![]() We used both parametric and semi-parametric regression models to examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). In order to overcome these challenges in the analysis of physical activity data, we used the method of principal component analysis (PCA) to identify physical activity patterns that take into account combinations of physical activities. ![]() Hence, the effect estimate of one type of physical activity does not present its pure effect, but includes the effects of total physical activity. As one type of physical activity increases, total physical activity increases as well, given that the other physical activities are fixed. Previous models that incorporate one type of physical activity of interest and other types of physical activity (as potential confounders) for exploring the effects of each type of physical activity on CVD may be problematic because of the concomitant change in total physical activity. Furthermore, studying different types of physical activity in isolation may not adequately consider any joint and interactive associations on the risk of CVD. Therefore, if we use a single summary measurement to reflect physical activity, such as METS, the association between physical activity and risk of CVD might be biased because subjects who have the same measured value may have a distinct combination of physical activities. There are also interactive effects between lack of exercise and sitting at work and between demanding household work and sitting at work on the association with increased risk of acute myocardial infarction (AMI). For example, some leisure time activities such as walking, stair climbing, and cycling provide protection against CVD, whereas others, such as intensive domestic physical activity, may not offer protection against CVD. However, studies have shown that different types of physical activities may have different effects on the risk of cardiovascular disease (CVD) and may interact together. Physical activity may contribute up to 20% - 30% reduced risk of coronary heart disease. Numerous observational epidemiologic studies have demonstrated that physical activity is inversely related to cardiovascular morbidity and mortality. PCA provides a new approach to investigate the relationship between physical activity and CVD risk. These data suggest that a light indoor activity pattern is associated with reduced AMI risk. There was an inverse association between total activity-related energy expenditure and AMI risk but it reached a plateau at high levels of physical activity ( P for non-linearity=0.01). The light indoor activity and rest/sleep patterns showed an inverse linear relation ( P for linearity=0.001) and a U-shaped association ( P for non-linearity=0.03) with AMI risk, respectively. ![]() Resultsįour physical activity patterns were retained from PCA that were characterized as the rest/sleep, agricultural job, light indoor activity, and manual labor job patterns. The component scores derived from PCA and total METS were used in natural cubic spline models to assess the association between physical activity and AMI risk. We examined the relationship between physical activity patterns, identified by principal component analysis (PCA), and AMI risk in a case-control study of myocardial infarction in Costa Rica (N=4172), 1994-2004. We aimed to identify physical activity patterns that take into account combinations of physical activities and examine the association between derived physical activity patterns and risk of acute myocardial infarction (AMI). The interactive effects of different types of physical activity on cardiovascular disease (CVD) risk have not been fully considered in previous studies.
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