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Study design and data collection methods

Rastogi and colleagues conducted a case-control study in 2004 with an objective to know the relationship between physical exercise and coronary heart disease (CHD) in the urban areas of India (Rastogi et al 2004). The urban hospitals of New Delhi and Bangalore cities of India were chosen to draw a sample of 350 cases and 700 controls. The selected cases and controls were then matched for age and gender. To proceed with data collection, the participants were asked certain questions regarding socioeconomic status, smoking history, history of hypertension, diabetes, hypercholesterolaemia, family history of Cardio Vascular Diseases, dietary intake, types of fat or oils used in cooking, nutritional supplement use, and physical activity. The anthropometric measurments were also taken regarding height, weight, hip & waist circumferences, body mass index (BMI) and waist to hip ratio. The data was analysed using conditional logistic regression analysis with in-built control for confounders. The results established that 48% of controls and 38% of cases joined some kind of leisure-time physical activity. The participants who engaged in highest level of leisure-time physical activity had lowest risk of developing cardiovascular diseases. On the other hand; participants with sedentary lifestyles, had high risk of developing cardiovascular diseases. The study concluded that leisure-time exercise had a protective effect on cardiovascular system as compared to sedentary lifestyle. Finally the study recommended healthy lifestyles including leisure-time exercise to be promoted in urban areas.

The exposure or intervention was leisure time exercise which was assessed with the help of physical activity questionnaire in this case-control study (Rastogi et al 2004, pg 2, para 5).

The outcome was Acute Myocardial Infarction (AMI). Cases were the participants having outcome. Controls were the participants in which outcome was absent (Rastogi et al 2004, pg 2, para 1).

The study design was case-control study having 350 cases of acute myocardial infarction (AMI) and 700 controls with the absence of AMI, matched for age and gender (Rastogi et al 2004, pg 1, Abstract).

The study population was urban population of two cities (New Delhi and Banglore) of India.

People with engagement in highest level of leisure time physical exercise i.e. 145 MET-minutes per day (equivalent to 36 minutes of brisk walking per day) had relative risk (RR) of 0.45 (95% CI 0.31-0.66) as compared to sedentary group. In other words leisure-time exercise had a protective effect for the risk of cardiovascular diseases. On the other hand participants with greater than 3.6 hours per day of sedentary activity had about 1.88 times (95% CI 1.09-3.20) higher risk of developing cardiovascular diseases, as compared to participants with less than 70 minutes per day of sedentary activity per day.

Results and conclusions

Non-causal explanations of the observed association could be attributed to selection and/or measurement bias, confounding, or chance variation which are explained in the following section –

The cases and controls were matched for age and gender; but still there may arise selection bias in the study. The author has stated in the paper that controls were relatively healthy and with minor ailments, as compared to cases which is a potential source of bias. The studies have shown that selection bias is a common source of bias in case-control studies (van Rein et al 2014). The selection of controls could also be a source of bias. Also in the selected study, the outcome is very severe and the risk of pre-mature death is high. Thus the study was able to select only those cases that survived the outcome of AMI and thus had comparatively less severe illness, which may be a source of bias (van Rein et al 2014). Thus, although there were 25 cases that did not survive, but they could not be included in the study. This kind of bias is also called “survivor bias in case-control studies” (ibid).

Next, the author has discussed in the paper that controls were selected from seven different sources; and so there was risk of cause-effect association being present in one particular source and not in other source; which might induce bias in the results. Moreover, author himself has contemplated the possibility that only health conscious participants were present in the study, which may be a source of bias in the study. Author has also mentioned that controls in the study were more educated and had lower incomes as compared to cases; which might be the source of bias in the study.

The study adjusted the following confounders: age, gender, cigarette and bidi smoking, BMI, WHR, alcohol intake, education, or income; but there might be other unknown confounding factors which might have introduced bias in the results. There is also a possibility of introducing the bias by matching itself. However matching is supposed to remove bias but studies have shown that it may also be the source of bias (Pearce 2016). While attempting to match the confounders; matching may also be done automatically for exposure itself (ibid). Further matched case-control design must include logistic regression analysis which was included in the study (ibid). Thus in this particular research various potential confounders were controlled in the analysis and every possible effort was done to remove confounding.

Potential sources of bias and confounding

The results of the study were significant at the 95 percent level of significance, and thus there were only 5 percent chances of chance variation in the study.

Chance variation is also called chance error or random error and is inherent in any research based on statistical predictions. It is the difference between the predicted value and the population value. In other words it is the probability by which the study estimates will differ from the population value. In a normal distribution curve, the range of results will be 1.96  SD (standard deviations) above and below the estimated mean; and thus there are 95% chances that population value will fall in that range; and thus there will be only 5 percent possibilities that population value will differ from this range, and this is called chance variation. To be very precise there will be 2.5% chances that population value will fall above this range and 2.5% chances that population value will fall below this range. (Sowey, & Petocz, 2017).

The research is reflected to be internally valid if the research had minimum Systematic error or bias. The internal validity of a research ensures that the cause-effect relationship in the research must not be a spurious one. There are several standards of internal validity which were defined by various epidemiologists and substantiate the evidence of causal relationship between the exposure and the outcome; within the study. Some of these principles are described in detail in the following section. The first standard mandates that the cause must precede the effect; which is also called temporal relationship. The second standard mandate strong correlation between cause and effect. In other words, by changing the values/levels of one, there should be an obvious change in the other. The third standard mandates that there should be a dose-response relationship between the cause and effect. In other words, higher the variation in one higher should be the variation in the other or vice-versa (Neuman 2016). These conditions for internal validity of study are further reflected in detail as follows.

The research has shown a temporal relationship between exposure and outcome. It was a case-control study and the exposure or non-exposure to leisure-time activity preceded the development or non-development of outcome i.e. acute myocardial infarction (AMI).

The relationship between leisure time exercise and AMI was very strong as the P value was less than 0.0001 for the relationship. It means that there were only 0.01 percent possibilities that the relationship between cause and effect do not exist. Also participants with 35-40 minutes of brisk walking had 55% lower risk of developing AMI as compared to controls who did not exercise.

Internal validity of the study

There was a dose-response relationship between exposure and the outcome. Participants in the highest level of physical exercise group had lowest risk of developing AMI and this observation was significant at p<0.0001.

The results were consistent within the study as age and sex adjusted analysis showed that leisure-time physical exercise lowered the risk of AMI. After adjusting for confounders the relationship was same. The results were also consistent in multi-variate analysis.

The findings are consistent with other evidence mentioned in the study as well as outside. The findings are consistent with some recent evidence. In 2016, Barengo and colleagues established that leisure-time physical activity is independently associated with cardiovascular diseases in older adults in Finland (Barengo et al 2016). Another prospective cohort study in Manhattan in 2016 showed similar relationship (Cheung et al 2016). In 2014, Andersen and colleagues found that leisure time physical activity had a protective effect towards the risk of developing MI and also had a dose-response relationship (Andersen et al 2014).  Similarly the INTERHEART study of China found a protective role of leisure-time physical exercise towards cardiovascular risk as compared to sedentary lifestyle (Cheng et al 2014). Moreover in Copenhagen City Heart study, it was found that leisure-time physical exercise had a protective effect in post-MI patients (Saevereid et al 2013). The article also compares and contrast several studies. One prospective study from US established that more than 3 hours per week of leisure time physical exercise had protective effect for cardiovascular diseases. Another cohort study on US men concluded that individuals doing more than 30 minutes per day of moderate-intensity physical exercise had 20 percent lower chances of developing cardiovascular diseases. The selected paper discussed another US-based prospective study on post-menopausal women, which also revealed that walking daily have a protective effect on heart.

The results are plausible in terms of a biological mechanism. The leisure-time physical exercise results in lipid lowering in Atherosclerotic plaques. It also reduces thrombotic potential and increases fibrinolytic potential (Backshall et al 2015). The study has also discussed underlying biological mechanisms due to which physical activity has protective effects on CVD risk. These include reduced blood pressure, increased HDL (High-Density Lipoproteins), increase in insulin sensitivity, improvement in endothelial function, and reduction in atherogenic cytokine production (Rastogi et al 2004).

The external validity of a research signifies the level to which the results of the study could be generalised across populations. The sampling bias may be an intimidation for external validity of research and may arise if the sample is not true representative of study population (Pearl 2017). The study results could be generalised to population if the sample is representative of population, otherwise it may result in sampling bias and the study cannot be said to be externally valid or with a low external validity. It is always central to emphasize the reporting of results on external validity so that the setting of application of results could be tacit. It is important to determine whether the results are applicable to local settings or wider settings. The external validity is important to convert research in to practice as the interventions may also be applicable to similar settings (Steckler, & McLeroy, 2008).

Consistency of results with previous studies

This particular research was conducted on hospital patients from the urban settings of Delhi and Banglore. Thus results of the study could be generalised to urban cities of India only. The study population was derived from urban hospitals of New Delhi and Banglore cities of India. The chosen sample was sufficient to generalise the findings to the source population; but as the research was conducted on patients selected from hospitals only, the generalisation of results to general population is uncertain. Thus the results of this research could be generalised to urban hospital patients of New Delhi and Banglore cities of India.

Conclusion

The study conducted by Rastogi and colleagues has maintained rigour during research. The study was a case-control study in which leisure-time exercise was exposure and AMI was outcome. The study found a cause and effect relationship where leisure time exercise was protective for the risk of developing AMI. The relationship do not seem to have non-causal explanations as all the bias including selection bias, measurement bias and confounding bias were tried to minimise by matching and logistic regression analysis. The chance variation could be only 5%. The study results seem to be internally valid as the cause and effect have a strong relationship, temporal relationship, and dose-response relationship. The study results are also consistent within the study. The cause and effect relationship is biologically plausible. The external validity of research is uncertain and the study results could only be generalised to urban areas of India.

References 

Andersen, K., Mariosa, D., Adami, H. O., Held, C., Ingelsson, E., Lagerros, Y. T., ... & Sundström, J. (2014). Dose-response relations of total and leisure-time physical activity to risk of heart failure: a prospective cohort study. Circulation: Heart Failure, CIRCHEARTFAILURE-113.

Barengo, N. C., Antikainen, R., Borodulin, K., Harald, K., & Jousilahti, P. (2016). Leisure?Time Physical Activity Reduces Total and Cardiovascular Mortality and Cardiovascular Disease Incidence in Older Adults. Journal of the American Geriatrics Society.

Cheng, X., Li, W., Guo, J., Wang, Y., Gu, H., Teo, K., ... & Yusuf, S. (2014). Physical activity levels, sport activities, and risk of acute myocardial infarction: results of the INTERHEART study in China. Angiology, 65(2), 113-121.

Cheung, Y. K., Moon, Y. P., Kulick, E. R., Sacco, R. L., Elkind, M. S., & Willey, J. Z. (2016). Leisure-time physical activity and cardiovascular mortality in an elderly population in northern Manhattan: a prospective cohort study. Journal of General Internal Medicine, 1-7.

Backshall, J., Ford, G. A., Bawamia, B., Quinn, L., Trenell, M., & Kunadian, V. (2015). Physical activity in the management of patients with coronary artery disease: a review. Cardiology in review, 23(1), 18-25.

Neuman, W. L. (2016). Understanding research. Pearson.

Pearce, N. (2016). Analysis of matched case-control studies. bmj, 352, i969.

Pearl, J. (2017). The Eight Pillars of Causal Wisdom (Lecture notes for the UCLA WCE conference, April 24, 2017).

Rastogi, T., Vaz, M., Spiegelman, D., Reddy, K. S., Bharathi, A. V., Stampfer, M. J., ... & Ascherio, A. (2004). Physical activity and risk of coronary heart disease in India. International journal of epidemiology, 33(4), 759-767.

Saevereid, H. A. S., Schnohr, P. S., & Prescott, E. P. (2013). Speed and duration of walking and other leisure time physical activity and the risk of heart failure: the Copenhagen City Heart study. European Heart Journal, 34(suppl 1), P3646.

Sowey, E., & Petocz, P. (2017). A Panorama of Statistics: Perspectives, Puzzles and Paradoxes in Statistics. John Wiley & Sons.

Steckler, A., & McLeroy, K. R. (2008). The importance of external validity. American Journal of Public Health, 98(1), pp. 9–10.

van Rein, N., Cannegieter, S. C., Rosendaal, F. R., Reitsma, P. H., & Lijfering, W. M. (2014). Suspected survivor bias in case–control studies: stratify on survival time and use a negative control. Journal of clinical epidemiology, 67(2), 232-235.

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