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A case-control study of physical activity patterns and risk of non-fatal myocardial infarction
© Gong et al.; licensee BioMed Central Ltd. 2013
Received: 5 July 2012
Accepted: 4 February 2013
Published: 8 February 2013
The interactive effects of different types of physical activity on cardiovascular disease (CVD) risk have not been fully considered in previous studies. 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).
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. 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.
Four 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 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. 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).
These data suggest that a light indoor activity pattern is associated with reduced AMI risk. PCA provides a new approach to investigate the relationship between physical activity and CVD risk.
Numerous observational epidemiologic studies have demonstrated that physical activity is inversely related to cardiovascular morbidity and mortality [1–4]. Physical activity may contribute up to 20% - 30% reduced risk of coronary heart disease [5, 6]. 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 [7–12]. For example, some leisure time activities such as walking, stair climbing, and cycling provide protection against CVD [7–12], whereas others, such as intensive domestic physical activity, may not offer protection against CVD . 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) . 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. Furthermore, studying different types of physical activity in isolation may not adequately consider any joint and interactive associations on the risk of CVD.
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. As one type of physical activity increases, total physical activity increases as well, given that the other physical activities are fixed. Hence, the effect estimate of one type of physical activity does not present its pure effect, but includes the effects of total physical activity.
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. 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). Data from a population-based, case-control study in Costa Rica were utilized for purposes of this investigation.
In Costa Rica, CVD has been the country’s leading cause of death since 1970 and the mortality rate for CVD has been declining since 2002 according to 2007 Health in the Americas, a report from World Health Organization. The participants in this study are cases and controls from a case-control study of non-fatal myocardial infarction conducted in the Central Valley in Costa Rica from 1994 to 2004. The study design and population have been described previously [14, 15]. In brief, eligible cases were men and women who were diagnosed as survivors of a first AMI by two independent cardiologists at any of the six recruiting hospitals in the Central Valley of Costa Rica during the period 1994-2004. All cases met the World Health Organization criteria for AMI . Enrollment was carried out while cases were in the hospital’s step-down-unit. One free-living control subject for each case, matched for age (± 5 years), sex, and area of residence (county), was randomly selected using information available at the National Census and Statistics Bureau of Costa Rica. Participation rates were 98% for cases and 88% for controls. Cases and controls provided informed consent on documents approved by the Human Subjects Committee of the Harvard School of Public Health and the University of Costa Rica.
Trained interviewers visited all study participants at their homes for purposes of collecting sociodemographic characteristics, physical activity, lifestyle, medical history, smoking, and dietary data by use of a standardized questionnaire . They visited cases, on average, within 3 weeks of hospital discharge (for controls, hospital discharge of the corresponding case subject) and when possible, by the same interviewer. Identical questionnaires and data collection procedures were used for cases and controls. 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 (METs): lying quietly in bed: afternoon nap or rest and night sleep (0.9 METs); sitting (1.0 METs); light indoor activity such as standing at work or at home (2.4 METs); moderate outdoor activity such as gardening, light agriculture and construction, and walking on flat surfaces (3.6 METs); vigorous aerobic activity such as heavy agriculture and construction, walking uphill, climbing stairs, jogging and other sports (7.1 METs); strenuous anaerobic activity such as carrying, pushing and lifting heavy objects (7.8 METs). Energy expenditure for each activity was calculated as the product of frequency, time, and intensity (METs). 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 [17, 18]. 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 (Version 9.1; SAS Institute, Cary, NC). 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 (n=139), implausible total activity-related energy expenditure (> 2 SD from the mean energy expenditure, n=187), and losing matched controls/cases after performing rematching based on the original matching criteria (n=223). The final study sample consisted of 1999 case-control pairs (total n=3998). We used PCA on the 18 questions of the standardized activity questionnaire to identify physical activity patterns. The components (i.e. physical activity patterns) 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. The higher component scores indicate better adherence to a certain physical activity pattern. 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 (conditional logistic regression) and semi-parametric regression models (natural cubic splines) 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 (total METs per day) were divided into quintiles. Quintiles of those variables were entered in multivariate conditional logistic regression analysis to calculate odds ratios (OR) 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 piecewise third-order polynomials which pass through a set of control points and it is linear in its tail beyond the boundary knots [19–21]. Since they are numerically stable and allow computation of fit with great accuracy, natural cubic splines are widely used in semi-parametric 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.
Basic characteristics of first AMI survivors and matched controls in a case control study, Costa Rica, 1994 - 2004
Area of residence, % urban
Current smoker (%)
Total fat, % energy
Saturated fat, % energy
Monounsaturated fat, % energy
Polyunsaturated fat, % energy
Carbohydrate, % energy
Protein, % energy
Total calorie intake (kcal/day)
Activity-related energy expenditure and time spent on different daily activities in a case control study, Costa Rica, 1994 - 2004
Activity-related energy expenditure (METs/day)
All physical activities
Lying and napping
Light indoor activities
Physical activity patterns from PCA in a case control study, Costa Rica, 1994 - 2004
Sleep during weekday
Sleep during weekend
Lie in bed during the day to watch TV, read, and listen to music
Sit, either at work or in activities such as driving, watching TV
Stand in very light activities at work or at home such as filing, coping, and doing laundry
Stand cleaning in general such as moping, brooming, garage, washing windows, and sidewalk
Standing and squatting in the garden work such as weeding and watering
Work in agriculture (not vigorously) such as planting, picking coffee, and cultivating.
Work in construction such as painting, chopping wood, and carpentry
Walk on flat terrain in the city
Do heavy and vigorous jobs which made you sweat such as shovelling, digging ditches, cutting trees
Walk on mountainous terrain (farm)
Practice sports, i.e. teams, such as soccer, basketball, and volleyball
Practice sports, i.e. running, bicycling, swimming, etc.
Practice any other sports (not listed above)
Move or carry very heavy items which made you sweat such as carrying furniture, luggage, and water
Characteristics by quintiles of total activity-related energy expenditure (METs/day) among controls in a case control study, Costa Rica, 1994 - 2004
Total activity-related energy expenditure (METs / day)
Area of residence, %urban
Current smoker (%)
Plasma Triglyceride (mg/dl)
Plasma HDL (mg/dl)
Saturated fat intake (mg/day)
Total calorie intake (kcal/day)
TEEa in light indoor activities
TEE in light-moderate activities
TEE in vigorous activities
TEE in Sports
TEE in Sleeping
Odds ratios and 95% confidence interval for AMI according to quintiles of scores for four physical activity patterns and daily total activity-related energy expenditure in a case control study, Costa Rica, 1994 - 2004
Quintiles of component scores for the first factor (rest/sleep)
Quintiles of component scores for the second factor (agricultural job)
Quintiles of component scores for the third factor (light indoor activity)
Quintiles of component scores for the fourth factor (manual labor job)
Quintiles of total activity-related energy expenditure (METs / day)
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 semi-parametric 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 [20, 23]. 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 [24–27] and long sleep duration (sleep duration >9 hours/night) [24, 25]. The potential mechanisms between decreased sleep duration and risk of CHD are not fully understood but likely include sympathetic overactivity, 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 (data not shown)
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 [29, 30] and null effects [31, 32], to harmful effects [33, 34]. 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 [7, 9]. On the other hand, the light indoor activity pattern did not include some strenuous or very strenuous work (e.g. lifting, carrying, and planting workload), 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 [9, 12], 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 [35–38]. However, the exact shape of the dose-response curve remains unclear. Consistent with previous studies [35–38], 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. Occupational physical activities contributed to high levels of total activity-related energy expenditure in our study (Table 4), and we did not find an association of AMI risk with the agricultural or manual labor job patterns.
Our study has several limitations that must be kept in mind in interpreting our study findings. Our study is a case-control study and, thus, the temporal relationship between physical activity and AMI risk is unclear. As in all observational studies, we cannot establish causal associations. Self-reported physical activity measurements contain large measurement error [39, 40], which may lead to underestimate the effect of physical activity on AMI risk . Recall bias is an issue in case-control studies. If controls are more likely to under-report daily physical activities than cases, the results could be biased towards the null hypothesis; if controls, due to social desirability, overestimate their physical activities while cases do not, then the effects of physical activity could be overestimated. However, our results on total activity-related energy expenditure are consistent with those from previous studies. Thus, recall bias is less likely to play a role in our study. Another potential limitation is that cases only included survivors of a first AMI. We cannot exclude residual confounding in our estimates. For example, occupation stress, a potential confounder, was not accounted for in our study because the information was not available. Our results may not be generalizable to other populations, since physical activity patterns are likely to vary according to many factors such as population level economic development, individual level socioeconomic status, the built environment, and distribution of leisure and occupational activities.
In conclusion, principal component analysis provides a new approach to investigate the relationship between physical activity and CVD risk and semi-parametric regression models could be a valuable method to explore exposure-response associations. Based on these approaches, we found that the light indoor activity pattern was inversely associated with risk of AMI, a U-shaped association was found for the rest/sleep pattern, and we confirmed the negative but nonlinear association between total activity-related energy expenditure and AMI risk. Further research on different populations is required to validate the application of PCA to deriving physical activity patterns and confirm our findings.
This work was supported by the National Institutes of Health (HL49086, HL60692, and HL081549).
- Kokkinos P, Myers J, Kokkinos JP, Pittaras A, Narayan P, Manolis A, Karasik P, Greenberg M, Papademetriou V, Singh S: Exercise capacity and mortality in black and white men. Circulation. 2008, 117 (5): 614-622. 10.1161/CIRCULATIONAHA.107.734764.View ArticlePubMedGoogle Scholar
- Manson JE, Hu FB, Rich-Edwards JW, Colditz GA, Stampfer MJ, Willett WC, Speizer FE, Hennekens CH: A prospective study of walking as compared with vigorous exercise in the prevention of coronary heart disease in women. N Engl J Med. 1999, 341 (9): 650-658. 10.1056/NEJM199908263410904.View ArticlePubMedGoogle Scholar
- Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE: Exercise capacity and mortality among men referred for exercise testing. N Engl J Med. 2002, 346 (11): 793-801. 10.1056/NEJMoa011858.View ArticlePubMedGoogle Scholar
- Oguma Y, Shinoda-Tagawa T: Physical activity decreases cardiovascular disease risk in women: review and meta-analysis. Am J Prev Med. 2004, 26 (5): 407-418. 10.1016/j.amepre.2004.02.007.View ArticlePubMedGoogle Scholar
- Sofi F, Capalbo A, Cesari F, Abbate R, Gensini GF: Physical activity during leisure time and primary prevention of coronary heart disease: an updated meta-analysis of cohort studies. Eur J Cardiovasc Prev Rehabil. 2008, 15 (3): 247-257. 10.1097/HJR.0b013e3282f232ac.View ArticlePubMedGoogle Scholar
- Physical Activity Guidelines Advisory Committee Report: Physical Activity Guidelines advisory Committee. 2008, Physical Activity Guidelines advisory CommitteeGoogle Scholar
- Barengo NC, Hu G, Lakka TA, Pekkarinen H, Nissinen A, Tuomilehto J: Low physical activity as a predictor for total and cardiovascular disease mortality in middle-aged men and women in Finland. Eur Heart J. 2004, 25 (24): 2204-2211. 10.1016/j.ehj.2004.10.009.View ArticlePubMedGoogle Scholar
- Boreham CA, Kennedy RA, Murphy MH, Tully M, Wallace WF, Young I: Training effects of short bouts of stair climbing on cardiorespiratory fitness, blood lipids, and homocysteine in sedentary young women. Br J Sports Med. 2005, 39 (9): 590-593. 10.1136/bjsm.2002.001131.View ArticlePubMedPubMed CentralGoogle Scholar
- Fransson E, De Faire U, Ahlbom A, Reuterwall C, Hallqvist J, Alfredsson L: The risk of acute myocardial infarction: interactions of types of physical activity. Epidemiology. 2004, 15 (5): 573-582. 10.1097/01.ede.0000134865.74261.fe.View ArticlePubMedGoogle Scholar
- Fransson EI, Alfredsson LS, de Faire UH, Knutsson A, Westerholm PJ: Leisure time, occupational and household physical activity, and risk factors for cardiovascular disease in working men and women: the WOLF study. Scand J Public Health. 2003, 31 (5): 324-333. 10.1080/14034940210165055.View ArticlePubMedGoogle Scholar
- Stamatakis E, Hamer M, Lawlor DA: Physical activity, mortality, and cardiovascular disease: is domestic physical activity beneficial? The Scottish Health Survey -- 1995, 1998, and 2003. Am J Epidemiol. 2009, 169 (10): 1191-1200. 10.1093/aje/kwp042.View ArticlePubMedGoogle Scholar
- Yu S, Yarnell JW, Sweetnam PM, Murray L: What level of physical activity protects against premature cardiovascular death? The Caerphilly study. Heart. 2003, 89 (5): 502-506. 10.1136/heart.89.5.502.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson RA, Wichern DW: Principal components. Applied multivariate statistical analysis. Edited by: Johnson RA, Wichern DW. 2002, Upper Saddle River, N.J: Prentice Hall, 767-5Google Scholar
- Baylin A, Kabagambe EK, Ascherio A, Spiegelman D, Campos H: Adipose tissue alpha-linolenic acid and nonfatal acute myocardial infarction in Costa Rica. Circulation. 2003, 107 (12): 1586-1591. 10.1161/01.CIR.0000058165.81208.C6.View ArticlePubMedGoogle Scholar
- Campos H, Siles X: Siesta and the risk of coronary heart disease: results from a population-based, case-control study in Costa Rica. Int J Epidemiol. 2000, 29 (3): 429-437. 10.1093/ije/29.3.429.View ArticlePubMedGoogle Scholar
- Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A: Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation. 1994, 90 (1): 583-612. 10.1161/01.CIR.90.1.583.View ArticlePubMedGoogle Scholar
- Campos H, Mata L, Siles X, Vives M, Ordovas JM, Schaefer EJ: Prevalence of cardiovascular risk factors in rural and urban Costa Rica. Circulation. 1992, 85 (2): 648-658. 10.1161/01.CIR.85.2.648.View ArticlePubMedGoogle Scholar
- Campos H, Bailey SM, Gussak LS, Siles X, Ordovas JM, Schaefer EJ: Relations of body habitus, fitness level, and cardiovascular risk factors including lipoproteins and apolipoproteins in a rural and urban Costa Rican population. Arterioscler Thromb. 1991, 11 (4): 1077-1088. 10.1161/01.ATV.11.4.1077.View ArticlePubMedGoogle Scholar
- Durrleman S, Simon R: Flexible regression models with cubic splines. Stat Med. 1989, 8 (5): 551-561. 10.1002/sim.4780080504.View ArticlePubMedGoogle Scholar
- Ruppert D, Wand MP, Carroll RJ: Semiparametric regression. 2003, New York: Cambridge University PressView ArticleGoogle Scholar
- Herndon JE, Harrell FE: The restricted cubic spline as baseline hazard in the proportional hazards model with step function time-dependent covariables. Stat Med. 1995, 14 (19): 2119-2129. 10.1002/sim.4780141906.View ArticlePubMedGoogle Scholar
- Govindarajulu US, Spiegelman D, Thurston SW, Ganguli B, Eisen EA: Comparing smoothing techniques in Cox models for exposure-response relationships. Stat Med. 2007, 26 (20): 3735-3752. 10.1002/sim.2848.View ArticlePubMedGoogle Scholar
- Willett W: Nutritional epidemiology. 1998, New York: Oxford University Press, 2View ArticleGoogle Scholar
- Meisinger C, Heier M, Lowel H, Schneider A, Doring A: Sleep duration and sleep complaints and risk of myocardial infarction in middle-aged men and women from the general population: the MONICA/KORA Augsburg cohort study. Sleep. 2007, 30 (9): 1121-1127.PubMedPubMed CentralGoogle Scholar
- Ayas NT, White DP, Manson JE, Stampfer MJ, Speizer FE, Malhotra A, Hu FB: A prospective study of sleep duration and coronary heart disease in women. Arch Intern Med. 2003, 163 (2): 205-209. 10.1001/archinte.163.2.205.View ArticlePubMedGoogle Scholar
- Qureshi AI, Giles WH, Croft JB, Bliwise DL: Habitual sleep patterns and risk for stroke and coronary heart disease: a 10-year follow-up from NHANES I. Neurology. 1997, 48 (4): 904-911. 10.1212/WNL.48.4.904.View ArticlePubMedGoogle Scholar
- Chen JC, Brunner RL, Ren H, Wassertheil-Smoller S, Larson JC, Levine DW, Allison M, Naughton MJ, Stefanick ML: Sleep duration and risk of ischemic stroke in postmenopausal women. Stroke. 2008, 39 (12): 3185-3192. 10.1161/STROKEAHA.108.521773.View ArticlePubMedPubMed CentralGoogle Scholar
- Ohayon MM: From wakefulness to excessive sleepiness: what we know and still need to know. Sleep Med Rev. 2008, 12 (2): 129-141. 10.1016/j.smrv.2008.01.001.View ArticlePubMedPubMed CentralGoogle Scholar
- Salonen JT, Slater JS, Tuomilehto J, Rauramaa R: Leisure time and occupational physical activity: risk of death from ischemic heart disease. Am J Epidemiol. 1988, 127 (1): 87-94.PubMedGoogle Scholar
- Menotti A, Lanti M: Coronary risk factors predicting early and late coronary deaths. Heart. 2003, 89 (1): 19-24. 10.1136/heart.89.1.19.View ArticlePubMedPubMed CentralGoogle Scholar
- Rosengren A, Wilhelmsen L: Physical activity protects against coronary death and deaths from all causes in middle-aged men. Evidence from a 20-year follow-up of the primary prevention study in Goteborg. Ann Epidemiol. 1997, 7 (1): 69-75. 10.1016/S1047-2797(96)00106-8.View ArticlePubMedGoogle Scholar
- Wagner A, Simon C, Evans A, Ferrieres J, Montaye M, Ducimetiere P, Arveiler D: Physical activity and coronary event incidence in Northern Ireland and France: the Prospective Epidemiological Study of Myocardial Infarction (PRIME). Circulation. 2002, 105 (19): 2247-2252. 10.1161/01.CIR.0000016345.58696.4F.View ArticlePubMedGoogle Scholar
- Stender M, Hense HW, Doring A, Keil U: Physical activity at work and cardiovascular disease risk: results from the MONICA Augsburg study. Int J Epidemiol. 1993, 22 (4): 644-650. 10.1093/ije/22.4.644.View ArticlePubMedGoogle Scholar
- Tuomi K: Characteristics of work and life predicting coronary heart disease. Finnish research project on aging workers. Soc Sci Med. 1994, 38 (11): 1509-1519. 10.1016/0277-9536(94)90113-9.View ArticlePubMedGoogle Scholar
- Lees SJ, Booth FW: Sedentary death syndrome. Can J Appl Physiol. 2004, 29 (4): 447-460. 10.1139/h04-029. discussion 444-446View ArticlePubMedGoogle Scholar
- Kesaniemi YK, Danforth E, Jensen MD, Kopelman PG, Lefebvre P, Reeder BA: Dose-response issues concerning physical activity and health: an evidence-based symposium. Med Sci Sports Exerc. 2001, 33 (6 Suppl): S351-358.PubMedGoogle Scholar
- Blair SN, Kampert JB, Kohl HW, Barlow CE, Macera CA, Paffenbarger RS, Gibbons LW: Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996, 276 (3): 205-210. 10.1001/jama.1996.03540030039029.View ArticlePubMedGoogle Scholar
- Kohl HW: 3rd: Physical activity and cardiovascular disease: evidence for a dose response. Med Sci Sports Exerc. 2001, 33 (6 Suppl): 472-483. discussion S493-474View ArticleGoogle Scholar
- Philippaerts RM, Westerterp KR, Lefevre J: Comparison of two questionnaires with a tri-axial accelerometer to assess physical activity patterns. Int J Sports Med. 2001, 22 (1): 34-39. 10.1055/s-2001-11359.View ArticlePubMedGoogle Scholar
- Adams SA, Matthews CE, Ebbeling CB, Moore CG, Cunningham JE, Fulton J, Hebert JR: The effect of social desirability and social approval on self-reports of physical activity. Am J Epidemiol. 2005, 161 (4): 389-398. 10.1093/aje/kwi054.View ArticlePubMedPubMed CentralGoogle Scholar
- Ferrari P, Friedenreich C, Matthews CE: The role of measurement error in estimating levels of physical activity. Am J Epidemiol. 2007, 166 (7): 832-840. 10.1093/aje/kwm148.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/13/122/prepub
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