Accelerometer-Determined Physical Activity in Adults and Older People : Medicine & Science in Sports & Exercise

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Accelerometer-Determined Physical Activity in Adults and Older People


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Medicine & Science in Sports & Exercise 44(2):p 266-272, February 2012. | DOI: 10.1249/MSS.0b013e31822cb354
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Regular physical activity (PA) is beneficial for preventing noncommunicable diseases and obesity (34). Although the numerous health benefits of PA are well documented, population levels are suspected to be low (11). Therefore, increasing PA and decreasing sedentary activity are important targets of public health promotion. Although both global (2,33) and national (20) health initiatives include focus on national monitoring of PA levels, there is a lack of large-scale comparable data on PA at the population level.

PA epidemiology has traditionally been based on self-report methods, which by nature are susceptible to many forms of bias (25). The method has substantial limitations for accurately quantifying PA levels and often produces contradictory evidence compared with measurements based on objective methods (22). This contradictory evidence hampers comparisons between studies and makes it difficult to assess the population level of PA adequately.

Because of the limitations of self-report, interest in objective measurements of PA has increased (27). The use of accelerometers allows for accurate measurement of intermittent and spontaneous intensity-specific PA and is currently viewed as the minimum standard for PA assessment in epidemiological research (3).

In children, data on objectively measured PA are accumulating rapidly, and large-scale studies from several countries have compared across groups on the basis of sex, age, social class, region, and country (12,23,24). We are aware of only two studies that used objective measurement of PA in nationally representative samples of adults and older people in Western populations and one study in an Asian population (4,21,28). Hence, there is a need for more studies in the adult population.

The purpose of this study was to assess objectively the levels of PA and sedentary activity in adults and older people living in Norway. We wanted to explore the overall activity levels by age and sex and to determine the percentage of the population meeting the current national PA recommendations to accumulate at least 30 min of daily moderate-intensity PA.



This was a multicenter study involving 10 regional test centers throughout Norway. A representative sample of 11,515 adults (20–85 yr) from the areas surrounding each test center was drawn from the Norwegian population registry. The only inclusion criterion was that the participants had to be between ages 20 and 85 yr. The recruitment strategy included several mechanisms, such as local media coverage, personalized invitational letters, and offers of individual survey reports. The study information and informed consent were distributed via mail to the representative sample; 267 invitations were returned because of an unknown address. This resulted in an eligible sample of 11,248 individuals invited to participate. Written informed consent was obtained from 3867 subjects (34%). Three hundred eight-two subjects did not return any data, giving a final sample of 3485 participants. Of the final sample, 86 did not wear the accelerometer, 14 had defective monitors, and 118 participants were excluded for providing less than 4 d of valid accelerometer data, giving an analyzed sample of 3267 participants (94% of the final sample) with at least 4 d of valid accelerometer recordings. The study was approved by the Regional Ethics Committee for Medical Research, the Norwegian Social Science Data Services, and the Norwegian Tax Department.

Assessment of PA

The ActiGraph GT1M (ActiGraph, LLC, Pensacola, FL) was used to assess the participants’ PA level. The accelerometer is lightweight (27 g) and small (3.8 × 3.7 × 1.8 cm) and comprises a solid-state monolithic accelerometer that uses microprocessor digital filtering. The accelerometer’s response to 1g acceleration of the earth is fixed upon installing it into the circuit, which removes the need for unit calibration (13). The accelerometer registers vertical acceleration in units called counts and samples data at a rate of 30 times per second in user-defined sampling intervals (epochs). The number of steps per day was registered as number of cycles of the signal, which is claimed to be representative of the number of steps taken (14).

The participants received a preprogrammed accelerometer by mail. Standardized instructions included how to wear the accelerometer over the right hip in an elastic band while awake and to remove it for water activities such as swimming. The participants wore the accelerometer for seven consecutive days. After the registration period, the participants returned the accelerometers by prepaid express mail.

The accelerometers were initialized and downloaded using the ActiLife software provided by the manufacturer (ActiGraph LLC). The data were collected in 10-s epochs. To analyze the data, the 10-s epochs were collapsed into 60-s epochs for comparisons with other studies. The data were reduced using an SAS-based software program (SAS Institute, Inc., Cary, NC) called the CSA Analyzer ( Data were included if the subject had accumulated a minimum of 10 h of valid activity recordings per day for at least 4 d, which is in accordance with similar studies (5) and in line with the suggestions by Trost et al. (29). Wear time was defined by subtracting nonwear time from 18 h (all data between midnight and 6:00 a.m. were excluded). Nonwear time was defined as intervals of at least 60 consecutive minutes with zero counts, with allowance for 1 min with counts greater than zero.

The PA levels assessed by the accelerometer are presented as 1) mean counts per minute (cpm), 2) number of minutes spent in intensity-specific categories, 3) number of steps registered per day, 4) percentage of the study population meeting the national PA recommendations, and 5) percentage of the study population accumulating ≥10,000 steps per day.

Counts per minute (cpm) is a measure of overall PA and was expressed as the total number of registered counts for all valid days divided by wearing time. To identify PA of different intensities, count thresholds corresponding to the energy cost of the given intensity were applied to the data set. Sedentary activity was defined as all activity below 100 cpm, a threshold that corresponds with sitting, reclining, or lying down (8,19). Low-intensity PA was defined as counts between 100 and 759, and time in lifestyle activity (e.g., slow walking, grocery shopping, vacuuming, and child care) was defined as counts between 760 and 2019 (5,18). Moderate-to-vigorous PA (MVPA) is equivalent to an energy expenditure of ≥3 METs and was defined as all activity ≥2020 cpm (28). This level of activity corresponds to walking at speeds of ≥78 m·min−1 as well as more vigorous activities (18). The numbers of minutes per day at different intensities were determined by summing all minutes where the count met the criterion for that intensity, divided by the number of valid days.

Adherence to the current Norwegian PA recommendations was examined by determining the percentage of participants accumulating a minimum of 30 min of daily moderate PA in bouts of 10 min or more (1). All MVPA that occurred in bouts of ≥10 min (with allowance for interruptions of 1–2 min) during the registration period was divided by the number of valid days to examine whether PA recommendations were met. This definition allowed participants to have longer bouts of activity on certain days and to be less active on other days and still meet the recommendations.

Other measures

Data on demography, anthropometry, education, prevalence of disease, and tobacco use were collected from a questionnaire. Body mass index (BMI) was computed as weight (kg) divided by height squared (m2). Overweight and obesity were defined as a BMI of 25–29 and ≥30, respectively (32). Educational attainment was categorized into four groups: less than high school, high school, less than 4 yr of university, and university for 4 yr or more. Participants also reported the type of PA they most commonly participated in.

Dropout analysis

Statistics Norway completed a dropout analysis that compared factors between those who responded positively and those who were invited but did not respond. The factors analyzed were age, sex, country of birth, number of children, civil status, level of education, and level of income. Level of education was the strongest predictor of a positive response. The probability of a positive response increased with increasing age to 50–59 yr and with increasing number of children up to three children but leveled off above these values. Women had a higher probability of a positive response compared with men, as did married individuals compared with unmarried or divorced individuals. People born outside of Western Europe had a lower probability of a positive response compared with those born in Western Europe.


All statistical analyses were performed using PASW Statistics 18 for Windows (IBM Corporation, Somers, NY). Descriptive data are presented as proportion, mean and SD or SEM, and 95% confidence interval (CI) where appropriate. Because of the small differences in overall PA level across the age range of the study population, the data are presented for two main age groups: 20–64 and 65–85 yr. Overall activity level (cpm) varied between test centers and with age, and these variables were considered potential confounders in the association analyses between overall activity level and other factors. Registered monitor wearing time also varied by age and was considered a potential confounder where appropriate. Differences between groups were assessed using ANCOVA with Bonferroni adjustments for multiple comparisons. Differences between the proportions of individuals meeting PA recommendations were assessed using chi-square tests. Linear regression analyses were used to estimate the changes in activity with increasing age. All tests were based on two-sided probability.


The physical characteristics of the study population are presented in Table 1. The final sample comprised 1859 women and 1626 men, whose mean ± SD ages were 48.3 ± 14.9 and 50.0 ± 14.9 yr, respectively. Overall, 37% and 12% of the participants were classified as overweight and obese, respectively. Twenty-two percent reported being either current smokers or current users of smokeless tobacco, and 33% reported having smoked previously. The most commonly reported diseases and conditions were rheumatism (10%), asthma (9%), poor mental health (9%), cardiovascular disease (5%), cancer (5%), type 2 diabetes (3%), and osteoporosis (2%).

Physical characteristics of the study sample (n = 3485) by age and sex.

Participants achieved a mean of 6.8 d of valid activity recordings and a mean daily accelerometer wear time of 14.6 ± 1.1 h. The total PA (cpm) and number of steps taken per day are presented in Table 2; cpm did not differ between men and women in either age group. The participants age 20–64 yr had a higher cpm than did those age 65–85 yr; the mean difference was 70 cpm (95% CI = 58–83). Within the age group of 20–64 yr, cpm did not change with increasing age. By contrast, in the age group of 65–85 yr, the estimated decrease in cpm was 9 per year (95% CI = 7–12). Women in the 20- to 64-yr age group achieved, on average, 256 more steps per day compared with men (95% CI = 30–474). As with cpm, steps taken per day were stable across age in the 20- to 64-yr age group but decreased by an estimated 215 steps per year in the older age group (95% CI = 168–263).

Mean ± SEM accelerometer counts per minute and mean ± SEM steps per day, by age and sex.a

Table 3 presents the means for minutes per day of total accumulated time spent in PA at different intensities and for minutes per day spent in bouts of ≥10 min of MVPA. Men in both age groups spent more time being sedentary and achieved more minutes of MVPA compared with women. Women in both age groups completed more minutes of low-intensity PA compared with men. In the 20- to 64-yr age group, the number of accumulated minutes in the different intensity categories did not increase with age, except for small changes in low-intensity PA and bouts of MVPA for men and small changes in lifestyle activities for women. Men showed an estimated decrease of 0.3 min of low-intensity PA (95% CI = 0.1–0.5) and increase of 0.1 min of MVPA per year (95% CI = 0.04–0.2). Women showed an estimated increase in lifestyle activity of 0.2 min·yr−1 (95% CI = 0.08–0.4). Changes with age were more apparent in the 65- to 85-yr age group. Women and men showed a yearly estimated increase in the amount of sedentary activity of 4.4 min·d−1 (95% CI = 2.8–6.1) and 3.2 min·d−1 (95% CI = 1.5–4.9), respectively. The yearly estimated low-intensity PA and lifestyle activity decreased by 1.6 min·d−1 (95% CI = 0.6–2.7) and 1.5 min·d−1 (95% CI = 0.8–2.3) for women and 0.7 min·d−1 (95% CI = 0.3–1.7) and 1.4 min·d−1 (95% CI = 0.6–2.2) for men. The yearly estimated MVPA decreased by 1.3 min·d−1 (95% CI = 0.8–1.7) in women and by 1.1 min·d−1 (95% CI = 0.5–1.7) in men. Similar but somewhat smaller changes were found for bouts of MVPA; the yearly estimated MVPA decreased by 0.9 min·d−1 (95% CI = 0.5–1.3) in women and by 0.7 min·d−1 (95% CI = 0.2–1.3) in men.

Mean ± SEM minutes per daya of sedentary activity, low PA, lifestyle PA, MVPA, and time spent in bouts of MVPA.

The prevalence of adherence to the PA recommendations is shown for sex and age groups in Table 4. Overall, 20.4% of the study population met the PA recommendations, and this percentage did not differ between women and men. A slightly higher percentage of the participants accumulated ≥10,000 steps per day, compared with the PA recommendations (22.7% vs 20.4%, respectively). Sixty-six percent of participants meeting the PA recommendations also accumulated ≥10,000 steps per day.

Prevalence (95% CI) of the population meeting current PA recommendations.


The adults and older people who participated in this study spent 62% of their time awake being sedentary. Twenty percent of the study population met the current PA recommendations, and 22.7% accumulated ≥10,000 steps per day. Overall PA did not differ between sexes, although women in the younger age group (20–64 yr) accumulated, on average, 3% more steps per day compared with men in that age group. Both overall activity levels and steps per day were steady with age until reaching 65 yr, after which these values decreased. Men accumulated more minutes of sedentary activity, lifestyle activity, and MVPA compared with women, whereas women accumulated more minutes of low-intensity PA and time accumulated in bouts of MVPA.

From studies using self-report, men traditionally report a higher level of PA compared with women (16,30). We observed no sex differences in overall PA in the present study. Similar findings have been reported in a Swedish study of accelerometer-determined PA (4). The authors noted that the assessment methods in older studies were designed primarily to capture leisure time exercise and not overall PA. The inconsistency between studies using self-report and those using objective measures might be attributable to the fact that females may spend more time doing activities that are normally not classified as real exercise, such as walking and household and child care activities. This assumption is supported by the observation that the women in the present study accumulate more minutes of low PA than men and that a higher percentage of women accumulated ≥10,000 steps per day. In light of this, the commonly accepted assumption that men are more physically active than women may no longer be valid, at least not in Scandinavia.

In 2005–2006, PA was assessed by accelerometry in 2299 randomly selected 9- and 15-yr-old Norwegian children (12). Overall PA level (cpm) decreased from 9 to 15 yr to a similar extent in boys and girls (30% and 32%, respectively). Combining these results with our present results suggests that this decline continues into and throughout adulthood. From ages 15 to 20–64 yr, the activity seems to decline by 30% in females and 35% in males. Despite the decline in PA from childhood to adolescence and further into adulthood, the activity level seems to be stable in adulthood until about retirement age. From ages 50–64 to 65–74 yr, activity levels declined by 12% in women and 8% in men (data not shown). Moving from the 65- to 74- to the 75- to 85-yr age group was associated with an additional decline of 36% in women and 30% in men (data not shown). These results show that the age-related decline in PA is most prominent in the transitions from youth to adulthood and from adulthood to retirement age. There is no known biological reason for the decline in PA from youth to adulthood, although the decline in activity observed when entering the 65- to 85-yr age group might be attributed to changes in health status associated with aging.

Recent evidence has shown that time spent pursuing sedentary activities, independent of time spent in MVPA, is related to numerous health outcomes (6). In the present study, most time awake was spent either being sedentary (62%) or in low-intensity PA (25%) and lifestyle activity (9%). Similar distributions were reported in a sample of adult Australians (9). Interestingly, in this Australian study, sedentary activity correlated positively with a clustered metabolic risk score, whereas light PA correlated negatively, indicating that metabolic benefits can be obtained by replacing sedentary activity with light PA (9), a finding that is also supported by others (15).

Eighty percent of Norwegian adults and older people are not meeting the current PA recommendations of 30 min of daily MVPA, sustained in bouts of 8–10 min. In comparison, 80% of children and 50% of adolescents in Norway meet the current PA recommendations for children and adolescents of at least 60 min of daily MVPA (12). When considering adherence to PA recommendations, one must acknowledge that the current recommendations are built upon data from several different studies including randomized controlled trials and large cohort studies (1). However, the PA information is mainly based on self-report, and there is a lack of objectively assessed PA for health outcome in adults (7). Because self-report and accelerometers indeed have different qualities in measuring the level of PA, one should be aware that the cut points for objectively assessed PA and health outcome are not yet known and may be different from the cut points that are now commonly used. Further, standards for accelerometer data reduction have not been established, and the use of different algorithms for determining intensity-specific PA will affect outcomes such as time spent in MVPA (17). However, the reported age-related decline in adherence to the PA recommendations corresponds with the reported age-related decline in overall PA. In our study, women spent an average of 18 min in bouts of MVPA each day, whereas men spent 17 min·d−1. Although the difference is small, this sex difference in bouts of MVPA might indicate that women engage in more sustained PA such as walks or training sessions. This is consistent with similar findings for steps per day, and the results are also consistent with the Swedish study, in which women and men accumulated 17 and 16 min in bouts of MVPA per day, respectively. However, these values are higher than those reported in the 2003–2004 NHANES study (9 and 11 min for women and men, respectively) (5).

The major strength of this study is the use of accelerometers to assess PA and the large sample size. Participants showed good compliance with the protocol, and few data were lost because of insufficient wearing time or defective monitors. We acknowledge some limitations of our study. The main limitation is the low participation rate. The dropout analysis showed that the responses varied according to sociodemographic variables, which is consistent with other population-based studies in Western countries (26). Although the activity levels reported in the present study might be somewhat overestimated because of positive selection, it is not evident that a higher response rate would have eliminated the possibility of selection bias. Several studies have demonstrated only moderate changes in prevalence estimates and sociodemographic distribution when comparing results from different studies with response rates ranging from 30% to 70% (26,31). In addition, it is reasonable to assume that a larger proportion of people than reported never received the invitation to participate or were unable to process the information. Although we did not investigate the reasons why some chose not to participate in the present study, invitees or their relatives occasionally reported that the invitee was dead, institutionalized, or cognitively not able to participate.

Another limitation lies within the nature of a waist-mounted uniaxial accelerometer. Like any waist-mounted activity monitor, an accelerometer located on the trunk is likely to underestimate upper body movement such as weight training and carrying heavy loads (10). Other activities likely to be missed or underestimated are swimming and cycling. However, accelerometers are most sensitive to ambulatory activities such as walking. The participants reported walking as the most frequently performed during the study period, and this diminishes the possibility that PA level was underestimated because the participants performed other activities such as cycling.

The numerous advances in information technologies and the development of labor saving devices have engineered sedentary activity into the modern lifestyle, and many of the settings where PA had occurred naturally in the past have been removed. The accumulating body of evidence on PA at the population level as well as the numerous health risks associated with being sedentary clearly shows that strategies to reduce sedentary activity and increase PA need to be implemented at several levels. Policy makers must initiate strategies to change PA behaviors at the structural level, including transportation and urban planning.


The high level of sedentary activity and low adherence to PA recommendations reported in the present study and several other studies indicate that population levels of PA are low. Adults and older people spend most of their time pursuing sedentary activities, and only 20% of the population meets the current PA guidelines. To assess temporal trends in PA and to evaluate health initiatives taken to increase PA at the population level, it is vital that a recurring surveillance system be established using the same standardized methods and data reduction procedures.

The study was funded by the Norwegian Directorate of Health and the Norwegian School of Sport Sciences.

The authors thank all the test personnel at the 10 institutions involved in the study for their work during the data collection: Finnmark University College, Hedmark University College, Norwegian University of Science and Technology Social Research, Sogn og Fjordane University College, University of Agder, University of Nordland, University of Stavanger, Telemark University College, Vestfold University College, and the Norwegian School of Sport Sciences.

The authors declare that they have no competing interests.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.


1. Becker W, Pedersen A, Lyhne N, et al.. Nordic Nutrition 2004 Recommendations. Integrating Nutrition and Physical Activity. 4th ed. Copenhagen (Denmark): Nordic Council of Ministers; 2004. 436 p.
2. Carlson SA, Densmore D, Fulton JE, Yore MM, Kohl HW 3rd. Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. J Phys Act Health. 2009; 6 (1 suppl): S18–27.
3. Corder K, van Sluijs EM. Invited commentary: comparing physical activity across countries—current strengths and weaknesses. Am J Epidemiol. 2010; 171 (10): 1065–8.
4. Hagströmer M, Oja P, Sjöström M. Physical activity and inactivity in an adult population assessed by accelerometry. Med Sci Sports Exerc. 2007; 39 (9): 1502–8.
5. Hagströmer M, Troiano RP, Sjöström M, Berrigan D. Levels and patterns of objectively assessed physical activity—a comparison between Sweden and the United States. Am J Epidemiol. 2010; 171 (10): 1055–64.
6. Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes. 2007; 56 (11): 2655–67.
7. Haskell WL, Lee IM, Pate RR, et al.. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007; 116 (9): 1081–93.
8. Healy GN, Dunstan DW, Salmon J, et al.. Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose. Diabetes Care. 2007; 30 (6): 1384–9.
9. Healy GN, Wijndaele K, Dunstan DW, et al.. Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes Care. 2008; 31 (2): 369–71.
10. Jørgensen T, Andersen LB, Froberg K, Maeder U, Smith LVH, Aadahl M. Position statement: testing physical condition in a population—how good are the methods? Eur J Sport Sci. 2009; 9 (5): 257–67.
11. Knuth AG, Hallal PC. Temporal trends in physical activity: a systematic review. J Phys Act Health. 2009; 6 (5): 548–59.
12. Kolle E, Steene-Johannessen J, Andersen LB, Anderssen SA. Objectively assessed physical activity and aerobic fitness in a population-based sample of Norwegian 9- and 15-year-olds. Scand J Med Sci Sports. 2010; 20 (1): e41–7.
13. Kozey SL, Staudenmayer JW, Troiano RP, Freedson PS. Comparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion. Med Sci Sports Exerc. 2010; 42 (5): 971–6.
14. Le Masurier GC, Tudor-Locke C. Comparison of pedometer and accelerometer accuracy under controlled conditions. Med Sci Sports Exerc. 2003; 35 (5): 867–71.
15. Levine JA, Schleusner SJ, Jensen MD. Energy expenditure of nonexercise activity. Am J Clin Nutr. 2000; 72 (6): 1451–4.
16. Livingstone MB, Robson PJ, Wallace JM, McKinley MC. How active are we? Levels of routine physical activity in children and adults. Proc Nutr Soc. 2003; 62 (3): 681–701.
17. Mâsse LC, Fuemmeler BF, Anderson CB, et al.. Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc. 2005; 37 (11 suppl): S544–54.
18. Matthews CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005; 37 (11 suppl): S512–22.
19. Matthews CE, Chen KY, Freedson PS, et al.. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol. 2008; 167 (7): 875–81.
20. Norwegian Directorate of Health. The Action Plan on Physical Activity 2005–2009. Working Together for Physical Activity. Oslo (Norway): Norwegian Directorate of Health, 2005, 85 p.
21. Peters TM, Moore SC, Xiang YB, et al.. Accelerometer-measured physical activity in Chinese adults. Am J Prev Med. 2010; 38 (6): 583–91.
22. Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008; 5: 56.
23. Riddoch CJ, Bo AL, Wedderkopp N, et al.. Physical activity levels and patterns of 9- and 15-yr-old European children. Med Sci Sports Exerc. 2004; 36 (1): 86–92.
24. Riddoch CJ, Mattocks C, Deere K, et al.. Objective measurement of levels and patterns of physical activity. Arch Dis Child. 2007; 92 (11): 963–9.
25. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport. 2000; 71 (2 suppl): S1–14.
26. Sogaard AJ, Selmer R, Bjertness E, Thelle D. The Oslo Health Study: The impact of self-selection in a large, population-based survey. Int J Equity Health [Internet]. 2004 [cited 2011 Feb 16]; 3 (1): 3. Available from:
27. Troiano RP. A timely meeting: objective measurement of physical activity. Med Sci Sports Exerc. 2005; 37 (11 suppl): S487–9.
28. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008; 40 (1): 181–8.
29. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005; 37 (11 suppl): S531–43.
30. 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.
31. Van Loon AJ, Tijhuis M, Picavet HS, Surtees PG, Ormel J. Survey non-response in The Netherlands: effects on prevalence estimates and associations. Ann Epidemiol. 2003; 13 (2): 105–10.
32. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. Geneva (Switzerland): World Health Organization; 2000. 252 p.
33. World Health Organization. Global Strategy on Diet, Physical Activity and Health. Geneva (Switzerland): World Health Organization; 2004. 18 p.
34. World Health Organization. Global Health Risks. Mortality and Burden of Disease to Selected Major Risks. Geneva (Switzerland): World Health Organization; 2009. 62 p.


©2012The American College of Sports Medicine