship between HWDI and BF% performed on men and women separately.
The BF% in men was statistically significantly lower than in women (27% and 34%, resp.; p < 0.001).
Relationship between HWDI and BF%. Figure 2 shows the relationship between HWDI and BF%.
The Effect of Age, Gender, and HWDI on BF%. The study of the effect of age, gender, and HWDI on BF% showed all three variables' relationship with BF% to be statistically significant for building a prediction model (p < 0.001 for all variables).
Statistical comparisons of categorical variables (BMI and BF%) were computed using Pearson chi square test.
The overall mean age was 25.6710.10 years BMI was 27.798.57kg/m2 and BF% was 24.61%7.61.
Both BMI and BF% showed positive correlation with age (r=0.144; p=0.001) (r=0.261; p=0.001) and weight (r=0.578; p=0.001) (r=0.444; p=0.001) respectively.
Here we propose and agree that Asian populations need to be evaluated by their own cut-off values in terms of BMI BF% and associated health risks.13
Bland Altman plots exploring for individual differences between the criterion and prediction BF% values are shown in Figures 1-3.
Due to athletes typically having higher muscle masses at any given BMI compared to non-athletes (19), our hypothesis was that the BMI-based BF% equations would not be an accurate technique when compared to DEXA for estimating BF% in female athletes.
Our results were similar, showing that there was either no (i.e., JBMI-BF and DBMI-BF) or little (i.e., GBMI-BF) significant difference in the mean BF% values between the DEXA and the BMI-based BF% equations across the entire cohort of female athletes.
Athletes have been shown to have significantly lower levels of BF% compared to non-athletes of the same BMI (19,24).