Body Mass Index and Age Influence on Blood Pressure

Abstract

Background

Hypertension is one of the factors that predispose people to cardiovascular diseases. The increasing incidences of cardiovascular diseases emanate from the lifestyles that people have adopted in modern society such as physical inactivity and junk food. Some studies show that there is a link between hypertension and age and obesity. Hence, the objective of the study is to establish the influence of age, body weight, and body mass index on the occurrence hypertension among individuals.

Method

The study analyzed data of 160 participants (N =160) with 76 males and 84 females with their ages ranging from 15 to 95 (M = 48.5, SD = 16.6). Descriptive statistics and correlation analysis were used for exploring data. Subsequently, linear regression analysis and multiple regression analysis were used in ascertaining the influence of age, body weight, and body mass index on systolic blood pressure.

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Results

According to linear regression analysis, age, body weight, and body mass index individually explain 6.6%, 9.8%, and 39.4% of the variation in systolic blood pressure respectively. Multiple regression analysis indicates that age, body weight, and body mass index jointly explain 43.6% of the variation in systolic blood pressure among individuals.

Conclusion

Regression analysis reveals that age, body weight, and body mass index are statistically significant predictors of systolic blood pressure. Body mass index is the most influential predictor because it explains 39.4% of the variation in systolic blood pressure among the participants.

Introduction

Premise

Hypertension is a major public health issue in the United States and across the world because it predisposes people to cardiovascular diseases. Recent statistics indicate that cardiovascular diseases cause more than 1.6 million deaths in the United States annually, which forms 38% of deaths resulting from non-communicable diseases and 30% of all deaths (Ordunez, Prieto-Lara, Gawryszewski, Hennis, & Cooper, 2015). These statistics show that cardiovascular diseases constitute a major public health issue in the United States. Across the world, the trend of cardiovascular diseases is worrying as millions of people suffer from more than one form of these diseases. The increasing incidences of cardiovascular diseases emanate from the lifestyles that people have adopted in modern society such as physical inactivity and bad eating habits (Mungreiphy, Kapoor, & Sinha, 2011). Fundamentally, obesity predisposes people to hypertension because the accumulation of fats constricts blood vessels and restricts circulation of blood in the body. Moreover, age is a predictor of hypertension because the occurrence of cardiovascular diseases increases with the age of individuals. Therefore, the study aims to analyze data and examine the influence of age, body weight, and body mass index on the blood pressure among normal individuals.

Research Questions

    1. What is the influence of age on the blood pressure of individuals?
    2. What is the influence of body weight on the blood pressure of individuals?
    3. What is the influence of body mass index on the blood pressure of individuals?
    4. What is the collective influence of body weight, body mass index, and age on the blood pressure of individuals?

Null Hypotheses

H01: Age has no statistically significant influence on the blood pressure of individuals.

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H02: Body weight has no statistically significant influence on the blood pressure of individuals.

H03: Body mass index has no statistically significant influence on the blood pressure of individuals.

H04: Body weight, body mass index, and age have no statistically significant influence on the blood pressure of individuals.

Methodology

Research Design

The study used correlational research design in examining the relationship between the dependent variable (hypertension) and independent variables (gender, body weight, body mass index, and age). Given that these variables exist on a continuous (numeric) scale, the study employed the quantitative approach in the analysis of data.

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Sample Size

The study selected data that has variables of interest, namely, hypertension, gender, body weight, body mass index, and age. The sample size of the study is 160 participants with ages ranging from 15 to 95 years (M = 48.5, SD = 16.6). The study participants comprised 84 females and 76 males (N = 160). The participants were normal individuals who were selected randomly from the population and their details recorded for data analysis and inferences.

Statistical Analyses

The study performed descriptive analysis to explore the pattern and the trend of the data. Correlation analysis is a statistical test that the study used in exploring the relationship between the dependent variable (hypertension) and independent variables (gender, body weight, body mass index, and age). The correlation analysis is important in data analysis because correlation coefficient (r) provides the magnitude and the direction of the relationship between two variables (Field, 2013). Subsequently, the study used linear regression to ascertain the magnitude of influence of gender, body weight, body mass index, and age on hypertension. The linear regression analysis indicates how each independent variable influences the dependent variable (Macdonald, 2015). Ultimately, the study used multiple regression analysis in ascertaining the collective influence of gender, body weight, body mass index, and age on hypertension among individuals.

Results

Descriptive Statistics

Table 1.

Gender of Respondents
Frequency Percent Valid Percent Cumulative Percent
Valid Female 84 52.5 52.5 52.5
Male 76 47.5 47.5 100.0
Total 160 100.0 100.0

Table 2.

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Body Weight, Body Mass Index, Systolic Blood Pressure, and Age
N Minimum Maximum Mean Std. Deviation
Body Weight 160 45 120 79.06 15.590
Body Mass Index 160 18 42 26.38 4.114
Systolic Blood Pressure 160 120 148 129.85 6.322
Age 160 15 95 48.54 16.597
Valid N (listwise) 160
Distribution of Systolic Blood Pressure
Figure 1.

Correlation

Table 3.

Correlations
Age Body Weight Body Mass Index Systolic Blood Pressure
Age Pearson Correlation 1 .205 .210 .257
Sig. (2-tailed) .009 .008 .001
N 160 160 160 160
Body Weight Pearson Correlation .205 1 .675 .313
Sig. (2-tailed) .009 .000 .000
N 160 160 160 160
Body Mass Index Pearson Correlation .210 .675 1 .627
Sig. (2-tailed) .008 .000 .000
N 160 160 160 160
Systolic Blood Pressure Pearson Correlation .257 .313 .627 1
Sig. (2-tailed) .001 .000 .000
N 160 160 160 160

Linear Regression Analysis

Age and Systolic Pressure

Table 4.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .257a .066 .060 6.129

Table 5.

ANOVATest
Model Sum of Squares df Mean Square F Sig.
1 Regression 418.828 1 418.828 11.149 .001
Residual 5935.572 158 37.567
Total 6354.400 159

Table 6.

Coefficientsof Age
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 125.103 1.502 83.292 .000
Age .098 .029 .257 3.339 .001

Body Weight and Systolic Blood Pressure

Table 7.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .313 .098 .092 6.023

Table 8.

ANOVA Test
Model Sum of Squares df Mean Square F Sig.
1 Regression 622.718 1 622.718 17.166 .000
Residual 5731.682 158 36.276
Total 6354.400 159

Table 9.

Coefficientsof Body Weight
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 119.814 2.469 48.532 .000
Body Weight .127 .031 .313 4.143 .000

Body Mass Index and Systolic Blood Pressure

Table 10.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .627 .394 .390 4.939

Table 11.

ANOVA Test
Model Sum of Squares df Mean Square F Sig.
1 Regression 2500.511 1 2500.511 102.515 .000b
Residual 3853.889 158 24.392
Total 6354.400 159

Table 12.

Coefficientsof Body Mass Index.
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 104.418 2.542 41.077 .000
Body Mass Index .964 .095 .627 10.125 .000

Multiple Regression Analysis

Table 13.

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .660 .436 .425 4.794

Table 14.

ANOVATest of the Regression Model
Model Sum of Squares df Mean Square F Sig.
1 Regression 2769.588 3 923.196 40.175 .000
Residual 3584.812 156 22.980
Total 6354.400 159

Table 15.

Coefficients of Age, Body Weight, and Body Mass Index
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 104.005 2.564 40.571 .000
Age .055 .024 .145 2.346 .020
Body Weight -.089 .033 -.220 -2.685 .008
Body Mass Index 1.145 .126 .745 9.094 .000

Discussion

Descriptive Statistics

Descriptive statistics (Table 1) show that the participants (N = 160) comprise 76 males (47.5%) and 84 females (47.5%). Further descriptive statistics as shown in Table 2 show that the age of the participants ranges from 15 to 95 (M = 48.6, SD = 16.6) and their systolic blood pressure ranges from 120-148 (M = 129.9, SD = 6.3). The distribution (Figure 1) illustrates that most participants have their systolic pressure with the normal range. The body weight of the participants ranges from 45 to 120 (M = 79.1, SD = 15.6). The distribution of body weight indicates that most participants fall within the range of 65 and 95. The body mass index ranges from 18 to 42 (M = 26.4, SD = 4.1), which shows that most participants are overweight.

Correlation Analysis

Correlation analysis reveals that age, body weight, and body mass index have a positive relationship, which is statistically significant (p < 0.05), with systolic blood pressure among individuals. Table 3 shows that body weight and systolic blood pressure have a moderate positive correlation (r = 0.313), which is statistically significant (p = 0.000). The age has a weak positive correlation with systolic blood pressure (r = 0.257), which is statistically significant (p = 0.001). The analysis of the relationship between body mass index and systolic blood pressure indicates that they have a strong positive relationship (r = 0.627), which is statistically significant (p = 0.000).

Linear Regression Analysis

The regression model (Table 4) shows that age has a weak positive relationship with the systolic blood pressure (R = 0.257). According to Mungreiphy, Kapoor, and Sinha (2011), age has a positive association with blood pressure for the risk of hypertension increases with age. Essentially, age explains 6.6% of the variation in systolic blood pressure (R­­­2 = 0.066). The regression model used predicting the relationship is statistically significant, F(1,158) = 11.149 = 001). The coefficients’ table (Table 6) depicts a regression equation, which predicts that a year increase age results in an increase in systolic blood pressure by 0.098. Therefore, the regression test rejects the null hypothesis that age has no statistically significant influence on the blood pressure of individuals (R = 0.257, R2 = 0.066, p = 0.001).

Moreover, the regression model (Table 7) indicate that body weight and systolic blood pressure have a moderate positive relationship (R = 0.313), which is statistically significant (p = 0.000). R-square reveals that body weight is a statistically significant predictor because it explains 9.8% of the variation in systolic blood pressure (R2 = 0.098, p = 0.000). Roka, Michimi, and Macy (2015) notes that body mass index effectively predicts the occurrence of hypertension among individuals. The regression model used in explaining the influence of body weight on the systolic blood pressure is statistically significant, F(1,158) = 17.166 = 000). The coefficients table of body weight indicates that a unit increase in weight causes an increase in systolic blood pressure by 0.127. Hence, regression analysis rejects the null hypothesis that body weight has no statistically significant influence on the blood pressure of individuals (R = 0.313, R2 = 0.098, p = 0.000).

Outstandingly, the body mass index has a strong relationship with systolic blood pressure among the participants (R = 627). R-square value shows that body mass index explains 39.4% of the variation in systolic blood pressure (R2 = 0.394). Statistical analysis of the regression model is statistically significant in predicting the influence of body mass index on systolic blood pressure, F(1,158) = 102.515, p = 0.000). The table of coefficients predicts that a unit increase in body mass index causes an increase in systolic blood pressure by 0.964. In this view, the regression analysis rejects the null hypothesis that body mass index has no statistically significant influence on the blood pressure of individuals. Thus, the findings are consistent with the earlier findings of Roka, Michimi, and Macy (2015), which show that body mass index is a significant factor that influences the occurrence of hypertension among individuals.

Multiple Regression Analysis

The model summary (Table 13) shows that there is a strong relationship between systolic blood pressure and age, body weight, and body mass index of the participants (R = 0.66). The R-square value shows that age, body weight, and body mass index jointly explain 43.6% of the variation in systolic blood pressure among the participants. Numerous studies have found out that age, body weight, and body mass index are considerable predictors of hypertension among individuals (Mungreiphy, Kapoor, & Sinha, 2011; Roka, Michimi, & Macy, 2015; Jerant, & Franks, 2011). The regression model that effectively predicts the influence of age, body weight, and body mass index on systolic blood pressure is statistically significant (p = 0.000). The table of coefficients provides regression equation, which predicts the influence of age, body weight, and body mass index on systolic blood pressure. The regression equation predicts that a unit increase in each independent variable, namely, age, body weight, and body mass index causes 0.055, -0.089, and 1.145 changes in systolic blood pressure respectively among the participants. Therefore, the outcome of multiple regression analysis rejects the null hypothesis that body weight, body mass index, and age have no statistically significant influence on the blood pressure of individuals.

Limitation of the study

The first limitation of the study is that it used a small sample, which does not adequately represent the target population. In this view, the findings have low external validity because they are only relevant and applicable to the sampled individuals. The second limitation of the study is the validity of the data because the study obtained it as secondary data. In this case, the findings have low internal validity for it cannot verify the veracity of the data used.

References

Field, A. (2013). Discovering statistics using IBM SPSS statistics. London: SAGE Publication.

Jerant, A., & Franks, P. (2011). Body Mass Index, Diabetes, Hypertension, and Short-Term Mortality: A Population-Based Observational Study, 2000-2006. Journal of the American Board of Medicine, 25(4), 422-431.

Macdonald, S. (2015). Essentials of Statistics with SPSS. Raleigh: Lulu Publisher.

Mungreiphy, K., Kapoor, S., & Sinha, R. (2011). Association between BMI, Blood Pressure, and Age: Study among Tangkhul Naga Tribal Males of Northeast India. Journal of Anthropology, 2011(748147), 1-6.

Ordunez, P., Prieto-Lara, E., Gawryszewski, V., Hennis, A., & Cooper, R. (2015). Premature mortality from cardiovascular disease in the Americas: Will the goal of a decline of ‘25% by 2025’ be met? PLOS ONE, 10(10), 1-11.

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

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