The articles highlight the study background by linking lack of physical activity as a risk factor for coronary heart disease and its impact on the economy. The author has clearly stated the gap in knowledge that exist, consequently, justifying the purpose and the rationale of the study. For example, according to (Rastogi et al., 2004), there is no study in India that has determined the association between physical inactivity, and the risk of developing coronary heart disease, the lack of research on the subject, therefore, justifies the need to conduct research to provide more information on the subject matter. Moreover, the statement of the problem has been clearly stated in the in the introduction part of the article. For example, in order to identify research problem the author has cited an article that emphasizes that physical inactivity is associated with 1.5 to 2.4 folds increase in CHD risk (Rostagi et al., 2004) A study conducted by Soares et al. (2015) is another good example of journal article citing association between physical activity and coronary heart disease. This information transition well into the purpose of the research. At the same time, there are issues of concern that can be raised by the reader of the article. For example, the author doesn’t clearly state the research questions or hypothesis in the introduction part of the article, hence there is a lack of emphasis on the study aims. A studies conducted by (Anderson et al., 2016; Shiroma et al., 2010; Hashibe et al., 2009), are good examples where the research questions have been stated at the end of the introduction part in order to highlight the study aims.
In the introduction part the researchers have reviewed different literature with a view of describing both the exposure and the outcome. Furthermore, the information has help the reader understand the subject of interest. The literature used in this journal have helped in highlighting the knowledge gap that exists within the subject area. For example, according to (Rostagi et al., 2004), the researchers have not assessed the association between sedentary life style and risk of coronary heart disease within India. Therefore, the literature justifies the
Description of Evidence
The exposure in this study was sedentary life or physical inactivity, it was measured using validated questionnaire.
The risk of developing coronary heart disease as an outcome was associated physical inactivity, the findings from this study revealed that people involved in taking part in walking for 36 minutes every day were having 50% less risk of developing coronary heart disease as compared to non-exercisers as suggested by Rostagi et al. (2004). At the same time, the result from this study revealed that there was an association between increased sedentary life and risk of developing coronary heart disease. For example, the study revealed that spending 3.6 hours in a day on sedentary life related activities was connected to increased risk of developing coronary heart disease by almost 90% (Rostagi et al., 2004).
A case-control study design was used to collect data from acute myocardial infarction in patients. The study participants who were recruited as cases and control Cases were followed up for one year during the period of January 1999 to 2000. There is a good move by the author to improve the internal validity of the study by matching control and cases using age, gender and neighborhood to eliminate the confounding factors. As suggested by (Bowling, 2014; Kepes et al., 2014; Zaccai, 2004), confounding is a type of bias that causes falsehood assessed effect of the exposure on the outcome, and in the case-control studies, it can be controlled through matching. Therefore, the argument justifies the use of matching as suggested by (Rastogi et al., 2004) to use matching to validate the result of this study. At the same time, the use of case-controls design for this study was rightful move, this is because coronary heart disease is rare, and therefore, it required large sample size and a long time to make the follow-up in order to discover newly developed cases to test the hypothesis. Moreover, the design allowed for analysis of multivariate which also helped in eliminating the confounding bias leading to the improved validity of the study findings. The study design used allowed the use of multiple exposures that could allow the testing of new hypothesis. The use of case-control design allowed the author to measure the association between the physical inactivity and coronary heart disease as an outcome by calculating the odd ratio. However, there are certain fundamental issues that can be raised by a reader of this article. For example, using case-control study design the researchers cannot assess the scale of other diseases that would result from physical inactivity as specific exposure. Another issue that might be raised by the reader concerning the use of case-control study is due to the fact that there are possibilities of recall bias. I would have suggested the use of cohort study despite some of its shortcomings. The argument of using cohort study design when assessing the association between physical activity and coronary heart disease has been supported by researchers. For example, according to (Shiroma et al., 2010), the use of prospective cohort study design is highly recommended when conducting a study to determine an association between physical activity and the occurrence coronary heart disease because the design limits the recall bias of physical activity. A studies conducted by (Li and Siegrist., 2012; Rognmo et al., 2012; Stott et al., 2012; Sofi et al., 2008) is a perfect example of a study that supports the above argument. The risk of association between the exposure and the outcome cannot be measured, whereas this can be achieved when a cohort study is done. For example, a studies conducted by (Soares et al., 2015; Mons et al., 2014; Sattelmair et al., 201; Chow et al., 2010), established the increased risk of adverse outcome in physically inactive coronary heart disease patients. Lastly, a variety of other healthcare problems resulting from the exposure (physical inactivity) cannot be determined because the case has to be defined before the commencement of the study.
The cases recruited in the study were inpatients diagnosed with acute myocardial infarction and aged 21-74 years. It is worth noting that the researchers involved in this study used the restriction of age to limit the confounding factors. According to (Boccia et al., 2007), the use of restriction is an effective way to prevent confounding in any study. At the same time, matching of control to cases by age, gender and hospital were a good move by the author because it also helped in limiting the confounding factors leading to study validation. Matching is another way of controlling the confounding as suggested by Boccia et al. (2007). However, there are drawbacks that the reader can realize from matching of the participants, for example, when several confounders are matched it can be difficult to recruit the study participants, it might be difficult to realize the effects of the matched variable, and lastly there can be an introduction of confounding if the variables that have a strong correlation with the exposure. The author clearly indicated how the cases were recruited into the study, according to (Rastogi et al., 2004), the cases were recruited using Indian Council of Medical Research (ICMR) study criteria. Besides, the author has precisely defined the case, and the inclusion and exclusion criteria of recruitment of the study participants have also been mentioned this makes it easy for the study to be reproduced besides reducing bias in the study. For example, in this study, the researchers excluded the individuals with the previous history of acute myocardial infarction from the study. This was well-thought move as family history is a risk factor for the development of acute myocardial infarction, from the table 2 family history has relative risk of 1.9. Therefore, it can cause confounding if such cases were not eliminated from the study. Patients recruited as the controls, in the study were selected from the hospital settings and the same exclusion criteria used for the cases was applied. To prevent selection bias it is recommended that the individuals recruited as the control be selected from the population where cases have been recruited from. Therefore, with regards to this study the controls were recruited from the hospital on the basis of the inclusion criteria used to recruit cases, besides controls were also recruited from the same hospital settings as the cases. This was done to reduce the chance of selection bias in the study. According to (Boccia et al., 2007), selection bias comes as result of the difference in the relationship between the study participants and those who theoretically could have participated in the study and this include those who were not recruited into the study, and it is related to recruitment of the study cases and control.
Main findings and Discussion
The researchers of this article begin results presentation by linking back the research findings to the general aim of the study which was to determine the association of physical inactivity and increased possibility of developing CHD (Rastogi et al., 2004). The result of multivariate analysis revealed that participating in 36 minutes of walking every day has the potential of reducing risk of developing CHD by 55% matched to individuals who do not practice the brisk walking every day, with the reported relative risk of 0.45. The bar graph (Figure 1) was used by the study researcher to represent the results of different levels of leisure time –exercise. The results revealed that for non-exercisers the relative risk was 1% ,for participants who exercised between 0-145 met-minutes/day the relative risk was 0.96% and for individuals who practiced >145 met-minutes/day the relative risk was 0.44%. The authors used a very simple and clear method of result presentation making it easy for interpretation and inferential statistics. The other major finding reported by the researchers was an association between sedentary lifestyle and increased possibility of developing CHD. According to (Rastogi et al., 2004), spending 3.6 hours in a day on sedentary lifestyle was associated with 90% increase in the risk of developing CHD. The results of this study revealed that the time spent on working each day was linked to the risk of developing CHD but it was not significant this are reflected in (Table 3B), according to (Rastogi et al., 2004), the results revealed that persons working a median of ten hours every day recorded an relative risk of 1.9%.
The validity of the results from this study can be obtained by focusing on the effects of chance, bias, and confounding. According to (Zaccai, 2004), validity is referred as the degree to which statistical inference drawn from research is warranted when an account is taken of the study methodology, how the sample recruited from the general population is a true representative and the general nature of the population from which the sample is drawn. Internal validity main focus is the elimination of systemic errors in the study, while the external validity focuses on extent to which the result of the study provides the foundation for making statistical inferences. It is not possible to completely rule out chance, in this study the authors reported findings that were statistically significant having the p-value with accepted values below the quoted hypothesis p= 0.05. For example, the result of multivariate analysis revealed that participating in 36 minutes of brisk walking per day has the potential of reducing the risk of suffering CHD by 55% in comparison to individuals who do not practice the brisk walking every day, with the reported relative risk of 0.45 and the reported P= 0.0001. Therefore, to eliminate non-causal association observed in from the study findings between exposure and outcome the authors controlled the covariates that included age, sex, hospital and performed multivariate analysis. This was part of internal validity by the researchers. At the same time, the researcher carried out the internal validity by eliminating the selection bias. For example, in this study to address the issue emanating from the selection bias the patients used as controls were recruited from the same hospitals where the cases were recruited.
To improve internal validity the researcher eliminated confounding factors. First, the patients used as the control in the study were recruited by matching their age, gender and those of the cases. At the same time, the eligible cases were patients aged 21-74 years, this is a form of restriction that was applied by the researchers to eliminate the confounding factors. It is worth noting that despite the efforts by the researchers of this study both internally and externally improve the study validity, it clear to the reader that there were some weakness in the study. For example, there could be other sources of bias in the study that were not addressed by the authors such as measurement biases. It is clear to me that more than one research assistant was involved in obtaining anthropometric measurement, hence it’s possible to have errors at the same time the researchers didn’t provide information regarding the validation of data collection tools such as scales, height boards and specifically questionnaire used for the anthropometric measurements. Moreover, the other source of bias to this study could be attributed to Berkson’s bias, this is because controls were selected from the hospital where the cases were admitted rather than the general population where the cases originated from. According to (Boccia et al., 2007), Berkson’s bias applies to case-control studies conducted in the hospital setting, because there are different rates of hospital admission for cases and controls. The other possible source of bias for this study is due to differential misclassification recall bias due to difference in accuracy and completeness of the exposure information between the cases and control patients. For example, individuals with coronary heart disease will tend to have more information on the risk factors associated with the disease and the exposure status as compared to control who are less interested in the disease and the exposure. According to (Boccia et al., 2007), differential misclassification is bias that occurs mostly in case-control studies and it is shown when there is difference in accuracy and completeness of exposure information between the cases and control. Non-differential misclassification due to recall bias could also invalidate this study. This is possible during the data collection processes where cases and control could not accurately remember the levels of physical activities they have conducted, leading to inaccurate classification into levels of exercise impacting on reduction in the difference in the exposure levels in both cases and control in the same manner.
A strong association in this study was established by the result of multivariate analysis that revealed that participating in 36 minutes of brisk walking per day has the potential of reducing risk of developing coronary heart disease by 55% compared to individuals who do not practice the brisk walking every day.
The findings from this research are consistent with other reported results. For example, the result of the multivariate analysis revealed that participating in 36 minutes of brisk walking per day has the potential of reducing the risk of developing CHD by 55% matched to individuals who do not practice the brisk walking every day, with the reported relative risk of 0.45. This findings are supported by a cohort studies conducted by (Heran et al., 2011; Meisinger et al., 2007), the results from this study revealed that high level participation in physical activity was associated with reduced risk of cardiovascular disease.
There is a dose-response relationship in this study because the relative risk of myocardial infarction has linear decrease with the increase in the level of leisure-time physical exercise.
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