Multiple linear regression data was initially utilized, followed closely by a study of the possibility of an excellent nonlinear relationship current by along with quadratic and cubic versions. Modified [Roentgen.sup.2] and you can basic mistake away from imagine (SEE) thinking were used evaluate brand new overall performance of the predictive model out of BF%.
All of the reports out of p had been several-sided and you will p lower than 0.05 are felt mathematically tall. Every analyses were performed using STATA app version a dozen.0 (STATA Corp, School Station, Colorado, USA) and you may SPSS variation 17.0 (SPSS Inc., Chicago, USA).
3.1. Study Population and Baseline Characteristics. All 2,771 participants in this study were over 18 years and comprised 64% women with a median age of 52 years (IQR, 43-60) and 36% men with a median age of 60 years (IQR, 47-68). The BF% in men was statistically significantly lower than in women (27% and 34%, resp.; p < 0.001). The difference in HWDI between men and women was also statistically significant (101 [IQR, 95-107] for men and 98 [IQR, 92-104] for women; p < 0.001) (see Table 1).
In our study, HWDI, a fairly the obesity aspect signal, is actually discovered for a keen inverse reference to BF% both in individuals
Shape 1 reveals the relationship anywhere between mediocre Bmi and you will ages. It had been discovered that, on ages of 18 to help you 39 ages, the latest mean Body mass index increases as age develops however,, immediately after interacting with sixty yrs old, the brand new suggest Body mass index decrease since decades increases (select Contour 1(a)). The opposite can be acquired with the relationship ranging from imply HWDI and you will decades.
3.2. Relationship between HWDI and BF%. Figure 2 shows the relationship between HWDI and BF%. Pearson’s correlation coefficient (r) = -0.200 (p < 0.001) in men and r = -0.473 (p < 0.001) in women. In contrast, the direct relationship was found between BMI and BF%, r = 0.144 (p < 0.001) in men and r = 0.421 (p < 0.001) in women. Furthermore, the relationship between HWDI and BF% was statistically significant even when analyzed with respect to age group and gender (p < 0.001) (see Figure 3).
Mathematically, an enthusiastic inverse matchmaking between HWDI and you can BF% is discover since HWDI enhanced when you’re BF% well worth rather diminished
3.3. 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 sugar baby New York for building a prediction model (p < 0.001 for all variables). HWDI and BF% by age and gender resulted in r = -0.629/ - 0.518 (men/women) for the 18-39-year age group, r = -0.372/ - 0.560 for the 40-59-year age group, and r = -0.125/ - 0.369 for age group over 60 years (see Figure 3).
3.4. Predictive Modeling of BF% by Gender. In this study, several forms of relationship between HWDI and BF% were studied: linear, quadratic, and cubic. However, Figure 3 shows that the relationship tended to be in linear form more than the others, and so we elected to use a linear form in the construction of the BF% prediction model. The results of a multivariate linear regression analysis, which includes the HWDI and age variables, yielded a BF% for men of – 0.159 (HWDI) + 0.161 (age) [adjusted [R.sup.2] = 0.215, standard error of estimate (SEE) = 5.37%, p < 0.001], and, for women, - 0.265 (HWDI) + 0.132 (Age) [adjusted [R.sup.2] = 0.337, SEE = 4.39%, p < 0.001] (see Table 2).
But not, Pearson’s correlation coefficients was seen to be low (roentgen = -0.20 for males and r = -0.47 for women) in comparison to earlier degree one to utilized most other obesity research gadgets that have BF% [sixteen,18, 19]. Inside 1996, Gallagher et al. learned the connection anywhere between Body mass index and you will BF% and advertised thinking from r = 0.58 for men and you may r = 0.72 for women. Ilman et al. enjoys reported roentgen = 0.85 for males and you may r = 0.83 for ladies. Per study revealed a distinct BF% prediction model. It was in the past reported that, besides ages and you can gender, additional factors such as nationality, ethnicity, and religion can also help help the reliability away from good BF% prediction design [16,19-23].