The standardized activity questionnaire consisted of 18 questions and physical activity was determined by asking subjects the average frequency and time spent on several occupational and leisure time activities during the last year. These activities were grouped into six categories according to their intensity or metabolic equivalents : lying quietly in bed: afternoon nap or rest and night sleep ; sitting ; light indoor activity such as standing at work or at home ; moderate outdoor activity such as gardening, light agriculture and construction, and walking on flat surfaces ; vigorous aerobic activity such as heavy agriculture and construction, walking uphill, climbing stairs, jogging and other sports ; strenuous anaerobic activity such as carrying, pushing and lifting heavy objects . Energy expenditure for each activity was calculated as the product of frequency, time, and intensity . Total activity-related energy expenditure per day was calculated by the sum of energy expenditure on each activity listed in our questionnaire and was measured by total METs of activity performed each day. This questionnaire was previously used in a study of 465 people conducted in Costa Rica. The data showed that the reported time spent on different types of daily activities using the questionnaire predicted higher fitness scores, lower LDL levels, and lower BMI. These results allow us to consider that the predictive validity of the questionnaire is reasonable.All analyses were carried out with SAS . The original sample size was composed of 2,273 cases and 2,274 controls. A total of 274 cases and 275 controls were excluded due to missing information on physical activity and the covariates in the data analysis ,commercial grow rack implausible total activity-related energy expenditure , and losing matched controls/cases after performing rematching based on the original matching criteria . The final study sample consisted of 1999 case-control pairs . We used PCA on the 18 questions of the standardized activity questionnaire to identify physical activity patterns.

The components were extracted using an orthogonal matrix to achieve a simple structure that facilitates interpretability and makes the derived patterns independent of each other. The following three criteria were used to determine the number of components to retain: the criterion of eigenvalues exceeding one, the scree plot, and the interpretability of each component. The component score of each pattern for each subject was calculated by summing the hours spent on physical activities weighted by their component loadings.As part of a sensitivity analysis, we performed PCA stratified by sex. We used paired t-tests and McNemar tests to compare means and proportions between cases and controls, given the matched design. We used parametric regression models and semi-parametric regression models to assess the association of AMI risk with extracted physical activities patterns and total activity-related energy expenditure. In the parametric regression models, component scores of each extracted pattern and total activity-related energy expenditure were divided into quintiles. Quintiles of those variables were entered in multivariate conditional logistic regression analysis to calculate odds ratios and 95% confidence intervals. Tests for trend were derived from conditional logistical regression with a single term representing the medians of quintiles 1-5. In semi-parametric regression models, natural cubic splines were fitted to conditional logistic regression models to examine the relationship between total activity-related energy expenditure and risk of AMI and the association between extracted physical activity patterns and risk of AMI. Natural cubic splines are smooth polynomial functions that can be used to fit data and accommodate potential changes in the direction of the association across the distribution of an exposure.

They are useful to examine non-parametrically the potential non-linear relation between the exposure and the outcome of interest. They are constructed of piece wise third-order polynomials which pass through a set of control points and it is linear in its tail beyond the boundary knots. Since they are numerically stable and allow computation of fit with great accuracy, natural cubic splines are widely used in semiparametric regression. A SAS macro named ‘lgtphcurv9’ was used which implements natural cubic spline methodology to fit potential non-linear dose-response curves in logistic regression models. Likelihood ratio tests were performed to test non-linear and linear relations. In semi-parametric regression models, the median value of the first quintile of exposure was used as reference.The baseline characteristics of the study population are shown in Table 1. Compared to controls, cases had lower annual income and higher total daily caloric intake. Cases were more likely to be current smokers, have hypertension, diabetes, hypercholesterolemia, and a sedentary lifestyle. The median total activity-related energy expenditure was 30.9 METs/day for cases and 32.4 METs/day for controls . Cases spent more time on lying and napping compared to controls. In contrast, controls spent more time on light indoor activities and light-moderate activities . The loadings for the first four components of our PCA are presented in Table 3. The first pattern had high positive loadings on sleep measures and high negative loadings on lying in bed during the day to watch TV, read books, or listen to music, and we labeled it as the rest/ sleep pattern. The second pattern had high positive loadings on items which are used to measure activities relevant to gardening and farming and high negative loadings on standing in very light activities at work or at home, and we labeled it as the agricultural job pattern. The third pattern had high positive loadings on items which are related to activities performed in the office or at home , and we labeled it as the light indoor activity pattern. The last pattern had high positive loadings on items which are used to assess activities related to construction and high negative loadings on napping, and we labeled it as the manual labor job pattern.

We performed PCA stratified by sex. There was no manual labor pattern in women, but the other three physical activity patterns were similar between women and men. Thus, we only report the results from the combined analysis to maximize power. Increased activity-related energy expenditure was associated with area of residence, less annual income, hypertension, higher saturated fat intake, and higher total calorie intake per day among controls . Table 5 summarizes conditional logistic regression models that were used to evaluate the associations between four extracted physical activity patterns, total activity-related energy expenditure, and risk of AMI. The first models were controlled by matching factors , and the fully adjusted models were controlled by matching factors plus adjustment for annual income, smoking status, and saturated fat intake per day. Among the four extracted physical activity patterns, only the light indoor activity pattern was significantly associated with AMI risk. As compared to subjects in the lowest level of component score, the OR for those in the highest level was 0.72 in the model adjusted for matching factors. This association remained statistically significant in the fully adjusted model . However, we observed a U-shaped relationship between the rest/sleep pattern and AMI risk. In the fully adjusted model, compared to subjects in the first quintile of component score,cultivation grow rack the ORs were 0.85 for subjects in the second quintile, 0.79 in the third quintile, 0.87 in the fourth quintile, and 0.85 in the highest quintile. No statistically significant associations were found between the remaining two physical activity patterns and risk of AMI. Total activity-related energy expenditure was negatively associated with risk of AMI. The OR for subjects in the highest vs. lowest category was 0.71 in the model adjusted for matching factors. This association did not change in the fully adjusted model. To further explore the association of AMI risk with the rest/sleep pattern, the light indoor activity pattern, and total activity-related energy expenditure, we fitted natural cubic splines. Models were controlled for the matching factors and potential confounders including annual income, smoking status, and daily saturated fat intake. As shown in Figure 1, there was a non linear relationship between the rest/sleep pattern and risk of AMI . Consistent with the parametric models, there was an inverse linear association between the light indoor activity pattern and risk of AMI . Figure 3 shows that the risk of AMI declined with the increase of total activity-related energy expenditure, but flattened out at high levels of physical activity .Four major physical activity patterns were identified from PCA in this Costa Rican population. The light indoor activity pattern was linearly and inversely associated with risk of AMI, whereas a U-shaped association was found for the rest/sleep pattern. No association was found between the agricultural job pattern and the manual labor job pattern and risk of AMI. In addition, we observed an inverse relationship between total activity related energy expenditure and AMI risk that reached a plateau at high levels.

In this study, we utilized two approaches for exposure response modeling: quintile presentation of the exposure and continuous presentation of the exposure fitting semiparametric models. Compared to the former approach, the latter one has several advantages: no need for the selection of cut-points to categorize exposure, which can influence the shape of a fitted dose-response curve; no power loss; and ease of comparisons across studies. The results from these two analytic approaches were consistent, indicating that semi-parametric models are valuable and powerful to explore the shape of an exposure-response relationship. Previous studies have observed an association between sleep duration and risk of CVD, finding an increased risk of CHD or stroke with habitual sleeping duration of less than 6 hours per night and long sleep duration. The potential mechanisms between decreased sleep duration and risk of CHD are not fully understood but likely include sympathetic over activity, increases in blood pressure, and decreased glucose tolerance. Consistent with these results, we observed a U-shaped association between the rest/sleep pattern and AMI risk. Although the component score of the rest/sleep pattern could not provide the exact range of sleep duration beyond which the risk of AMI would be increased, the majority of the rest sleep pattern is sleeping and our results suggest that either shortened or long sleep duration could increase the risk of CHD. It is possible that longer sleep duration is related to sleep apnea,however we cannot assess this association directly since we did not collected sleep apnea information. On the other hand sleep duration and BMI were not associated in this population Study results on the association between domestic physical activity and CVD risk vary from protective to null. Likewise, studies on the effects of occupational related physical activity on the risk of CVD also have shown inconsistent results ranging from protective effects and null effects, to harmful effects. These inconsistencies might be due to residual confounding effects, distinct definitions of domestic or occupational physical activity, measurement error, and different characteristics of the study population. In our study, the occupational physical activities in the light indoor activity pattern mainly correlated positively with standing and moving at work and inversely with sitting. These activities have been associated with a lower risk of CVD in previous studies. On the other hand, the light indoor activity pattern did not include some strenuous or very strenuous work , which have been found to increase the risk of AMI. We found no associations between the agricultural job pattern and the manual labor job pattern and risk of AMI. While walking and climbing steps could provide beneficial effects on CVD, some strenuous or very strenuous work such as lifting, carrying, and planting could increase the risk of AMI. Thus, it is possible that the protective effects of some activities in the agricultural job and manual labor job patterns, such as walking and climbing steps, are overshadowed by the potential detrimental effects of some very strenuous activities such as lifting and carrying. It is noteworthy that agricultural and manual labor jobs in Costa Rica still include very strenuous activities as opposed to other countries like the US. On the other hand, our null findings may also be the result of measurement error and residual confounding because of imperfect adjustment for socioeconomic status and other lifestyle factors such as diet and smoking. A dose-response relation between physical activity and risk of CVD has been well documented in several large scale prospective studies. However, the exact shape of the dose-response curve remains unclear. Consistent with previous studies, our study indicated that the association between total activity-related energy expenditure and AMI risk is protective. However, we observed that the decreasing risk flattened out at high levels.