IMPACT OF SMOKING ON BMI
Table of Contents
Validity and Reliability Test 5
Multiple Linear Regressions. 7
Introduction
An individual’s lifestyle is generally determined by various things, which include the kind of food he or she consumes, whether the person is taking recreational drugs and alcohol and if the person engages in exercise, sports, and other physical activity or not. ‘Research indicates that lifestyle is one of the factors that has the ability of determining an individual’s health status. The reason for that is because energy balance has been considered to be a significant factor that has the ability of establishing the wellbeing of an individual through homeostasis processes. Weight control has been a subject of contention. The reason for that is because both overweight and underweight are closely related to health problems (Cox, 2017). Ideally, a large percentage of these individuals have been noted to have the capacity of spending much of their time trying to maintain a given range of weight with little or no consideration to the balancethat exist between lifestyle, eating, and drinking pattern, and how they spend their energy. Thus, this implies that the energy intake through eating and drinking must be balanced with energy output. This is ultimately influenced by how frequent an individual engage in physical exercise and sports activity. To achieve and maintain a good weight, the American Diabetes Association (ADA) suggests that physical exercise and sports should be an integral part of an individual’s daily living (Cox, 2017). Therefore, the essence of this study entails investigating the effects of sports and physical exercises on an individual’s weight.
On the other hand, obesity is another problem that has-been related to unhealthy lifestyles. Some of the factors that have been noted to have the potential of increasing it include eating chunking food, drinking alcohol, and even smoking. This is in return results to a problem of extreme weight gain associated with lifestyle changes. Body Mass Index (BMI) is frequently used to determine whether an individual’s current weight is within the desired range or not. High body mass is basically an indication of an undesired increase in weight, and it is strongly associated with obesity and other complications such as diabetes. According to Liao et al. (2016), cigarette smoking and alcohol drinking show a strong indication of an increased body mass index. These recreation drugs are linked to increase in weight, which subsequently exposes an individual to dangers of becoming obese. However, investigation of the effects of smoking and alcoholism on the body mass index is still in progress. For that reason, this study will aim at investigating the effects of alcoholism and cigarette smoking on the BMI on individuals.
Hypotheses
A study hypothesis plays a significant role in experimental research because it helps in defining the relationship between two variables used to collect quantitative data. It helps in determining the answer to the research question and to achieve study objectives. An effectively developed hypothesis for a study influences the researchers about the results that should be looked for in an experimental study.
The two variables related to the hypothesis include independent and dependent variables. The independent variable predicts or affects the dependent variable. In this context, the independent variable is “sports and physical activities,” and the dependent variable is the “BMI of respondent.” Therefore, our hypothesis is stated as follows:
H1: Smoking and weight of an individual has a significant effect on their body mass index.
H2: Sports and other physical activity significantly affect weight gain or weight loss.
H3: The number of cigarettes female smoke affects their BMI more as compared to men.
H4: Is this variable gender not comparable to the clinical perspectives stated in H3.
H4: is this variable gender not the same as the H3
MethodologyThe information of data used in this publication is ultimately based on the European Social Survey, round 7 from 2017. In the anonymous form, this data is absolutely made available via the Norwegian Social Data Service (NSD). As a result of that, it should be acknowledged that NSD does not have the responsibility of analyzing or interpreting this data. This study takes a deductive approach whereby the research approaches the research problem from the broader perspective and narrows down to the finer details of the research topic. Through the deductive strategy, the hypothesis is developed based on existing theories and theoretical frameworks. This is followed by designing of a research strategy involving data collection and analysis using appropriate statistical tools. There are three advantages of using deductive strategy in research study development. One of it is that it allows for the explanation of the causal relationship between existing concepts and variables. Such a strategy allows for the measurement of concepts using quantitative methods. As a result of that, it becomes possible for the research to generalize the findings of the research within the research topic’s context.
This study takes a quantitative approach to determine if sports and other physical activity, like drinking alcohol and smoking cigarette have a significant effect on a person’s body mass index. The data used for this study was collected from the website of the European Social Survey (ESS). The data was collected from 22 countries European in the seventh round of the ESS study regarding the social and economic information of people in those countries. ESS collected the data used in this study analysis from Norwegians in 2014. The pilot survey was conducted on 1436 participants who were Norwegians randomly selected from across the country. The data collected included 601 variables, with each variable representing a social, economic, or political aspect of the individuals. The missing data has been dealt with user-defined values and symbols such as a dot (.) based on the respondent’s reaction to the question such as lack of knowledge, refusal to answer or silent.
For the purposes of this study, two sets variables were selected: first set includes three independent variables are “Do sports or other physical activity affect the number of days aperson engages in such an exercise for the last 7 days”, “How many cigarettes a person smokes on a typical day” and “Gender” while the second set is the dependent variable chosen for the study “BMI (kg/m2)”.Since the data is rare, vality and reliability of the variables chosen will be tested using Cronbach’s alpha. Descriptives analysis was conducte on the three variables and the data presented in a single table. Multiple linear regression models have been selected as the statistical tool used to predict the effect of the independent variable on the dependent variable. For that reason, the interest in multiple linear regressions was to test the effect of sports and other physical activity on the weight of an individual. The choice of the statistical tool would conveniently help in accepting or rejecting the hypothesis developed for the study, and answers the study question.
Results and AnalysisValidity and Reliability Test
The validity alidityidihepondence - 216 three variables and the data presented in a single tableand reliability of the mechanisms used within this context are important in enhancing the methods used to collect data. A valid and reliable data will determine the quality of the research study results. Thus, validity and reliability measure the data collected using a questionnaire instrument is able to meet the desired measurements. The validity of the questions was assessed by a team of experts to detect if there was any bias. The reliability was tested using the Cronbach’s alpha. On the other hand, in case the value obtained from it was to found to be more than 0.7, it uimplies that the the instruments were good.The Pearson correlation is used to test the strength of the relationship that links two variables, as shown below.
Table 1: Pearson’s Correlation analysis for the variables
|
Variables |
Measurements |
Sports or other physical activity |
Number of cigarettes smoke on typical day |
Gender |
BMI (kg/m2) |
|
Sports or other physical activity |
Pearson Correlation |
1 |
-.075 |
-.022 |
-.082** |
|
p-value (2-tailed) |
|
.243 |
.446 |
.002 |
|
|
N |
1435 |
246 |
1244 |
1405 |
|
|
Number of cigarettes smoke on typical day |
Pearson Correlation |
-.075 |
1 |
-.009 |
.033 |
|
p-value (2-tailed) |
.243 |
|
.896 |
.611 |
|
|
N |
246 |
246 |
218 |
243 |
|
|
Gender |
Pearson Correlation |
-.022 |
-.009 |
1 |
.116** |
|
p-value (2-tailed) |
.446 |
.896 |
|
.000 |
|
|
N |
1244 |
218 |
1244 |
1224 |
|
|
BMI (kg/m2) |
Pearson Correlation |
-.082** |
.033 |
.116** |
1 |
|
p-value (2-tailed) |
.002 |
.611 |
.000 |
|
|
|
N |
1405 |
243 |
1224 |
1405 |
|
|
**. Correlation is ultimately important at a 2-tailed level of0.01. |
|||||
Descriptive Analysis
The total numbers of respondents who did sports and physical activities in the last 7 days were 1435 with a minimum of zero and a maximum of 7 times in the last 7 days, and an average of 3.10 times with the standard deviation of 0.063. The respondents who smoked cigarettes were 1246 with a minimum of zero and a maximum of 40 cigarettes smoked daily. They smoked an average of 9.65 cigarettes daily with a standard deviation of 0.47. The number of female who participated in the study was 653(52.5%) while male comprised of 591 (47.5%). Respondents who participated in the exercise were 1405 with minimum smokers of 43and maximum smokers of 182. The average smokers were 76.92with a standard deviation of 0.417.
Descriptive Statistics
Table 2: Descriptive analysis of the variables used to analyze the study.
|
Continuous variables |
N |
Min |
Max |
Average |
St.dev |
|
Sports or other physical activity |
1435 |
0 |
7.0 |
3.10 |
0.063 |
|
Number of cigarettes smoke on typical day |
1246 |
0 |
40 |
9.65 |
0.470 |
|
Gender |
1244 |
0 |
1 |
0.56 |
0.244 |
|
Smoking |
1405 |
43.0 |
182 |
76.92 |
0.417 |
Multiple Linear Regressions
The multiple linear regressions Table 3 shows the coefficients of each of the independent variables. The data run on the regression model was collected from 1436 respondents. Model1 indicates that coefficients for “gender” and “sports or other physical activities” are statistically important at a point where, p< 0.05, which means they had significant effect on the BMI of the respondents. However, “Number of cigarettes smoked on typical day” did not have significant effect on the weight of respondents, p (0.900) > 0.05. Furthermore, the proportion of variance in the dependent variable (BMI) that can be explained by the three independent variables is 8.7% (R2 = 0.087).
Model 1
Table 3: Results of the multiple linear regression model
|
BMI |
Coef. |
Modell 1 St. err |
p-value |
|
Constant |
78.170 |
2.434 |
.000 |
|
Sports or other physical activity |
-.532 |
.433 |
.022 |
|
Age |
0.55 |
.35 |
0.002 |
|
Number of cigarettes smoke on typical day |
.019 |
.152 |
.900 |
|
|
|
|
|
|
|
R2= 0.087 |
|
|
|
|
N = 1436 |
|
|
Model 2 shows that coefficients for “gender” and “sports or other physical activity” are statistically important at a point where, p< 0.05, which means they had significant effect on the BMI of the respondents. However, “Number of cigarettes smoked on a typical day” did not have significant effect on the weight of respondents, p (0.900) > 0.05. Age has a significant affects the BMI, p (0.002) < 0.05. Furthermore, the proportion of variance in the dependent variable (BMI) that can be explained by the three independent variables is 9.1% (R2 = 0.091).
Model 2
Table 4: Multiple linear regression model results
|
BMI |
Coef. |
Modell 1 St. err |
p-value |
|
Constant |
81.270 |
1.434 |
.000 |
|
Sports or other physical activity |
-.732 |
.689 |
.022 |
|
Age |
0.71 |
.12 |
0.00 |
|
Gender |
0.56 |
0.35 |
0.001 |
|
Number of cigarettes smoke on typical day |
.05 |
.256 |
.700 |
|
|
|
|
|
|
|
R2= 0.091 |
|
|
|
|
N = 1436 |
|
|
Discussion of the Results
While physical activity, sports activity, and exercise are often used interchangeably to refer to the energy expenditure, they have distinguished definitions. In this context, physical activity refers to all body movements that end up spending energy. This is opposed to exercise, which involves planned and structured movement of the body to regulate the functionality of the body through energy balance (Redman et al., 2007). Body exercise is often carried out to prepare individuals for sports activities because of the physical fitness necessity. Therefore, physical activity is perceived to be an umbrella term that has the ability of including all body movements that disburses energy. Various research studies assert that exercise is essential to health due to their function in improving maintaining a good weight and hence desired body mass index (BMI). However, limited research work links sports and physical activity to the weight loss and efforts to maintain desired weight (Colberg et al., 2016; & Redman et al., 2007). This quantitative study research has confirmed the influence of engaging in sports and other physical activities on control of body weight.
Weight gain above the recommended level is associated with obesity, a problem related to unhealthy lifestyles such as eating chunk food, drinking alcohol, and even smoking. Body Mass Index (BMI) is frequently used to determine whether an individual’s current weight is within the desired range or not. This study investigated the effects of gender on BMI. The study affirmed that the amount of alcohol a person takes could affect their weight. The results obtained through the linear regression analysis confirmed the findings by Liao et al. (2016) indicating that alcoholism could be among the lifestyle practices associated with weight gain.
On the other hand, cigarette smoking is another lifestyle practice that many researchers have been relating to weight control. According to Picone et al. (1982), cigarette smoking is associated with stress, which can stimulate abnormal functions of hormones, leading to weight. Liao et al. (2016) developed interest and decided to invest the effects cigarette has on weight gain but the result indicated insignificant effects. This study confirms the previous findings by showing that the number of cigarettes a person smokes has no statistically significant influence on weight gain or loss in an individual.
ConclusionThis study significantly concluded that sports and physical activity, gender, hasa significant influence on individuals’ BMI. Increase in the BMI is not a healthy status of the body. Gaining weight is an indication of increased body mass index, which can culminate in other health problems such as obesity and diabetes. The study further has conclusively asserted that the weight trend of an individual is significantly influenced by one’s lifestyle such as sports and physical activity as well as the use of recreational drugs such as alcohol. Even smoking is linked to bad lifestyle among young people; it does not have a significant influence on the weight of an individual. The majority of people, especially young adults are compelled to these lifestyles by their peer and pressure from the media industry. Through the results obtained from the qualitative analysis of the data collected from ESS 7, an informed knowledge regarding the effects of sports, exercise and physical activity as well as eating style, gender and smoking habits on body weight has been established. This will help individuals and policymakers to influence a positive change in the social lifestyles of people.
References
Colberg, S. R., Sigal, R. J., Yardley, J. E., Riddell, M. C., Dunstan, D. W., Dempsey, P. C., ... & Tate, D. F. (2016). Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes Care, 39(11), 2065-2079.
Cox, C. E. (2017). Role of Physical Activity for Weight Loss and Weight Maintenance. Diabetes Spectrum, 30(3), 157-160.
Liao, C., Gao W., Cao, W., Lv, J., Yu, C., Wang, S., & Wu, F. (2016). The association of cigarette smoking and alcohol drinking with body mass index: a cross-sectional, population-based study among Chinese adult male twins. BMC public health, 16(1), 311.
Picone, T. A., Allen, L. H., Olsen, P. N., & Ferris, M. E. (1982). Pregnancy outcome in North American women. II. Effects of diet, cigarette smoking, stress, and weight gain on placentas, and on neonatal physical and behavioral characteristics. The American journal of clinical nutrition, 36(6), 1214-1224.
Redman, L. M., Heilbronn, L. K., Martin, C. K., Alfonso, A., Smith, S. R., Ravussin, E., & Pennington CALERIE Team. (2007). Effect of calorie restriction with or without exercise on body composition and fat distribution. The Journal of Clinical Endocrinology & Metabolism, 92(3), 865-872.
