According to doctors in the United States, one out of every four women dies from heart disease. Women are more likely than men to develop heart disease, such as Coronary Microvascular Disease (MVD) and Broken Heart Syndrome. The current research investigates some of the attitudes, opinions, and experiences of heart disease among women. The P= 0.016 demonstrates that female participants' BMI differs from men's. P-value =0.03 suggests that information about the disease's incubation period without detection is substantially connected with women's attitudes toward the disease as the leading cause of death. The regression analysis P –value =0.000 implies that the number of times exercised per week by women significantly reduces with age.
Introduction
In the United States, medics argue that one in every four women dies from the heart disease. The forms of heart diseases that are more common with women as compared to men include the Coronary Micro-vascular Disease (MVD) and the Broken Heart Syndrome. The MVD attacks the tiny heart arteries while the Broken Heart Syndrome involves emotional stress that leads to severe though often short-lived heart muscle failure. There are number of symptoms and factors that have been associated with the heart diseases in women. Some of the symptoms of heart disease among women include the uncomfortable pressure or pain in the center of chest that last for a while and the goes, shortness of breath, cold sweat, nausea or even discomfort at the back of the neck. The factors that contribute to heart disease among women include diabetes, smoking, menopause, or even inactivity just to mention a few. The current paper focuses on investigating on what are some of the attitudes, perspectives and experiences of the heart diseases among women.
Statement of the hypotheses
Null hypothesis: The BMI of female participants equals the BMI of men.
Alternative hypothesis: The BMI of female participants differs from the BMI of men.
Null hypothesis: The attitude towards the heart disease as killer disease is independent on participant knowledge of its incubation.
Alternative hypothesis: The attitude towards the heart disease as killer disease is dependent on participant knowledge of its incubation.
Null hypothesis: The age of women does not affect their frequency of exercise. Alternative hypothesis: The age of women affects their frequency of exercise
Null hypothesis: The heart disease prevention choices are independent on gender.
Alternative hypothesis: The heart disease prevention choices are associated with gender.
All the above stated hypotheses are tested at the 0.05 level of significance. The decision rule is to reject the null hypothesis whenever the P-value < 0.05.
Literature review.
Maas and Appelman (2010) conducted a study with objective of summarizing some of the pertinent issues to consider when conducting a diagnosis as well as treatment of heart disease in women. In the review, Maas and Appelman (2010) explains that though the cardiovascular disease is the major cause of death it may take between 7 and 10 year more in women as compared to develop. The study findings indicates that women are less presented in the clinical trials which may even explained the less aggressive treatment approaches to women than in men. Moreover, women self-awareness and their ability to identify possible heart disease risk factor is an issue that requires much better attention.
Merz and Cheng (2016) conducted similar study with aim of identifying sex-specific patterns of cardiovascular and role-played by ageing. The study finding indicates that women have greater prevalence as compared to men of age-related heart failure. To support this notion the study explains that vascular functioning and structure tends to change with sex during ageing. In addition, Merz and Cheng (2016) explain that hormonal and non-hormonal elements have the ability to create the cardiovascular sex-specific variation during the ageing process. This explains further, why women in menopause are likely to suffer heart failures that men at the same age.
Methodology
Sampling and instrumentation
A cross-sectional survey design was used to collect data from a conveniently selected sample of thirty-five participants who committed to filling a mail-survey questionnaire by providing their personal email address. The informed consent involved explaining to each participant the need for information as well guidelines of maintaining anonymity throughout the study. After one week, the participants were contacted and only 31 confirmed their availability to complete the survey. After confirmation, the survey questionnaire was emailed to each of the 31 participants though only thirty were able to send back completed duly filled questionnaires. After receiving the questionnaires, the data was entered into the SPSS software for cleaning and analysis. From the data, seven variables were recorded based on research questions.
Description of the variables
The enumerated variables include the age, gender, the body mass index, attitude towards heart disease among women, incubation knowledge about heart disease, prevention or risk reduction choice, and frequency of exercise in a week. Age, body mass index and frequency of exercise in a week are quantitative variables with scale (interval/ratio) level of measurement. Gender, attitude towards heart disease as number one killer among women, and incubation knowledge about the heart disease are categorical nominal dichotomous variables while prevention or risk reduction choice is categorical nominal variable.
Data analysis
The data analysis was conducted using the SPSS software based on the above stated hypotheses. Both explorative and inferential techniques were used to analyze the data. A pie chart and percentage frequencies were use do determine proportion of each heart disease prevention choice with the sample. For inferential analysis, an independent sample t-test was conducted to determine whether the Body Mass Index of female and male participants vary from each other. The chi-square test of association was used to test independence between attitude towards heart disease among women and incubation knowledge about heart disease as well as between prevention or risk reduction choice and gender of the participant. A simple linear regression was conducted to determine whether weekly frequency exercise among women is affected by age.
Results
The following section provides graphical summaries and the results of the hypothetical analysis:
Figure 1: Pie chart for the best choice of the prevention or reducing risk of heart disease
Figure 1 indicates that most of the participants (30% of participants) opted to reducing stress as the best way to prevent or reduce risk of heart disease among women. Exercise and reducing cholesterol intake came second each with 26.67% while only 16.67% choose quitting smoking as the best way to reduce or prevent the risk of heart disease among women. The tables in the appendices indicate the SPSS output for the hypotheses tests. Appendix 1 provide the result of the independent samples t-test which indicates the BMI for females has (M=25.46, SD= 4.231) and BMI for males has (M= 22.04, SD=2.928). The test statistic t (28) =2.574, P= 0.016. Since p-value < 0.05, the null hypothesis is rejected. The 95% confidence interval for mean difference is (0.698, 6,142).
Appendix 2 indicates the result of chi-square test for independence between attitude towards heart disease among women and incubation knowledge about heart disease. The chi-square statistic χ2(1) , n= 30, P-value =0.03 implies the null hypothesis attitude towards the heart disease as killer disease is independent on participant knowledge of its incubation is rejected. Similarly, appendix 3 involves chi-square test but this time for association between prevention or risk reduction choice and gender of the participant. The result test statistic χ2(3) , n= 30, P-value =0.230 which implies that null hypothesis cannot be rejected. Finally, appendix 4 indicates the result of the regression analysis for effects of age on frequency of exercise among women. The overall model statistic F (1, 13) = 102.182, P –value =0.000 which implies the rejection of the null hypothesis. The R-square implies that the model contributes 88.7% of variation in time exercise per week. The regression coefficient indicates every additional year of age of women reduces exercise frequency by .114 times.
Limitations
The use convenient sampling in the choosing the participant was likely to introduce a selection bias in the study. Though the study used the Pearson chi-square, some assumptions of the test were violated since not all observed counts met the minimum threshold. The study did not identify any inclusion criteria of the participant apart from convenience.
Conclusions
In closure, the study result indicated that the BMI was significantly different between men and women at alpha level = 0.05. The fact that the difference was positive as provide by the confidence intervals indicates that the female BMI was higher than that of males. The chi-square test of association indicates that the attitude toward the disease as the number one killer among women is significantly associated with the knowledge about its incubation period without detection. On the contrary, the same test indicates then choice prevention or reducing risk technique is not associated with gender. Finally, the number of times exercised per week by women significantly reduces with age. The high BMI in women may explain the high rates of heart disease in women than men. Similarly, reduced exercise as women ages may contribute to accumulation of body fats causing heart disease since it well known that body exercise helps in burning excess calories and boosting blood circulation.
References
Maas, A., & Appelman, Y. (2010). Gender differences in coronary heart disease. Netherlands Heart Journal, 18(12), 598-603. doi:10.1007/s12471-010-0841-y
Merz, A. A., & Cheng, S. (2016). Sex differences in cardiovascular ageing. Heart, 102(11), 825- 831. doi:10.1136/heartjnl-2015-308769
APPENDICES
Appendix 1: Independent t-test
Group Statistics
Gender
N
Mean
Std. Deviation
Std. Error Mean
Body Mass Index
Female
15
25.4600
4.23148
1.09256
Male
15
22.0400
2.92790
.75598
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Body Mass Index
Equal variances assumed
3.735
.063
2.574
28
.016
3.42000
1.32861
.69847
6.14153
Equal variances not assumed
2.574
24.906
.016
3.42000
1.32861
.68315
6.15685
Appendix 2: chi-square test
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Exact Sig. (2-sided)
Exact Sig. (1-sided)
Pearson Chi-Square
8.623a
1
.003
Continuity Correctionb
6.537
1
.011
Likelihood Ratio
9.124
1
.003
Fisher's Exact Test
.007
.005
Linear-by-Linear Association
8.335
1
.004
N of Valid Cases
30
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.13.
b. Computed only for a 2x2 table
Symmetric Measures
Value
Asymp. Std. Errora
Approx. Tb
Approx. Sig.
Nominal by Nominal
Phi
.536
.003
Cramer's V
.536
.003
Interval by Interval
Pearson's R
.536
.151
3.361
.002c
Ordinal by Ordinal
Spearman Correlation
.536
.151
3.361
.002c
N of Valid Cases
30
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
Appendix 3: chi-square test
Chi-Square Tests
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
4.311a
3
.230
Likelihood Ratio
4.499
3
.212
Linear-by-Linear Association
1.065
1
.302
N of Valid Cases
30
a. 8 cells (100.0%) have expected count less than 5. The minimum expected count is 2.50.
Symmetric Measures
Value
Asymp. Std. Errora
Approx. Tb
Approx. Sig.
Nominal by Nominal
Phi
.379
.230
Cramer's V
.379
.230
Interval by Interval
Pearson's R
-.192
.180
-1.033
.310c
Ordinal by Ordinal
Spearman Correlation
-.211
.183
-1.145
.262c
N of Valid Cases
30
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
Appendix 4: Regression analysis
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Gender = Female (Selected)
1
.942a
.887
.878
.64375
a. Predictors: (Constant), Age in years
ANOVAa,b
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
42.346
1
42.346
102.182
.000c
Residual
5.387
13
.414
Total
47.733
14
a. Dependent Variable: Time exercise per week
b. Selecting only cases for which Gender = Female
c. Predictors: (Constant), Age in years
Coefficientsa,b
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
8.290
.505
16.407
.000
Age in years
-.114
.011
-.942
-10.108
.000
a. Dependent Variable: Time exercise per week
b. Selecting only cases for which Gender = Female
Survey questionnaire
Survey on Cardiovascular Disease in Women
What is your age in years?
Gender
Female
Male
What is your Body Mass Index?
Had you known that heart disease is the number one killer of women?
Yes
No
Do you know that heart disease can develop over many years and go undetected?
Yes
No
Which of the following do you think is the best way of prevent or reduce risk heart disease in women?
Exercise
Reducing stress
Reducing cholesterol
Quit smoking
How many times do you exercise in a week?
