Dyrstad, Sindre M.1; Hansen, BjØrge H.2; Holme, Ingar M.2; Anderssen, Sigmund A.2
Questionnaires are the most commonly used method to assess physical activity (PA) at population level, and a diversity of questionnaires is available for this purpose. A review of PA questionnaires for adults states that although no conclusion could be drawn regarding the best available questionnaire (32), the International Physical Activity Questionnaire (IPAQ) appeared to be the most widely used PA questionnaire. The IPAQ was developed by a multinational working group as a tool suitable for assessing population levels of PA across countries. There are two versions of the IPAQ—the IPAQ–Short Form (IPAQ-S) and the IPAQ–Long Form. Both can be administered by telephone interview or self-administered. The forms were developed to be used among 18- to 65-yr-old adults in diverse settings. In the IPAQ-S, participants report the frequency and duration of vigorous, moderate, and walking activities as well as the time spent sitting during the last 7 d. The IPAQ instrument has gained wide acceptance, and the short version in particular has been used in many international studies (3,4,28).
The first reliability and validity study of the IPAQ-S was conducted across 12 countries in year 2000. It demonstrated reasonable test–retest reliability and intermethod validity (9). The criterion validity against accelerometer had a pooled correlation coefficient of 0.30, but large differences were found between countries. Some studies have reported that the IPAQ-S may overestimate total PA (7,27). Low test–retest reliability for the IPAQ-S telephone interview was also found by the European Physical Activity Surveillance System (26). Fogelholm et al. (11) validated the IPAQ-S against fitness and found that almost 10% of the young men participating in the study had poor fitness and apparently low PA, but they reported very high PA on the IPAQ. They concluded that there was an evident need to develop the IPAQ further to solve the apparent overreporting by a considerable proportion of sedentary individuals.
A total of 23 validation studies were included in a review of the validity of the IPAQ-S (21). The correlation between total PA level measured by the IPAQ-S and objective measurements varied widely and was found to be lower than the acceptable standard. Because few studies had evaluated the concordance of the absolute values between the IPAQ and an accelerometer, the authors recommended further validation studies. Investigations of the validity of the IPAQ in different populations as well as further explorations of sex, age, socioeconomic and regional differences were called for by Craig et al. (9). Despite a large number of evaluation studies of the IPAQ in the interim, Lee et al. (22) repeated this call, and van Poppel et al. (32) stated the importance of researchers assessing the measurement properties of the questionnaire in their own language and in their own target population. To examine some of these questions, researchers require a large number of participants from a representative sample of the population. To date, none of the IPAQ-S reliability and validity studies has involved such a large sample, and the Norwegian version of IPAQ-S has not been validated except for men age 20–39 yr (20). The purpose of the present study was therefore to compare PA and sedentary time from the self-administered short version of the IPAQ with an objective measure of PA using an accelerometer, supplemented with data on sex, age, and education in a large Norwegian sample.
The study design, dropouts, and assessment of PA have been described in detail elsewhere (17). In brief, this multicenter study involved 10 regional test centers throughout Norway. A representative sample of 11,515 adults (age 20–84 yr) from the areas surrounding each test center was drawn from the Norwegian population registry. Written informed consent was obtained from 3867 participants (34%). The participants received a preprogrammed accelerometer and the questionnaire by mail and wore the accelerometer for seven consecutive days. After the registration period, the participants returned the accelerometer and questionnaire by prepaid express mail. Of the 3238 participants with IPAQ data and valid accelerometer recordings, 2462 answered most of the IPAQ questionnaire, whereas a total of 1751 subjects had a complete set of IPAQ data. Participants 65 yr and older were overrepresented among those with missing IPAQ data. The study was approved by the Regional Ethics Committee for Medical Research, the Norwegian Social Science Data Services AS, and the Norwegian Tax Department.
Objective PA measurement
The ActiGraph GT1M (ActiGraph, LLC, Pensacola, FL) was used to assess participants’ PA levels. Participants with at least 4 d of at least 10 h of daily recordings were included in the analysis. Data were collected in 10-s epochs, which were collapsed into 60-s epochs for comparison with other studies. The data were reduced using an SAS-based macro (SAS Institute Inc., Cary, NC). Wear time was defined by subtracting nonwear time from 18 h because all data between 12:00 a.m. and 6:00 a.m. were excluded to avoid potential bias due to participants forgetting to remove the monitor when going to bed at night. 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 (30).
Participants accumulating a minimum average of 30 min of daily moderate-intensity PA in bouts of 10 min or more (with allowance for interruptions of 1–2 min) were categorized as being sufficiently active (5). This definition allowed participants to have longer bouts of activity on certain days and to be less active on other days, but to still be categorized as sufficiently active.
Counts per minute is a measure of total 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 time was defined as all activities less than 100 cpm, a threshold that corresponds with sitting, reclining, or lying down (18,24). Low-intensity PA was defined as between 100 and 759 cpm. Time in moderate-intensity was defined as between 760 and 5998 cpm (16,23) (tasks, 3–6 METs) and 5999 cpm or more for vigorous intensity (≥6 METs) (30). Because 760 cpm provides a useful cut point of moderate-intensity activities in daily life, included walking, the self-reported walking and the moderate-intensity PA from IPAQ were merged when compared with moderate-intensity PA from the accelerometer. Mean minutes per day at different intensities was determined by summing all minutes where the count met the criterion for that intensity, divided by the number of valid days.
Self-reported PA over the previous 7 d was obtained by a Norwegian version of the short, self-administered version of the IPAQ. Additional questions including age, anthropometry, exercise habits, health status, income, and education were included in the questionnaire. Participants also reported the type of PA they most commonly participated in. Age was categorized into four levels: 20–34, 35–49, 50–64, and 65–84 yr. Body mass index (BMI) was computed as body weight divided by height squared (kg·m−2). Overweight and obesity were defined as a BMI of 25–29 and ≥30 kg·m−2, respectively (34). Educational attainment was categorized into four groups: less than high school, completed high school, less than 4 yr of university/college, and university/college education lasting 4 yr or more.
The IPAQ addresses PA performed for at least 10 min and time spent at three intensities: walking, moderate, and vigorous. Examples of activity commonly performed in the different intensities were mentioned in the items. Sitting time, which also included lying down to watch television, was expressed as minutes per day. Data within the different intensities were summed to estimate the total amount of time spent in PA per week or day. Total daily PA in MET-minutes per day was estimated by summing the product of reported time within each intensity by a MET value specific to each category of PA and expressed as a daily average MET score according to the official IPAQ scoring protocol (19). Vigorous-intensity PA was assumed to correspond to 8 METs, moderate-intensity activity to 4 METs, and walking to 3.3 METs. The participants were categorized into three PA levels according to the IPAQ scoring criteria:
Low: meets neither moderate nor high criterion.
Moderate: meets any of the following three criteria: (a) three or more days of vigorous-intensity activity of at least 20 min·d−1, (b) five or more days of moderate-intensity activity and/or walking of at least 30 min·d−1, and (c) five or more days of any combination of walking, moderate-intensity, or vigorous-intensity activities achieving a minimum of at least 600 MET·min·wk−1.
High: meets any one of the following two criteria: (a) vigorous-intensity activity on at least 3 d and accumulating at least 1500 MET·min·wk−1 and (b) seven or more days of any combination of walking, moderate-intensity, or vigorous-intensity activities accumulating at least 3000 MET·min·wk−1.
Those categorized as moderate or high activity were classified as being sufficiently active according to PA guidelines. Data cleaning and processing were carried out in accordance with the guidelines published by the IPAQ Research Committee, and the methods used to score the IPAQ are described in the IPAQ scoring protocol. Of those with valid accelerometer data, 75% answered most of the IPAQ questions, whereas 54% had valid answers to all IPAQ questions.
Variable values are presented as proportion, mean ± SD, or mean ± SE. Independent-sample t-test or one-way ANOVA with the Tukey post hoc tests were used to test associations of anthropometric or other continuous variables with sex and age. A multivariate general linear model, adjusted for age and sex, was used to assess educational differences between the self-reported and the objectively measured PA. Spearman’s correlation coefficient, ρ (rho), was used to assess associations between the two methods. The strength of agreement between the two methods was assessed using the Bland–Altman technique (6). Sedentary time and walking + moderate to vigorous physical activity (MVPA) from the IPAQ were compared with sedentary time and MVPA from the ActiGraph in these analyses. The ability of the IPAQ to identify not sufficiently active individuals was used to measure the questionnaires sensitivity (10). A P value of less than 0.05 was regarded as statistically significant. All statistical analyses were performed using PASW Statistics 18 for Windows (IBM Corporation, Somers, NY).
The physical characteristics and education levels of participants are presented in Table 1. The age span was 20–84 yr. No sex difference in level of education was observed. Overall, 36% of the participants were overweight and 11% were defined as obese.
The descriptive data for the IPAQ and the accelerometer are shown in Table 2. There were no significant differences between men and women in total PA measured by accelerometer counts per minute, but men reported a total activity level (MET·min·d−1) 25% higher than women. The self-reported vigorous-intensity PA were 67% higher in men compared with women, whereas no significant difference in accelerometer-measured vigorous PA was found (Table 2). Accelerometer-measured moderate PA where reduced from 110 to 42 min (95% confidence interval = 41–43), a reduction by 62% (P < 0.0001), when only time in 10-min blocks with an allowance for interruptions of 2 min was included. Using 2020 cpm, and not 760 cpm, as the cut point for moderate-intensity PA would have excluded 77 min of PA from the accelerometer data. When analyzing these data in 10-min blocks, self-reported moderate-intensity (walking not included) PA was 70% higher than corresponding accelerometer data.
Analyses comparing the concordance of absolute values between the IPAQ and accelerometer data showed that both men and women in all age groups reported less sedentary time, less moderate-intensity, and higher level of vigorous-intensity PA compared with the accelerometer data (Table 3; P < 0.0001 for all tests, analyzed in 1-min blocks). The Bland–Altman plot (Figs. 1 and 2) illustrates the agreement between the two methods and shows that the mean difference was −54 and 13 min·d−1 for moderate- and vigorous-intensity PA, respectively. The corresponding data for sedentary time were −131 min·d−1 (SD = 166 min·d−1; figure not shown). Participants age 20–34 yr reported 25% less sedentary time than measured by the accelerometer. The corresponding value for men and women older than 65 yr was 61% (P < 0.0001; Table 3). There were no significant differences in accelerometer-measured sedentary time in any age groups. The difference between self-reported and objectively measured sitting time was 98% larger among men with high school or less compared with men with college/university (Table 3). BMI was not associated with the difference between the self-reported and the accelerometer-measured sedentary time or MVPA.
The association between variables for self-reported PA and objectively measured PA are shown in Table 4. Sex and age affected the correlation coefficients. Self-reported sitting time and accelerometer-determined sedentary time had a correlation coefficient between 0.45 and 0.55 for both sexes and all age groups (P < 0.0001) except those 65 yr and older, which had a correlation coefficient of 0.06 for women and 0.24 for men. For moderate-intensity PA, the correlation coefficient varied between 0.14 (P = 0.052) for women 20–34 yr and 0.40 (P < 0.0001) for men 65 yr and older. For vigorous-intensity PA, the correlation coefficient varied between 0.25 (P < 0.0001) for men 50–64 yr and 0.47 (P < 0.0001) for women age 20–34 yr. Education level did not affect the correlation coefficients significantly.
Twenty-two percent of the participants were found to meet the guidelines of a minimum of 30 min of at least moderate-intensity activity per day (in bouts of 8–10 min) as determined by the accelerometer. Of these, 86% were also captured by the IPAQ. However, the sensitivity of the IPAQ in capturing those who were categorized as insufficiently active individuals by the accelerometer was only 39%.
The main finding of the study was that education level, sex, and age, but not BMI, affected the differences between self-reported (IPAQ-S) and accelerometer-measured (ActiGraph) PA and sedentary time. This finding is in contrast to studies carried out in other countries and with fewer participants (10,22). In the present study, participants with an education level of high school or less reported 21% higher total activity in minutes per day (walking + MVPA) than participants with a college/university degree, whereas no differences in accelerometer-determined total activity (cpm·d−1) was observed between the groups. Furthermore, men reported 47% more moderate to vigorous physical activity (MVPA) than women, although there was no difference between the sexes in accelerometer-determined MVPA. As suggested by Hagstromer et al. (14), it is possible that less-educated individuals, and men in general, engage in manual activities that might not be recorded efficiently using an accelerometer or that there could be a differential bias between educational groups or sex in how they answered the IPAQ. Although participants in the present study reported walking as the most frequently performed activity, we found that men carried out more running than women (data not shown). It is therefore possible that the sex difference in the choice of activity may affect the divergence between the two methods. On the other hand, an overestimation of self-reported PA may also be caused by social desirability response bias (1), which could be larger in men and less educated people. Finally, more educated participants and women in general may also be more thorough in their estimation of their PA level.
The divergence of self-reported and objectively measured PA could also be due to the IPAQ asking about time spent in moderate and vigorous PA, which is subjective and dependent on the participant’s physical fitness. At the same time, the accelerometer measures absolute movement independent of the individual’s fitness. Lower fitness among older people could explain why they report more moderate-intensity PA but are measured with less moderate-intensity PA than participants age 20–34 yr. A higher volume of self-reported vigorous-intensity PA compared with accelerometer-measured found in the present study has also been found in other studies (14,25). Possible factors explaining this divergence are (a) the inability of accelerometers to measure activities involving no vertical acceleration, such as cycling and upper-body movement, and (b) that high-intensity activities are easier to remember due to their association with the feeling of exhaustion and could easily be overestimated if warm-up and cool-down are included. The Bland–Altman plots (Figs. 1 and 2) showed that the difference between the two methods increased with higher activity levels, a finding consistent with other validation studies of different IPAQ versions (14,15,35). This indicates that either people with high activity levels overreport their amount of PA or the accelerometer is less suitable for validating the PA level in highly active people.
The highest correlation between the IPAQ and accelerometer items was found between total sitting and accelerometer counts per minute <100 (ρ = 0.46). Although this correlation is moderate, the mean self-reported sitting time was 2.18 h·d−1 lower compared with accelerometer data, with significant variations between age groups and education level (Table 3). The oldest age group, 65–84 yr, reported 3.5 h·d−1 less sedentary time compared with accelerometer measurements. Although the accelerometer measurement in the oldest age group showed around 5 min·d−1 more with sedentary time compared with the younger participants, the oldest participants reported approximately 90 min·d−1 less sedentary time than the younger age groups. Participants without a college/university degree reported around 18% less sitting time than participants with a college/university degree, but the difference between the groups according to the accelerometer was only 6% (Table 2). The cut point for sedentary time is <100 cpm and includes sitting, standing, and lying down to watch television. Because the accelerometer do not differentiate between sitting and standing, the standing time included in the accelerometer measurement increase the difference between the two methods because the IPAQ only asks about time spent sitting/lying and not standing.
Although the IPAQ is not designed for older people, a study of Japanese men and women aged 65–89 yr found that the validity for the IPAQ was adequate (29). However, the present study shows large differences between self-reported and measured PA in older people. Bauman et al. (2) also called attention to that IPAQ often is inappropriate used with older adults. Reducing the overestimation of PA using questionnaires seems to require highlighting important points for participants in general and older people in particular.
The choice of accelerometer cut points and analyze method have large influence on the comparison of the absolute results. Several studies have set the cut point of moderate-intensity PA at 1952 or 2020 cpm (13,30). However, Matthews (23) claimed that a cut point of 760 cpm provided the most accurate group level estimate of time spent in moderate-intensity activity in daily life, a finding also supported by other studies (31,33). This cut point captured most time spent in the activities that were ≥3.6 METs and included common daily activities of a moderate intensity, in addition to ambulatory activities traditionally captured by the 1952/2020 cpm cut points. Because walking in the IPAQ-scoring protocol is defined as 3.3 METs, merging self-reported walking and moderate-intensity PA in the present study and comparing it to accelerometer-measured moderate-intensity PA (760–5998 cpm) seem fair. We found that the participants reported 49% less moderate PA than measured by the accelerometer (Table 3), with no large differences between sex and education level. A lower self-reported level of moderate-intensity PA could be explained by the fact that the accelerometer measures all minutes spent at this intensity, whereas the participants are asked only to report 10-min blocks of activity. The absolute difference between self-reported and objectively measured moderate PA was reduced from 54 to 12 min·d−1 when only 10-min blocks of these accelerometer data were included.
Overall, the main correlation coefficients in the present study ranged from 0.23 to 0.46 and corresponded to the range found in the review study of the IPAQ-S (21). In three systematic reviews of the content and measurement properties of PA questionnaires, effect sizes higher than 0.5 were considered acceptable for correlations between objective activity-measuring devices and questionnaires (8,12,32). However, when the correlation criterion of 0.5 was used, the conclusions in the reviewed studies were overly optimistic in almost all cases. A reason for this could be that the researchers validating the questionnaires are the same researchers who want to use the questionnaire later on. None of the reviewed IPAQ-S studies reached the minimal acceptable correlation standard recommended in the literature of 0.5 for objective activity measures (21). On the other side, Lee et al. (22) concluded that a correlation of 0.3–0.4 is perhaps as close as can be expected for criterion validity of a physical-activity questionnaire with 10 questions against a mechanical device that detects body movement. In the present study, 67% of the participants were categorized as sufficiently active by the IPAQ, whereas the corresponding number for the accelerometer was 22% (data not shown). This indicates that the IPAQ ability to capture inactive people is limited, which could result in an overreporting of physically active people if PA were measured by the IPAQ only. However, this finding should be interpreted with care because the definitions of sufficiently physically active is different between IPAQ and accelerometer (see Methods section).
Strengths and limitations of the study
The major strength of the study is the large population sample size recruited from a wide age range throughout Norway and the random inclusion of rural and urban populations. Because of the large number of participants, we could obtain a sizable number of participants from every subgroup based on sex, age, education level, and BMI. Limitations of the study include the large number of participants not answering all the IPAQ questions. There was no particular question causing a large dropout number, but many participants had not reported either how many days per week they carried out the specific PA or how much time they usually spent doing the PA. One explanation could be that some participants found the questionnaire too long because we included other questions than the IPAQ. The mean age was higher in the group with incomplete IPAQ answers, so it seems that older people were more inclined to skip or misunderstand some of the items. Those with an incomplete set of IPAQ data had no significant difference in total PA level (>100 cpm), 4.0% less MVPA (>760 cpm; P = 0.011), and 2.7% less sedentary time compared with those with a complete set of IPAQ data (P < 0.0001). These differences in measured PA-level were relatively small. Thus, the exclusion of the group with an incomplete set of IPAQ data does not seem to have a large effect on the results in the present study. Because both the accelerometer and the IPAQ were sent to the participants by mail, another limitation of the study is that the questionnaire data and the accelerometer data do not necessarily represent the same 7 d. However, a comparison between the two methods provides valuable information, as they both provide snapshots of habitual physical activity level.
The present study shows large variations between self-reported and accelerometer-measured PA and sedentary time. Sex, age, and educational level, but not BMI, influenced these variations, and the diversity between the two methods in both sedentary time and vigorous-intensity PA was greatest among men with a lower education level and at the higher end of the age spectrum. The difference between the self-reported and objectively measured PA increased with higher activity and intensity levels. The general agreement between self-reported and accelerometer-measured PA was poor, and their correlation coefficients were lower than what is recommended. The choice of accelerometer cut points and data reduction method have large influence on the comparison of the absolute values. The sum of self-reported walking and moderate PA seems comparable with accelerometer-measured activity using a cut point of 760–5998 cpm and analyzed in blocks of 10 min.
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, NTNU Social Research AS, Sogn og Fjordane University College, University of Agder, University of Nordland, University of Stavanger, Telemark University College, Vestfold University College, and Norwegian School of Sport Sciences. The study was funded by the Norwegian Directorate of Health and the Norwegian School of Sport Sciences.
The authors declare no conflict of interests.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Aadahl M, Jorgensen T. Validation of a new self-report instrument for measuring physical activity. Med Sci Sports Exerc. 2003; 35 (7): 1196–202.
2. Bauman A, Ainsworth BE, Bull F, et al. Progress and pitfalls in the use of the International Physical Activity Questionnaire (IPAQ) for adult physical activity surveillance. J Phys Act Health. 2009; 6: S5–8.
3. Bauman A, Ainsworth BE, Sallis JF, et al. The descriptive epidemiology of sitting. A 20-country comparison using the International Physical Activity Questionnaire (IPAQ). Am J Prev Med. 2011; 41 (2): 228–35.
4. Bauman A, Bull F, Chey T, et al. The International Prevalence Study on Physical Activity: results from 20 countries. Int J Behav Nutr Phys Act. 2009; 6 (1): 21.
5. Becker W, Lyhne N, Pedersen A, et al. Nordic Nutrition Recommendations 2004—Integrating Nutrition and Physical Activity. 4th ed. Copenhagen (Denmark): Nordic Concil of Ministers; 2004. p. 436.
6. Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol. 2003; 22 (1): 85–93.
7. Brown W, Bauman A, Chey T, Trost S, Mummery K. Comparison of surveys used to measure physical activity. Aust N Z J Public Health. 2004; 28 (2): 128–34.
8. Chinapaw MJM, Mokkink LB, van Poppel MNM, van Mechelen W, Terwee CB. Physical activity questionnaires for youth. A systematic review of measurement properties. Sports Med. 2010; 40 (7): 539–63.
9. Craig CL, Marshall AL, Sjostrom M, et al. International Physical Activity Questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003; 35 (8): 1381–95.
10. Ekelund U, Sepp H, Brage S, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006; 9 (2): 258–65.
11. Fogelholm M, Malmberg J, Suni J, et al. International Physical Activity Questionnaire: validity against fitness. Med Sci Sports Exerc. 2006; 38 (4): 753–60.
12. Forsen L, Loland NW, Vuillemin A, et al. Self-administered physical activity questionnaires for the elderly. A systematic review of measurement properties. Sports Med. 2010; 40 (7): 601–23.
13. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998; 30 (5): 777–81.
14. Hagstromer M, Ainsworth BE, Oja P, Sjostrom M. Comparison of a subjective and an objective measure of physical activity in a population sample. J Phys Act Health. 2010; 7 (4): 541–50.
15. Hagstromer M, Oja P, Sjostrom M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006; 9 (6): 755–62.
16. Hagstromer M, Troiano RP, Sjostrom 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.
17. Hansen BH, Kolle E, Dyrstad SM, Holme I, Anderssen SA. Accelerometer-determined physical activity in adults and older people. Med Sci Sports Exerc. 2012; 44 (2): 266–72.
18. 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.
19. IPAQ. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ). Short and long forms. Available from http://www.ipaq.ki.se/scoring.pdf
. 15 pp.
20. Kurtze N, Rangul V, Hustvedt BE. Reliability and validity of the International Physical Activity Questionnaire in the Nord–Trondelag Health Study (HUNT) population of men. BMC Med Res Methodol. 2008; 8: 63.
21. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011; 8: 115.
22. Lee PH, Yu YY, McDowell I, Leung GM, Lam TH, Stewart SM. Performance of the International Physical Activity Questionnaire (Short Form) in subgroups of the Hong Kong Chinese population. Int J Behav Nutr Phys Act. 2011b; 8: 81.
23. Matthews CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005; 37 (11): S512–22.
24. 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.
25. 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.
26. Rutten A, Vuillemin A, Ooijendijk WT, et al. Physical activity monitoring in Europe. The European Physical Activity Surveillance System (EUPASS) approach and indicator testing. Public Health Nutr. 2003; 6 (4): 377–84.
27. Rzewnicki R, Vanden AY, De BI. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutr. 2003; 6 (3): 299–305.
28. Sjöström M, Oja P, Hagströmer M, Smith BJ, Bauman A. Health-enhancing physical activity across European Union countries: the Eurobarometer study. J Public Health. 2006; 14 (5): 291–300.
29. Tomioka K, Iwamoto J, Saeki K, Okamoto N. Reliability and validity of the International Physical Activity Questionnaire (IPAQ) in elderly adults: the Fujiwara-kyo Study. J Epidemiol. 2011; 21 (6): 459–65.
30. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008; 40 (1): 181–8.
31. van der Ploeg HP, Merom D, Chau JY, Bittman M, Trost SG, Bauman AE. Advances in population surveillance for physical activity and sedentary behavior: reliability and validity of time use surveys. Am J Epidemiol. 2010; 172 (10): 1199–206.
32. van Poppel MNM, Chinapaw MJM, Mokkink LB, van Mechelen W, Terwee CB. Physical activity questionnaires for adults: a systematic review of measurement properties. Sports Med. 2010; 40 (7): 565–600.
33. Welk GJ, McClain JJ, Eisenmann JC, Wickel EE. Field validation of the MTI ActiGraph and BodyMedia armband monitor using the IDEEA monitor. Obesity. 2007; 15 (4): 918–28.
34. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO consultation. Geneva: World Health Organization; 2000.
35. Wolin KY, Heil DP, Askew S, Matthews CE, Bennett GG. Validation of the International Physical Activity Questionnaire–Short among blacks. J Phys Act Health. 2008; 5 (5): 746–60.