BMI Statistical Report and the Importance of Determining the Factors Affecting BMI

 

 

 

BMI Statistical Report

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Abstract

The obesity prevalence is higher than ever before at the global stage and as the incidences increase there are concerns that obesity will pose a public health problem. The body mass index (BMI) is one of the common measures of weight levels where 24.9 kg/m² – 30 kg/m² indicates an overweight person and more than 30 kg/m² is obesity. There was an analysis of BMI among males and females where the mean and deviation (SD) of BMI indicates the range of values of the variables, while there are no significant differences between the BMI of males and females. Percentages of those overweight and obese (60%) 24/40 higher than those of females at (47.5%) 19/40, but 15% (6/40) males are obese compared to 17.5% of the females. The average BMI of males was 26.0 (95% CI: 24.93 - 27.06), is higher than that of females at 25.74 (95% CI: 23.83 - 27.65). The data obtained indicated that BMI varies by age, sex, blood pressure, cholesterol levels, pulse rate, height, and weight.

 

 

Introduction

Obesity is a chronic disease, characterized by an increase in body fat and is associated with greater risk to health. The rates of obesity have increased in last decades globally. The body mass index (BMI) is one of the indicators of weight levels, and being overweight is having a body mass index of 25 kg/m² or more for adults, those with a BMI below 18.5 kg/m² are underweight, while healthy weight is 18.5 -24.9 kg/m². The BMI is calculated by dividing the weight in kilograms and the square of the height in meters. There are differences in weight levels depending on gender, weight, and height, as men tend to be taller and weigh more than women (Roka, Michimi & Macy, 2015). Nonetheless, even when one is considered normal weight, there are instances of those with high body fat, while the BMI may categorize healthy people as being overweight or obese and especially bodybuilders (Karelis et al., 2015). However, the BMI remains a good indicator of the weight levels.

There is a combined sample of 80 subjects made up of (N=40) males and (N=40) females, and when conducting the t-test, an alpha of .05 was used. The data from the National Center for Health Statistics (NCHS) contains various variables, where the age range for females is 12-59 years, and 17-73 for males. Data on height and weight have been used to estimate the BMI and whether the weight levels are normal, below or over the cutoff point and partly explains the weight differences (Barte et al., 2014). The systolic blood pressure (SBP) in mm Hg is [95-153] for males and [89-181] females, while diastolic blood pressure (DBP) [44-87] for males and [41-102] mm Hg] for females.

In testing hypotheses, constructing a confidence interval (CI) estimates the means and standard deviation of the variables are utilized to estimate the value of the population parameters, and is associated with the is associated with the 95% confidence level. The range is the mean ±the standard deviation. The t-Test: Paired two samples for means were conducted using MS Excel with the result of the one tail and two tails for the BMI of the males and females. For the two samples, there was an assumption that the assumed population means the difference is at 95% significance level. In the results, there was no hypothesized mean, but the result included test statistic, the P-values for the one-tailed test and two-tailed test, and the critical values for the one-tail and two-tails. The assumption of unequal variance is made since there are two independent samples, even as the assumption of equal variance is linked with normality.

Being overweight and obese is responsible for the increasing burden of diabetes, cardiovascular diseases, high blood pressure and even some cancers (Veronese et al., 2016). Data will focus on the body mass index for a random sample of 40 male and 40 female subjects, and the aim is to compare the BMI values for both male as and females. Overweight and obesity are associated with an energy imbalance between calories consumed and calories expended, while a decrease in physical activities because of sedentary lifestyles increases the risk of obesity.

Methods

Descriptive methods

When testing whether the mean BMI of men is equal to that of women, the factors affecting the BMI are considered to establish whether there is a link between the predictor variables and the BMI in both males and females and differences based on gender (Di Angelantonio e al., 2017). To achieve, this data analysis employed inferential and descriptive analysis using both SPSS and MS Excel where the mean, median, mode, range and standard deviations were calculated, and histograms created to determine whether there were outliers. This was necessary to assess the central tendency of the variables, and since the data is precise, this will help in understanding how the BMI levels in males and females differ when looking at the other factors affecting weight levels.

Explanation

For this study, data were obtained from the National Center for Health Statistics (NCHS on both male and female BMIs, plus twelve other variables that are likely to affect weight. This BMI can be explained by the values ​​of weight and height, which were provided for each of the individuals. Among the females, three people below year 18 are 12, 16 and 17 years, and they were two males who were 17 years of age. Since there were few below 18, and there is no need to adjust the sample because there is no age bias as is the case when the sample is excluded from the child population. The BMI categorizations are low weight, normal weight, overweight and obese.

Analysis

The predictor variables of this study are height, weight, waist, pulse rate, systolic and diastolic blood pressure, cholesterol levels, upper leg length, elbow breadth, wrist breadth, and arm circumference in cm. Since the aim is to analyze between the body mass indexes (BMI) values of men and women, the independent variable of this study is gender either male or female, and BMI, which indicates obesity is the dependent variable, in the body status classification there is a determination of the number of people with a BMI above 25 kg/m². The systolic blood pressure (SBP levels for males is (M= 119, S.D= 10.5) and diastolic blood pressure (DBP) (M= 73.2, S.D= 9.13) while for females the SBP is (M= 111, S.D= 17.1) and DBP is (M= 67.4, S.D= 11.6)

Since there is an obesity program to be implemented, information on differences in BMI between males and females will provide insights on the effectiveness of interventions and whether there is a need to integrate more nutrient options to address obesity and improve health outcomes. For all the variables in the project, there are objective outcome measures. Williams et al. (2015) focused on abdominal obesity to evaluate the risk of health complications highlighting that females were more at risk of obesity as indicated by the waist circumference.

Results, conclusions, and recommendations

The mean BMI is 26 and 25.7 for males and females, respectively, while the median is 26.2 and 23.9, while multiple modes exist and the smallest value shown is 23.8 and 19.6 for males and females. The waist circumference (WC) in cm is 66.7 to 126.5 in females, while this ranges from 75.2 to 108.7 in males. In case of height (inches), this is between 57 and 68, and 94.3 and 255.9 pounds in females compared to a height of 61.3 to 76.2 and 119.5 to 237.1 lb in males. The sample collected was composed of 80 subjects in two groups of equal sizes, but different standard deviations. The age range that comprised of the analyzed sample between 12 and 59 years for female subjects, compared to 17 and 73 years for males. Age is the most unmodifiable cardiovascular risk factor, and the blood pressure increases with age, especially, the systolic pressure (Rapsomanik et al., 2014).

In the case of male BMI, the quartiles are Q1 =23.575, Q3= 27.700, IQR= 4.125 while the lower and upper limits are 17.38 and 33.88 and there were no outliers in the male BMI since the range was 19.6 to 33.2 (95% CI: 24.93 - 27.06). For Female BMIs the quartiles are Q1=20.825, Q3= 29.55, IQR= 8.725 and the range of values lie between 7.738 and 42.638, and there was one outlier 44.9 (95% CI: 23.83 - 27.65). 15% of the males and 17.5% of the females are obese. In the t-test: paired two sample for means, the p< 0.05 =0.4117 (one-tailed), p< 0.05 (two-tailed) = 0.8233, and the result is not significant at p < .05 BMI is a useful indicator of health and nutrition status, while height, weight, waist, pulse rate, systolic and diastolic blood pressure, cholesterol levels, upper leg length, elbow breadth, wrist breadth, and arm circumference are useful to assess changes in the BMI. The risk of obesity indicated by increased weight and the physical conditions indicate the likelihood of being overweight and obese. Focusing on lifestyle changes, including an emphasis on proper dieting will reduce the risk of obesity and nutrition program in health intervention will improve health outcome.

BMI does not differentiate between body fat and muscle, and considering other variables besides height and weight provides more insights into the risk of being obese. For instance, there was a link between the waist circumference and the BMI, and the cholesterol levels differed between males and females implying that adiposity is useful to determine those at risk of obesity. There is a relationship between BMI and physical characteristics, highlighting that decreased by bodyweight for those overweight and obese will improve health outcomes, and there should be a greater decrease in weight levels in males, who are more likely to be obese. This would lose reduce and waist circumference, which is associated with a lower risk of developing high blood pressure, diabetes and cardiovascular comorbidities.

The importance determines the factors affecting the BMI and differences between male and female subjects be that it helps to formulae health practices and policies targeting reduced obesity levels in the population, as well as in the clinical area. This would help to control the health consequences associated with obesity, and it is a realistic goal to focus on interventions in the short and medium term to improve long-term benefits. People most at risk of cardiovascular complications are overweight or obese, the more significant the reduction in body weight, the reduction in body mass index and the greater the health benefits at the cardiovascular level.

 

References

Barte, J. C., M., Veldwijk, J., Teixeira, P. J., Sacks, F. M., Bemelmans, W. J., & E. (2014). Differences in weight loss across different BMI classes: A meta-analysis of the effects of interventions with diet and exercise. International Journal of Behavioral Medicine, 21(5), 784-793

Di Angelantonio, E., Bhupathiraju, S. N., Wormser, D., Gao, P., Kaptoge, S., de Gonzalez, A. B., ... & Lewington, S. (2016). Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. The Lancet, 388(10046), 776-786.

Fung, T. T., Pan, A., Hou, T., Chiuve, S. E., Tobias, D. K., Mozaffarian, D., ... & Hu, F. B. (2015). Long-Term Change in Diet Quality Is Associated with Body Weight Change in Men and Women–3. The Journal of nutrition, 145(8), 1850-1856.

Karelis, A., Messier, V., Suppere, C., Briand, P., & Rabasa-Lhoret, R. (2015). Effect of cysteine-rich whey protein (Immunocal®) supplementation in combination with resistance training on muscle strength and lean body mass in non-frail elderly subjects: A randomized, double-blind controlled study. The journal of nutrition, health & aging, 19(5), 531-536.

Rapsomaniki, E., Timmis, A., George, J., Pujades-Rodriguez, M., Shah, A. D., Denaxas, S., . . . Hemingway, H. (2014). Blood pressure and incidence of twelve cardiovascular diseases: Lifetime risks, healthy life-years lost, and age-specific associations in 1?25 million people. The Lancet, 383(9932), 1899-1911.

Roka, R., Michimi, A., & Macy, G. (2015). Associations between hypertension and body mass index and waist circumference in U.S. adults: A comparative analysis by gender. High Blood Pressure & Cardiovascular Prevention, 22(3), 265-273.

Veronese, N., Li, Y., Manson, J. E., Willett, W. C., Fontana, L., & Hu, F. B. (2016). Combined associations of body weight and lifestyle factors with all-cause and cause-specific mortality in men and women: a prospective cohort study. BMJ, 355, i5855.

Williams, R. L., Wood, L. G., Collins, C. E., & Callister, R. (2015). The effectiveness of weight loss interventions–is there a difference between men and women: a systematic review. Obesity reviews, 16 (2), 171-186.

 

 

Appendix

Male

BMI