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Correlates of total physical activity among middle-aged and elderly women


Information on correlates of total physical activity (PA) levels among middle-aged and elderly women is limited. This article aims to investigate whether total daily PA levels are associated with age, body mass index, smoking, drinking status, and sociodemographic factors.

In a cross-sectional study of 38,988 women between the ages of 48 and 83 years residing in central Sweden, information on PA, weight, height, smoking, drinking, and sociodemographic factors was collected through a self-administered questionnaire. Total daily PA levels were measured as metabolic equivalents (MET-h/day). Odds ratios (OR) and 95% confidence intervals (CI) were estimated by ordinal logistic regression models.

We observed decreasing level of total PA with increasing age (for 5-year increase: OR = 0.87; 95% CI: 0.85–0.89) and body mass index (for 5-unit, kg/m2, increase: OR = 0.81; 95% CI: 0.79–0.84). Multivariable adjusted correlates of total PA level were smoking (current vs. never: OR = 0.83; 95% CI: 0.79–0.88), drinking (current vs. never: OR = 0.88; 95% CI: 0.82–0.94), educational level (university vs. primary: OR = 0.54; 95% CI: 0.51–0.58), employment status (housewife vs. full-work: OR = 2.59; 95% CI: 2.25–2.98), and childhood environment (city vs. countryside: OR = 0.62; 95% CI: 0.59–0.65).

In the present investigation, among middle-aged and elderly women, the likelihood of engaging in higher total daily PA levels decreased with age, body mass index, educational level, smoking, drinking, and growing up in urban places.


An extensive body of epidemiological evidence has shown positive associations between regular physical activity (PA) and health benefits [1]. Several studies support the important role of PA for both primary and secondary prevention of cardiovascular diseases [2]. There is also evidence that regular PA may prevent osteoporosis [3], some forms of cancer [4], type 2 diabetes [5, 6], and may increase longevity [7].

According to a review of correlates of participation in physical activity, middle-and older age groups are more likely to engage in low levels of physical activity than younger age groups, and women are more likely to be inactive than men [8]. In addition, many of the studies included in this review did not incorporate different domains of PA and focused mainly on leisure-time PA [8]. To our knowledge there are no population-based studies investigating different types of correlates of total PA levels among middle-aged and elderly women.

The aim of this study was to investigate, in a cross-sectional setting, the association between age, body mass index, smoking and drinking status, and sociodemographic factors and total daily PA levels in a large population-based cohort of Swedish women.

Study population

The Swedish Mammography Cohort (SMC87) was established between 1987 and 1990, when all (90,303) women aged 40–75 years and living in Västmanland and Uppsala County received a mailed questionnaire about diet, weight, height, and education. Completed questionnaires were obtained from 66,651 women in the source population (74%). We excluded women with missing or incorrect identification codes; missing date of the questionnaire; and missing date of death. After these restrictions our baseline cohort consisted of 61,433 women. In 1997 a more detailed questionnaire (SMC97) was sent to the members of the cohort. This questionnaire included a detailed food frequency questionnaire as well as questions on weight, height, smoking status, alcohol intake, educational level, childhood environment, employment status, and PA. The questionnaire was sent to 56,054 women who participated in the first wave, were alive, and had not moved out from the study area. The response rate was 70% in the SMC97. The study was approved by the Ethics Committees at the Uppsala University and the Karolinska Institutet. Obtaining written information about the study and completion of the questionnaire were considered to imply informed consent.

Physical activity assessment

Measurement of PA was based on a self-administered questionnaire. Five types of past year physical activities were estimated: home/household work, walking/cycling, work/occupation activity, TV/reading, and exercise. To calculate the activity score of specific type of activity, the intensity of these activities defined as metabolic equivalents (MET, kcal/kg/hour) was multiplied by reported time (hours) [5]. An open question asked about time spent sleeping, out of 24 hours per day. We estimated a total daily PA score by adding up the specific activities together [9]. We corrected the self-reported time to 24 hours per day, by adding hours (if the total sum was below 24 hours) or deleting hours (if the total was above 24 hours). This correction time was multiplied by the intensity factor of 2 MET, corresponding to the mean of self-care/walking at home (2.5 MET) and sitting (1.5 MET). This correction was based on the assumption that underestimation of time might be due to these common activities not being asked for in the questionnaire [10]. The PA questionnaire was validated against 2×7-days of activity diary in a group of Swedish men between the ages of 44 to 78 years and was shown to estimate total physical activity satisfactorily (Spearman correlation between the questionnaire and PA records was 0.6) [11]. Reproducibility of the PA questionnaire was evaluated in a subset of the SMC97 and it was shown to be acceptable (Intraclass correlation was 0.7 for the total activity score)[12].

Statistical analysis

To make description and interpretation of the associations easier, we categorized the total daily PA score into quintiles. We used an ordinal logistic regression model, also known as proportional odds model or cumulative logit model, to estimate associations (odds ratios and 95% confidence intervals) between levels of total PA and each correlate in univariable and multivariable models [13]. In this model the estimated odds ratio does not depend on the PA quintiles being compared (≥ k vs. <k, where k ranges between the second to the fifth quintile of PA).

The correlates included in the final model were age in groups (48–54; 55–59; 60–64; 65–69; 70–74; 75–79; 80–83 years); body mass index (BMI <25, 25–29, and ≥ 30 kg/m2); alcohol intake (never, former, and current); smoking status (never, former, and current); postmenopausal status; education (primary school, high, and university); employment status (full-work, part-time, housewife, retired, pension disability, and unemployed); and childhood environment (country side, small town, and city). We used a Wald test to assess whether each correlate (one or more non-referent odds ratios) was statistically significant at 5% level. All statistical analyses were performed using the statistical package Stata (StataCorp LP, Texas, USA).


Our study cohort consisted of 38,988 women between the ages of 48 and 83 years. The mean age was 62 ± 9 years, and mean BMI was 25 ± 4 kg/m2. The mean value of total daily PA was 42.2 ± 4.8 MET-h/day. On average, the major contributors to the 24 hours total activity score were occupational (12.1 MET-h/day), household (8.6 MET-h/day) activities and sleeping (6.4 MET-h/day), then, in order, leisure time physical inactivity (2.9 MET-h/day), walking/bicycling (2.3 MET-h/day) and exercise/sports activities (1.7 MET-h/day).

Cross-tabulations between each correlate and PA levels, together with multivariable adjusted associations are shown in Table 1. We observed a decreasing linear association between total PA and age (for 5-year increase: odds ratio (OR) = 0.87; 95% confidence interval (CI): 0.85–0.89) and BMI (for 5-unit, kg/m2, increase: OR = 0.81; 95% CI: 0.79–0.84). The odds of having higher PA level decreased by 40% (95% CI: 0.56–0.64) for obese women (BMI ≥ 30) when compared with normal weight women. Postmenopausal women were slightly more likely to engage in higher PA levels (OR = 1.09; 95% CI: 1.02–1.15). Being a current drinker or smoker was associated with a decreased likelihood of being classified into the highest category of PA levels. The odds of having higher PA levels decreased by 17% (95% CI: 0.79–0.88) for current smokers when compared with women who never smoked. Higher levels of education (university) were associated with a statistically significant decreased likelihood of having higher PA levels (OR = 0.54; 95% CI: 0.51–0.58). We found marked differences in the total PA levels by employment status. The odds of having higher PA levels was considerably greater among part-time workers (OR = 1.96; 95% CI, 1.85–2.09) and housewives (OR = 2.59; 95% CI: 2.25–2.98) as compared to full-time workers. Growing up in the city, as compared to the countryside, was associated with 38% decrease in the likelihood of engaging in higher levels of PA (95% CI, 0.59–0.65).

Table 1 Frequency counts and multivariable odds ratios with 95% confidence intervals for each correlate of total physical activity levels.


In this analysis of a cohort of middle-aged and elderly women in Sweden we found that the likelihood of engaging in high levels of total PA decreased with age, body mass index, and educational level. Behavioral factors such as drinking and smoking were inversely associated with total PA level. Furthermore, women who grew up in the city were less likely to be in the highest category of total PA levels.

This study had many strengths. Our large population-based cohort of 38,988 women is representative of the Swedish female population in regard to age range, educational status and relative weight [14]. The quantitatively estimated total daily PA is based on a self-administered questionnaire with acceptable validity and reproducibility [11, 12].

One of the concerns of this analysis is that women may not accurately recall their activities. The misclassification in the total activity score is likely to be nonsystematic and would attenuate any associations between total PA and correlates. Although self-reported PA has its limitations, in large population-based cohort studies it is more feasible than the gold standard (doubly labeled water) [15].

One of the difficulties when comparing our study with others is the variation in PA questionnaires and types of measurements used. Quantifying the level of leisure time exercise only may give a distorted understanding of total PA in a population. This is especially true for women who are more likely to engage in light or moderate activities. The majority of previous studies among women examined the influence of individual, social, and environmental factors on some aspects of PA behavior (mostly leisure-time or sport/exercise activities) [8, 1618].

Our results, that PA declines with age, are in agreement with previous studies that were based on doubly labeled water [19]. BMI and PA are inextricably linked. Our results support numerous studies that found an inverse association between PA and obesity [2022]. It is known that health-risk behaviors such as smoking, drinking, and inadequate levels of PA tend to cluster together [16], as we indeed observed in the present study. A possible explanation of the weak positive association between postmenopausal status and total PA is that postmenopausal status could act as a proxy for unmeasured factors associated with total PA. For instance, postmenopausal women are more likely to be retired and therefore have more free time available (lack of time constraints).

In our study occupational and household activities were two major contributors of the total daily activity score that is a combination of intensity and duration. Educational level was inversely associated with total daily PA level perhaps because higher educated women tend to have lighter or more sedentary jobs and less time for household activities [23]. Previous studies that focused on leisure-time PA only found that higher levels of education were associated with higher levels of leisure-time PA [24, 25]. In addition, a study among Australian women (mean age 43 years) also found the positive association between higher occupational status and leisure-time PA, which remained unchanged even after taking into account occupational/home PA [26].

A comparison of different studies, however, should be based on the same type of physical activities and age range. Estimating only leisure-time activities may however give an unclear picture of the levels of total PA, since a heavy manual worker with no leisure-time activity would be classified as inactive, while a sedentary worker that engages in volleyball twice a week would be classified as active. Therefore, findings that are not entirely consistent across studies highlight the importance of assessing different domains of PA [26].

A recent review about correlates of adults' participation in PA shows how different factors (demographic, biological, psychological, emotional, social, cultural and environmental) can affect PA patterns [8]. In particular, over the past decade there has been a growing recognition of the role of the environment in affecting healthy behaviors [27]. Our finding that the environment in which these women grew up is related to the current total PA seems to support a life course approach to chronic disease epidemiology where time and timing of exposure-disease associations are important [28]. Furthermore, our findings seem to support previous studies showing higher PA levels in women residing in rural areas as compared to urban areas [2931]. Given that PA is a complex behavior, the intensity and the direction of these associations might differ between populations. For instance, a study among US women aged 40 years and older found that rural women were less likely to engage in high leisure time activity levels in comparison to women residing in urban areas [25]. A possible explanation of these discrepancies is the different set of characteristics (socio-demographic and environment) associated with urban and rural settings in different areas of the world.


Our findings contribute to evidence of the correlates of such complex behavior as physical activity among middle-aged and elderly women. Since engaging and maintaining regular PA level plays a key role in reducing several public health problems, the identification of significant correlates may help researchers, clinicians, and health policy makers to design gender-specific interventions.

In summary, in this study among middle-aged and elderly women, the likelihood of engaging in higher total daily PA levels decreased with age, body mass index, educational level, smoking, drinking, and growing up in urban places.


  1. Pate RR, Pratt M, Blair SN, Haskell WL, Macera CA, Bouchard C, Buchner D, Ettinger W, Heath GW, King AC, et al: Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995, 273 (5): 402-407. 10.1001/jama.273.5.402.

    Article  CAS  Google Scholar 

  2. 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.

    Article  CAS  Google Scholar 

  3. Krall EA, Dawson-Hughes B: Walking is related to bone density and rates of bone loss. Am J Med. 1994, 96 (1): 20-26. 10.1016/0002-9343(94)90111-2.

    Article  CAS  Google Scholar 

  4. Kramer MM, Wells CL: Does physical activity reduce risk of estrogen-dependent cancer in women?. Med Sci Sports Exerc. 1996, 28 (3): 322-334. 10.1097/00005768-199603000-00008.

    CAS  Google Scholar 

  5. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, O'Brien WL, Bassett DR, Schmitz KH, Emplaincourt PO, Jacobs DR, Leon AS: Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000, 32 (9 Suppl): S498-504.

    Article  CAS  Google Scholar 

  6. Colditz GA, Coakley E: Weight, weight gain, activity, and major illnesses: the Nurses' Health Study. Int J Sports Med. 1997, 18 Suppl 3: S162-70. 10.1055/s-2007-972709.

    Article  CAS  Google Scholar 

  7. Oguma Y, Sesso HD, Paffenbarger RS, Lee IM: Physical activity and all cause mortality in women: a review of the evidence. Br J Sports Med. 2002, 36 (3): 162-172. 10.1136/bjsm.36.3.162.

    Article  CAS  Google Scholar 

  8. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W: Correlates of adults' participation in physical activity: review and update. Med Sci Sports Exerc. 2002, 34 (12): 1996-2001. 10.1097/00005768-200212000-00020.

    Article  Google Scholar 

  9. Orsini N, Bellocco R, Bottai M, Pagano M, Wolk A: Age and temporal trends of total physical activity among Swedish women. Med Sci Sports Exerc. 2006, 38 (2): 240-245. 10.1249/01.mss.0000185086.19220.b3.

    Article  Google Scholar 

  10. Norman A, Bellocco R, Vaida F, Wolk A: Total physical activity in relation to age, body mass, health and other factors in a cohort of Swedish men. Int J Obes Relat Metab Disord. 2002, 26 (5): 670-675. 10.1038/sj.ijo.0801955.

    Article  CAS  Google Scholar 

  11. Norman A, Bellocco R, Bergstrom A, Wolk A: Validity and reproducibility of self-reported total physical activity--differences by relative weight. Int J Obes Relat Metab Disord. 2001, 25 (5): 682-688. 10.1038/sj.ijo.0801597.

    Article  CAS  Google Scholar 

  12. Orsini N, Bellocco R, Bottai M, Pagano M, Wolk A: Reproducibility of the past year and historical self-administered total physical activity questionnaire among older women. Eur J Epidemiol. 2007, in press.

    Google Scholar 

  13. Armstrong BG, Sloan M: Ordinal regression models for epidemiologic data. Am J Epidemiol. 1989, 129 (1): 191-204.

    CAS  Google Scholar 

  14. Lissner L, Johansson SE, Qvist J, Rossner S, Wolk A: Social mapping of the obesity epidemic in Sweden. Int J Obes Relat Metab Disord. 2000, 24 (6): 801-805. 10.1038/sj.ijo.0801237.

    Article  CAS  Google Scholar 

  15. Melanson EL, Freedson PS: Physical activity assessment: a review of methods. Crit Rev Food Sci Nutr. 1996, 36 (5): 385-396.

    Article  Google Scholar 

  16. Sherwood NE, Jeffery RW: The behavioral determinants of exercise: implications for physical activity interventions. Annu Rev Nutr. 2000, 20: 21-44. 10.1146/annurev.nutr.20.1.21.

    Article  CAS  Google Scholar 

  17. Lindstrom M, Hanson BS, Ostergren PO: Socioeconomic differences in leisure-time physical activity: the role of social participation and social capital in shaping health related behaviour. Soc Sci Med. 2001, 52 (3): 441-451. 10.1016/S0277-9536(00)00153-2.

    Article  CAS  Google Scholar 

  18. Burton NW, Turrell G: Occupation, hours worked, and leisure-time physical activity. Prev Med. 2000, 31 (6): 673-681. 10.1006/pmed.2000.0763.

    Article  CAS  Google Scholar 

  19. Westerterp KR: Daily physical activity and ageing. Curr Opin Clin Nutr Metab Care. 2000, 3 (6): 485-488. 10.1097/00075197-200011000-00011.

    Article  CAS  Google Scholar 

  20. Martinez-Gonzalez MA, Martinez JA, Hu FB, Gibney MJ, Kearney J: Physical inactivity, sedentary lifestyle and obesity in the European Union. Int J Obes Relat Metab Disord. 1999, 23 (11): 1192-1201. 10.1038/sj.ijo.0801049.

    Article  CAS  Google Scholar 

  21. Hill JO, Peters JC: Environmental contributions to the obesity epidemic. Science. 1998, 280 (5368): 1371-1374. 10.1126/science.280.5368.1371.

    Article  CAS  Google Scholar 

  22. Owens JF, Matthews KA, Wing RR, Kuller LH: Physical activity and cardiovascular risk: a cross-sectional study of middle-aged premenopausal women. Prev Med. 1990, 19 (2): 147-157. 10.1016/0091-7435(90)90016-D.

    Article  CAS  Google Scholar 

  23. Pereira MA, Kriska AM, Collins VR, Dowse GK, Tuomilehto J, Alberti KG, Gareeboo H, Hemraj F, Purran A, Fareed D, Brissonnette G, Zimmet PZ: Occupational status and cardiovascular disease risk factors in the rapidly developing, high-risk population of Mauritius. Am J Epidemiol. 1998, 148 (2): 148-159.

    Article  CAS  Google Scholar 

  24. King AC, Castro C, Wilcox S, Eyler AA, Sallis JF, Brownson RC: Personal and environmental factors associated with physical inactivity among different racial-ethnic groups of U.S. middle-aged and older-aged women. Health Psychol. 2000, 19 (4): 354-364. 10.1037/0278-6133.19.4.354.

    Article  CAS  Google Scholar 

  25. Wilcox S, Castro C, King AC, Housemann R, Brownson RC: Determinants of leisure time physical activity in rural compared with urban older and ethnically diverse women in the United States. J Epidemiol Community Health. 2000, 54 (9): 667-672. 10.1136/jech.54.9.667.

    Article  CAS  Google Scholar 

  26. Salmon J, Owen N, Bauman A, Schmitz MK, Booth M: Leisure-time, occupational, and household physical activity among professional, skilled, and less-skilled workers and homemakers. Prev Med. 2000, 30 (3): 191-199. 10.1006/pmed.1999.0619.

    Article  CAS  Google Scholar 

  27. Ball K, Timperio AF, Crawford DA: Understanding environmental influences on nutrition and physical activity behaviors: where should we look and what should we count?. Int J Behav Nutr Phys Act. 2006, 3: 33-10.1186/1479-5868-3-33.

    Article  Google Scholar 

  28. Lynch J, Smith GD: A life course approach to chronic disease epidemiology. Annu Rev Public Health. 2005, 26: 1-35. 10.1146/annurev.publhealth.26.021304.144505.

    Article  Google Scholar 

  29. Potvin L, Gauvin L, Nguyen NM: Prevalence of stages of change for physical activity in rural, suburban and inner-city communities. J Community Health. 1997, 22 (1): 1-13. 10.1023/A:1025161522683.

    Article  CAS  Google Scholar 

  30. Bertrais S, Preziosi P, Mennen L, Galan P, Hercberg S, Oppert JM: Sociodemographic and geographic correlates of meeting current recommendations for physical activity in middle-aged French adults: the Supplementation en Vitamines et Mineraux Antioxydants (SUVIMAX) Study. Am J Public Health. 2004, 94 (9): 1560-1566.

    Article  Google Scholar 

  31. Plotnikoff RC, Mayhew A, Birkett N, Loucaides CA, Fodor G: Age, gender, and urban-rural differences in the correlates of physical activity. Prev Med. 2004, 39 (6): 1115-1125. 10.1016/j.ypmed.2004.04.024.

    Article  Google Scholar 

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This study was supported by Swedish Council for Working life and Social Research (FAS), the Swedish Research Council/Longitudinal studies, World Cancer Research Fund International, and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT).

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Correspondence to Nicola Orsini.

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The author(s) declare that they have no competing interests.

Authors' contributions

NO analyzed the data and drafted the manuscript incorporating critical inputs from all authors. AW is a principal investigator of the cohort; she conceived the study, participated in its design and coordination. RB, MB, MP, and AW provided critical revision of the paper and assisted with the analysis and interpretation. All authors have read and approved the final manuscript.

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Orsini, N., Bellocco, R., Bottai, M. et al. Correlates of total physical activity among middle-aged and elderly women. Int J Behav Nutr Phys Act 4, 16 (2007).

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