Effects of Cigarettes, Alcohol, and Physical Exercise on Human Health - Research Paper Example

Effects of Cigarettes, Alcohol and Physical Exercise on Human Health 0. Introduction The epidemics of smoking and alcoholism have been among the top catastrophes in public health over the past centuries. They are ranked among major contributors of disease and death across various age groups in the United Kingdom. Smoking refers to an act of burning substances whereby the resulting smoke is inhaled or taken in, tasted and absorbed into the bloodstream. Tobacco is the commonly abused or smoked substance across the UK’s population. Alcoholism refers to a chronic and progressive disorder which is associated with lacking control over drinking and being in constant use of alcohol even if it causes serious problems to the body. Health can be described as a level of metabolic efficiency of a human being; it is a complete state of mental, social and physical well-being of an individual. Thus, cigarettes and alcohol have a relatively huge impact on how the human body functions physically, mentally and socially. However, the human health can be sustained by constant or frequent physical exercise alongside a desirable or balanced lifestyle which is associated to sporting and physical exercises such as walking and jogging. Tobacco and alcohol are often used together. Various studies have proven that individuals who drink are more likely to smoke and those who smoke are much more likely to drink. This makes the dependence of these two substances to have some level of positive correlation in that those who depend on alcohol are two times likely, than other people in the general population, to be using tobacco. Moreover, the individuals who depend on smoking are three times more likely, compared to the general population, to depend on alcohol (Benowitz, & Henningfield, 2013). Thus, this research paper is meant to investigate how various activities in our lifestyles can influence the level of health. These activities include smoking, drinking and physical exercise. The dataset is of a sample size of 54597 whereby 74 variables are considered. However, this paper will consider only 1145 cases and eleven variables that match the topic of research. 1.1. Statement of Problem Understanding the impact of alcohol and tobacco on human health can sometimes be challenging due to the dynamics in lifestyles across different social classes. The co-use of these substances is common. Furthermore, they both have similar effects on a human brain. It is quite difficult to differentiate the combined impact of these substances on the human body and the effect they have on human health. Tobacco and alcohol are deemed to have a negative impact on general health. On the other hand, physical exercises have a boost on general health of human beings. Thus, it is not quite clear how smoking, drinking and physical exercise can impact on general health with the current lifestyles in the United Kingdom. 1.2. Objectives of the Study The primary objective of this study is to investigate if smoking, consumption of alcohol and physical exercises impact on general health of human beings. The secondary objectives of this research are: 1. To investigate the relationship between smoking and consumption of alcohol 2. To find out if physical activities such as walking is related to general health To meet the above objectives, this paper will use a number of procedures. To start with, it will review several literatures that are associated with the research topic. Most of these literatures will focus on cigarettes, alcohol, physical activities and human health. Secondly, descriptive statistics will be used to show the distributions and summaries of data as per individual variables. Lastly, regression and correlation analysis of the data are used to show relationship and dependencies of the variables. This will be accompanied by interpretations and conclusion of the results in order to provide a concrete answer to the research question. 2.1. Background Tobacco and alcohol have a relatively strong link whose implications are crucial for those people who are in the field of alcohol treatment and human health. A good number of alcoholics are deemed to be smokers. This places them at a high risk considering the tobacco related complications such as heart diseases, multiple cancers and lung diseases. According to statistics, most alcoholics die due to diseases that are tobacco-related compared to alcohol-related diseases. Additionally, health scientists have tried to solve or rather to treat these addictions through reliable programs that target start by targeting alcoholism and end by addressing smoking. Other related programs put emphasis on abstinence from using tobacco and alcohol simultaneously. Thus, an effective treatment depends on a perfect understanding of the interaction between these substances and their addiction. Thus, according to Reuben (2007), individuals who co-currently use tobacco and alcohol are considered to have the poorest health when compared to those who use one of them or not at all. According to World Health Organization (2007), alcohol, over the years, has been part of United Kingdom’s culture whereby many individuals used it sensibly. However, the use of this substance in an inappropriate way (whether as a heavy or moderate drinker) has a huge effect on the health of a drinker together with that of the family and friend. Poor health, as a result of alcohol, can be depicted by certain health complications such as blood pressure and liver diseases. Additionally, its addictiveness has a huge impact on the functioning of the brain whereby it slows body reactions that lead to a loss in inhibition. An approximated forty percent (40%) of UK citizens have poor health or body problems that result from the use of alcohol. This either comes by through alcohol-related accidents or injuries. Younger people or the youths are the most affected and whose health is proven to be below average. Williams and Brake (1980) argue that alcohol and tobacco are the leading causes of death in the United Kingdom that can be prevented. Despite various reports from health institutions, more than 30 million adults and more than two million high school students smoke and drink. Therefore, approximately fifty percent of those who use alcohol and tobacco have a relatively high probability of developing health complications that would render them poor health wise. The level of substance abuse has an effect on the health of the user. The department of health in UK recommended certain amount of alcohol that should be consumed for both males and females. Regularly, males should drink between three to four units of alcohol or less per day whereas females should not drink more than three units of per day. This is for an average drinker who takes alcohol that amount to twenty one units per week for men and fourteen units per week for women. For this case, 25ml of spirit is equivalent to a single unit. Thus, a glass of a standard wine which has 12% alcohol content has approximately 2.2 units of alcohol. Therefore, an individual who takes more than a glass of this kind of wine is deemed to have gone beyond the recommended health level of alcohol units per day. Moreover, these recommended limits are set to an optimal level for the entire population. However, for any level of alcohol consumption, there is no a safe or a risk free position since health complications such as cancer increases in the human when the level of consumption goes down beyond the recommended levels. Pregnant mothers are advised to totally abstain from drinking (World Health Organization, 2007). Smoking is associated with cancer and diseases that affect the respiratory system alongside other medical and physical complications. In cancer patients, smoking is deemed to worsen their health conditions. In men, smoking might interfere with the fertility and further cause erectile dysfunctions. Moreover, smoking of tobacco causes diabetes and tends to increase the risk of arthritis. Chronic coughing, loss of vision and hearing, bronchitis and emphysema are said to be as a result of smoking. Despite all the documented effects of smoking, smokers believe that this act relieves stress since it reduces the irritability and the tension which caused by addiction of nicotine. Physical exercise has a positive impact on human health. According to Brown and Wang (1992), the human body tends to increase the rate of detoxification when it is engaged in any kind of physical activities such as walking running and sporting. For instance, when the body exercises regularly, the rate of blood circulation is increased. As a result, the body’s metabolic wastes are released through skin (sweat), the respiratory organs actively releases unwanted gases out of the body and the fats are fully burnt so as to prevent the body from becoming obese. Moreover, for those who smoke and use alcohol, body exercise plays a crucial role in minimizing the effects of these substances to their bodies. 3.0. Model This is a quantitative and descriptive type of study whose aim is to provide an accurate and valid representation of factors or variables that are relevant to the study question. Thus, in order to achieve the objectives of this study, this paper uses primary source of information. Primary data is collected from 54597 respondents through prepared questionnaires that include close ended questions with 74 variables. However, for the purpose of the research question, this study narrows down to only eleven variables which are general health, done walking at least 10 minutes, number of days walked at least 10 minutes, number of days walked at least 30 minutes, smoke cigarettes now, usual number of cigarettes smoked per day, moderate intensity sports frequency, mild intensity sports frequency, accessibility of sporting facilities, how often have you had an alcoholic drink during the last 12 months and On how many days did you have an alcoholic drink? These variables are categorized according to different regression models and objectives they are meant to achieve in this research. Regression and correlation analysis is used to show how these variables can best explain the dependent variable (general health). 3.1. Regression Model 1: Effects of Smoking on General health The first regression model tests the effect of smoking on general health. This model comprises of independent variables such as Smoke Cigarettes now (b_smnow) and Usual Number of Cigarettes Smoked per day (b_ncigs). Thus, the model shall illustrate how the use of cigarettes explains the level general health of humans. Moreover, correlation analysis is used to show the relationship between the independent variables and the dependent variable and which explanatory variable is appropriate for this model. 3.2. Regression Model 2: Effects of Alcohol on General Health The second regression model tests the effect of alcohol on general health. In this model independent variables include: How often have you had an alcoholic drink during the last 12 months (b_scfalcdrnk) and on how many days did you have an alcoholic drink? (b_scnalcl7d). Therefore, this model shall show how consumption of alcohol can explain the position or level of general health. Additionally, correlation analysis is used to specify the strength of different relationships of variables and which variable(s) are appropriate in explaining this model. 3.3. Regression Model 3: Effects of Physical Exercise on General Health The third regression model tests the effect of physical exercise such as walking and sporting on general health. Explanatory variables in this model comprise of ‘done walking at least 10 minutes (b_wlk10m), number of days walked at least 10 minutes (b_wlk10m) and number of days walked at least 30 minutes (b_wlk30min), Moderate intensity sports frequency (b_sportsfreq), Mild intensity sports frequency (b_sports3freq) and Accessibility of sporting facilities (b_access). Thus, this model shall illustrate how physical exercise plays a role in determining the level of health. The relationship between the explanatory variables and the dependent variables is determined using correlation analysis. Furthermore, this technique shall determine the variables that are appropriate for this model. As per the dataset, the above models are used to show how general living and lifestyles have an impact on body health. They will explain the proportion of variation in general health that can be explained by the explanatory variables using the coefficient of determination (R-squared). Different variable coefficients shows how the presence or absence of an explanatory variable can influence the dependent variable; either improve or deteriorate the general health. 4.0. Data The data used in this research was retrieved from the United Kingdom Archive Data Dictionary where 54597 cases are considered alongside 74 variables. However, out of these cases, this paper considers a portion of the data and uses a sample size of 1145 alongside 11 variables. According to the primary objective of this study, general health is the dependent variable. It is abbreviated as b_sf1. Other determinants of health in the data are considered to be explanatory variables as discussed below: 4.1. Dependent variable: General Health (b_sf1) This variable represents the level of human health for each case in the sample size. Codes are used to show the level of health. For instance, excellent=1, very good=2, good=3, fair=4, poor=5 whereas the negative values represents either missing, refused, inapplicable or proxy. 4.2. Explanatory variables There are ten independent variables used in this study. Starting with smoking category, there are two independent variables: smoke cigarettes now (b_smnow) and usual number of cigarettes smoked per day (b_ncigs). B_smnow shows whether a personal is currently smoking cigarettes or not. Yes=1, N0=2, don’t know=-1, missing=-9, inapplicable=-8, proxy=-7 and refused=-2. B_ncigs shows the number of cigarettes an individual smoke per day. Under the alcohol category, there are two independent variables: How often have you had an alcoholic drink during the last 12 months (b_scfalcdrnk) and On how many days did you have an alcoholic drink? (b_scnalcl7d). B_scfalcdrnk shows the frequency of drinking whereas b_scnalcl7d shows the number of days in a week an individual takes alcohol. Physical exercise category has the following independent variables: Done walking at least 10 minutes (b_wlk10m), Number of days walked at least 10 minutes (b_wlk10m) and Number of days walked at least 30 minutes (b_wlk30min), Moderate intensity sports frequency (b_sportsfreq), Mild intensity sports frequency (b_sports3freq) and Accessibility of sporting facilities (b_access). All these variables are coded with both positive and negative values. 4.3. Descriptive Statistics Table 1: Descriptive Statistics b_sf1 b_ncigs b_smnow b_scfalcdrnk b_scnalcl7d N Valid 1145 1145 1145 1145 1145 Missing 0 0 0 0 0 Mean 2.66 -4.46 -2.87 2.60 -2.34 Std. Error of Mean .033 .251 .141 .146 .176 Median 3.00 -8.00 1.00 4.00 1.00 Mode 2 -8 -8 4 -9 Std. Deviation 1.108 8.498 4.778 4.933 5.962 Variance 1.228 72.224 22.826 24.331 35.549 Skewness .381 3.409 -.085 -1.266 .003 Minimum 1 -8 -8 -9 -9 Maximum 5 100 2 9 7 Table 2: Descriptive Statistics b_sportsfreq b_sports3freq b_access b_wlk10m b_daywlk b_wlk30min N Valid 1145 1145 1145 1145 1145 1145 Missing 0 0 0 0 0 0 Mean -1.55 -1.61 3.38 .63 10.48 5.14 Std. Error of Mean .156 .163 .090 .062 .396 .329 Median 1.00 1.00 4.00 1.00 10.00 2.00 Mode -8 -8 5 1 28 -8 Std. Deviation 5.277 5.521 3.048 2.114 13.405 11.119 Variance 27.850 30.479 9.292 4.470 179.691 123.621 Skewness -.279 -.187 -2.747 -3.188 -.030 .734 Minimum -8 -8 -9 -7 -8 -8 Maximum 6 6 6 3 28 28 Table 1 and table 2 are summaries of all the eleven variables. The means represent the average for all observations in the variables, the mode gives the most appearing observation, the median represents the middle observation, standard deviation shows the average distance of observations from the mean, the variance shows how far an observation is from the mean and skewness shows how observations for a given variable are distributed. Considering the dependent variable (b_sf1), it has a mean of 2.66 which can be rounded off to 3. This implies that the average health of the respondents is good. Moreover, the variable has a mode of 2 which shows that the health level of many respondents is very good. The variable has a smaller variance of 1.228 which implies that the data points are closer to the mean. Finally, general health is positively skewed; many observations are to the right of the distribution and thus we can conclude that most of the respondents have a good health. This is shown in the graph below: Diagram 1: General Health Histogram 5.0. Empirical Analysis Analysis is based on the fitted regression models that explain or rather determine the level of General Health. 5.1. Regression Model 1: General Health as a function of smoking A summary of this model is shown in the output below: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .138a .019 .017 1.098 .019 11.095 2 1142 .000 a. Predictors: (Constant), b_smnow, b_ncigs Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 2.753 .040 69.630 .000 b_ncigs .015 .004 .118 3.820 .000 .895 1.117 b_smnow .010 .007 .042 1.370 .171 .895 1.117 a. Dependent Variable: b_sf1 From the above output, this model significant at 5% level of significance since its p-value is less than 0.05. Additionally, the model has a coefficient of determination (R-squared) of 0.019 which implies that only 1.9% variation of the general health can be explained by the number of cigarettes smoked and if an individual smokes or not. The two explanatory variables are strongly related. This is proven by a relatively high collinearity (0.895) which shows that the number of cigarettes smoked highly depend on whether a person smokes or not. The y-intercept of this model shows that when all the variables are kept at zero, general health is at 2.75 which is a good measure. However, the number of cigarettes smoked is the only appropriate variable since its p-value is less than 0.05. Considering this variable in the model, we can say that whenever it is increased by a single unit, the level of health deteriorates by 0.015. 5.2. Regression Model 2: General Health as a function of alcohol A summary of this model is shown in the output below: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .181a .033 .031 1.091 .033 19.237 2 1142 .000 a. Predictors: (Constant), b_scnalcl7d, b_scfalcdrnk Coefficientsa Model Unstandardized Coefficients Standardized Coefficients T Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 2.527 .039 64.548 .000 b_scfalcdrnk .023 .007 .103 3.517 .000 .991 1.009 b_scnalcl7d -.029 .005 -.158 -5.421 .000 .991 1.009 a. Dependent Variable: b_sf1 From the above output, this model shows significance at 5% level of significance since its p-value is less than 0.05. Moreover, it has a coefficient of determination (R-squared) of 0.033 which implies that only 3.3% of variation in the general health can be explained by the frequency of drinking and the number of days per week an individual drinks. The two explanatory variables are strongly related. This is proven by a relatively high collinearity (0.991) which shows that the number frequency of drinking determines the number of days a person drinks per week. Looking at the coefficients of this model, we can conclude that only one variable (b_scfalcdrnk) significant for the model. The frequency of drinking alcohol in the past 12 months best explains this model. Thus, if this variable is increased by a single unit, the level of general health worsens or becomes poor by 0.023. 5.3. Regression Model 3: General health as a function of physical exercise This model can be summarized as shown below: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .427a .182 .178 1.004 .182 42.336 6 1138 .000 a. Predictors: (Constant), b_wlk30min, b_wlk10m, b_sportsfreq, b_sports3freq, b_daywlk, b_access Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 2.882 .074 38.756 .000 b_sportsfreq -.036 .006 -.174 -5.629 .000 .756 1.324 b_sports3freq -.027 .006 -.133 -4.290 .000 .742 1.348 b_access -.108 .025 -.297 -4.302 .000 .151 6.621 b_wlk10m .265 .034 .505 7.892 .000 .176 5.695 b_daywlk -.011 .004 -.132 -2.806 .005 .325 3.074 b_wlk30min -.002 .005 -.024 -.528 .598 .345 2.900 a. Dependent Variable: b_sf1 From the above output, this model has some significance at 5% level of significance since its p-value is less than 0.05. Additionally, the model has a coefficient of determination (R-squared) of 0.182 which implies that only 18.2% variation in the general health can be explained by physical exercise. The explanatory variables are not strongly related; they have a low tolerance value under collinearity (as low as 0.151). The y-intercept in this model shows that when all the factors are assumed to be absent, general health will be 2.88 which is quite good as per the scale. However, when the independent variables are present, this tends to reduce the y-intercept. From the general health’s scale, the lower the value the better the health. Thus, when these variables are increased by a single unit, the level of health is improved by respective values of the coefficients. 6.0. Conclusion The level of general health of human beings is affected or rather determined by a number of factors. Smoking cigarettes and consumption of alcohol are deemed to have a negative impact on general health whereas physical exercise gives it a boost. This paper uses three different models to show how health is affected by the above factors. From the first model, we find that the number of cigarettes smoked is the only appropriate variable in explaining this model. Thus, when this variable is increased by a single unit, the level of health deteriorates by 0.015. Likewise, only a single variable (b_scfalcdrnk) in the second model is appropriate for the model. Thus, any increase in the frequency of drinking alcohol by a single unit deteriorates the health level by 0.023. On the other hand, the third model describes how physical exercises impact on general health. From the model, all the independent variables are appropriate in describing the model apart from b_wlk30min which has a p-value greater than 0.05. Thus, when these variables are increased by as single value each, the level of health improves generally. Therefore, smoking and drinking reduces or deteriorates the general level of health whereas physical exercise adds value to general health. Moreover, individuals who drink and smoke at the same time are deemed to have poor health since these activities are proven to add no value to health (Brown & Wang, 1992). On the contrary, physical exercise and accessibility to sports facility have a general boost to health. References List BENOWITZ, N.L. & HENNINGFIELD, J.E. (2013). Reducing the nicotine content to make cigarettes less addictive. Tobacco Con­trol; vol.22, p.i14–i17. BROWN, D. R. & WANG, Y. (1992). The relationship among exercise training, aerobic capacity and psychological well-being in the general population. Journal of Medicine, Exercise, Nutrition and Health, vol.3. p.125-142. REUBEN, S.H. (2007). Promoting Healthy Lifestyles: Policy, Pro­gram, and Personal Recommendations for Reducing Cancer Risk. 2006–2007 Annual Report, President’s Cancer Panel. Washington: U.S. Department of Health and Human Services, National Institutes of Health, National Cancer Institute. Retrieved From: http://deainfo.nci.nih.gov/advisory/pcp/annualreports/pcp07rpt/ pcp07rpt.pdf WILLIAMS, G.P. & BRAKE, G.T. (1980). Drink in Great Britain 1900-1979. London: Edsall & Co Ltd. WORLD HEALTH ORGANIZATION. (2007). Evidence-based strategies and interventions to reduce alcohol-related harm. Retrieved From: http://www.who.int/gb/ebwha/pdf_files/WHA60/A60_14-en.pdf Appendix Variable label = general health This variable is numeric, the SPSS measurement level is SCALE Value label information for b_sf1 Value = 1.0 Label = excellent Value = 2.0 Label = very good Value = 3.0 Label = good Value = 4.0 Label = fair Value = 5.0 Label = poor Value = -1.0 Label = dont know Value = -9.0 Label = missing Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refused Variable label = done walking at least 10 minutes This variable is numeric, the SPSS measurement level is SCALE Value label information for b_wlk10m Value = 1.0 Label = yes Value = 2.0 Label = no Value = 3.0 Label = spontaneous: cant walk at all Value = -1.0 Label = dont know Value = -9.0 Label = missing Value = -8.0 Label = inapplicable Variable label = number of days walked at least 10 minutes This variable is numeric, the SPSS measurement level is SCALE Value label information for b_daywlk Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refused Value = -1.0 Label = dont know Value = -9.0 Label = missing Variable label = number of days walked at least 30 minutes This variable is numeric, the SPSS measurement level is SCALE Value label information for b_wlk30min Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refused Value = -1.0 Label = dont know Value = -9.0 Label = missing Variable label = smoke cigarettes now This variable is numeric, the SPSS measurement level is SCALE Value label information for b_smnow Value = 1.0 Label = yes Value = 2.0 Label = no Value = -1.0 Label = dont know Value = -9.0 Label = missing Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refused Variable label = usual no. of cigarettes smoked per day This variable is numeric, the SPSS measurement level is SCALE Value label information for b_ncigs Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refused Value = -1.0 Label = dont know Value = -9.0 Label = missing Variable label = how often have you had an alcoholic drink during the last 12 months? This variable is numeric, the SPSS measurement level is SCALE Value label information for b_scfalcdrnk Value = 1.0 Label = almost every day Value = 2.0 Label = five or six days a week Value = 3.0 Label = three or four days a week Value = 4.0 Label = once or twice a week Value = 5.0 Label = once or twice a month Value = 6.0 Label = once every couple of months Value = 7.0 Label = once or twice a year Value = 8.0 Label = not at all in the last 12 months Value = 9.0 Label = not in last 12 months but scalcl7day is answered Value = -1.0 Label = dont know Value = -9.0 Label = missing Value = -8.0 Label = inapplicable Value = -7.0 Label = proxy Value = -2.0 Label = refusal Variable label = on how many days did you have an alcoholic drink? This variable is numeric, the SPSS measurement level is SCALE Value label information for b_scnalcl7d Value = 1.0 Label = one day Value = 2.0 Label = two days Value = 3.0 Label = three days Value = 4.0 Label = four days Value = 5.0 Label = five days Value = 6.0 Label = six days Value = 7.0 Label = seven days Value = -1.0 Label = dont know Value = -9.0 Label = missing Read More