Skip Navigation LinksHome > July 2011 - Volume 43 - Issue 7 > Tracking of Leisure Time Physical Activity during 28 yr in A...
Medicine & Science in Sports & Exercise:
doi: 10.1249/MSS.0b013e3182084562

Tracking of Leisure Time Physical Activity during 28 yr in Adults: The Tromsø Study


Free Access
Article Outline
Collapse Box

Author Information

1Department of Community Medicine, University of Tromsø, Tromsø, NORWAY, and 2Department of Health and Care Sciences, University of Tromsø, Tromsø, NORWAY; and 3Department of Clinical Therapeutic Services, University Hospital of Northern Norway, Tromsø, NORWAY

Address for correspondence: Bente Morseth, Department of Community Medicine, Faculty of Medicine, University of Tromsø, 9037 Tromsø, Norway; E-mail:

Submitted for publication July 2010.

Accepted for publication November 2010.

Collapse Box


Purpose: Physical activity plays an important role in the prevention of many serious diseases. To develop targeted strategies that encourage physical activity, knowledge of stability of physical activity levels over time is essential. The aim of this study was to examine tracking of leisure time physical activity in adults in Northern Norway during three decades.

Methods: We followed 5432 women and men who attended the Tromsø Study in 1979-1980, as well as repeated examinations after 7 and 28 yr. Baseline age was 20-54 yr (mean age = 35.8 yr). Physical activity was assessed by self-administered questionnaires. Tracking of physical activity, defined as maintenance of relative rank of physical activity level, was estimated by Spearman correlation coefficient and by weighted κ statistics. Tracking in terms of predictability of later values from earlier measurements was analyzed by generalized estimating equations.

Results: A higher-than-expected proportion of subjects maintained their physical activity level from examination 1 to 2 (58%) and 3 (53%). κ statistics showed agreement of 0.41 and 0.29, respectively. Belonging to a specific physical activity level at baseline increased the odds of belonging to the same category at later examinations (sedentary odds ratio (OR) = 3.9 (95% confidence interval (CI) = 3.5-4.4), moderately active OR = 2.2 (95% CI = 2.0-2.4), active OR = 2.9 (95% CI = 2.6-3.3), and highly active OR = 14.0 (95% CI = 8.7-22.5)). Being physically active in young adulthood increased the odds of being physically active later in life (moderately active OR = 3.4 (95% CI = 3.0-3.9), active OR = 5.4 (95% CI = 4.6-6.4), and highly active OR = 13.0 (95% CI = 7.4-22.8)).

Conclusions: This study showed tracking of leisure time physical activity during 28 yr in a cohort of adults.

Numerous studies have documented that habitual physical activity induces health benefits (9,14). Physical activity plays an important role in prevention of many serious conditions, such as cardiovascular diseases (20,29), type 2 diabetes mellitus (2), osteoporosis (19), obesity (8), and some types of cancer (39); moreover, physical activity is inversely related to mortality (30). Nevertheless, in most countries, less than 50% of the population meets the national recommendations (15,34). Development of targeted strategies that encourage physical activity necessitates knowledge of stability, or tracking, of physical activity over time.

Tracking of a characteristic is commonly defined as 1) maintenance of relative rank or position over time or 2) predictability of later values from earlier measurements (11,42,46). To estimate tracking or stability, correlation between repeated measures is the most frequent effect measure (44). A few research groups have examined tracking of physical activity through adulthood. Studies from the United States (3,23,32), Belgium (7,24), Canada (11), and Finland (18,38) report low to moderate tracking of physical activity, with correlation coefficients approximate to 0.30 in most studies. Few studies have examined prediction of physical activity from earlier measurements (18,27,38,40), of which the majority of studies investigated the time span from adolescence to adulthood (27,38,40). Kirjonen et al. (18) found that level of physical activity in adulthood was a strong predictor of physical activity level 5-28 yr later.

Considering that most nations are characterized by high levels of inactivity and an increasing number of elderly people, an important task is to improve the understanding of stability of physical activity during adulthood. Thus, the aim of this population-based study was to examine tracking of physical activity in adult women and men during a period of 28 yr.

Back to Top | Article Outline


Back to Top | Article Outline
Study design and population.

The present study is a longitudinal tracking study of a cohort of men and women who participated in three of the six surveys in the Tromsø Study during the last three decades. The Tromsø Study is a population-based health study in Northern Norway, focusing on lifestyle and health-related topics (45). The study design included six repeated population health surveys, in 1974, 1979-1980, 1986-1987, 1994-1995, 2001, and 2007-2008. Total birth cohorts and additional random samples of adult inhabitants of the municipality of Tromsø, Norway, were invited to participate. All participants were recruited by mail. In the present study, we included subjects from the second survey in 1979-1980 (examination 1 in the present analyses) with repeated measures in the third survey in 1986-1987 (examination 2 in the present analyses) and the sixth survey in 2007-2008 (examination 3 in the present analyses). These surveys included the same question about leisure time physical activity. A total of 21,439 persons, all men in the municipality age 20-54 yr and all women age 20-49 yr, were invited to examination 1 in 1979-1980, and the participation rate was 77.5%. Of the 16,615 participants, 5432 persons also participated in the third and sixth surveys.

The study was approved by the Norwegian Data Inspectorate and recommended by the Regional Committee of Research Ethics. Each participant signed a written informed consent.

Back to Top | Article Outline
Assessment of leisure time physical activity.

At each examination, the participants responded to a self-administered questionnaire concerning several lifestyle and health-related topics (the Norwegian version of the questionnaire is available online at The three surveys forming the basis for the present analyses included the following question about physical activity and exercise: "State your bodily movement and physical exertion in leisure time. If your activity varies much, for example between summer and winter, then give an average. The question refers only to the last 12 months. Tick 'yes' in the most appropriate box." The question included four response options:

1. Reading, watching TV, or other sedentary activity.

2. Walking, cycling, or other forms of exercise at least 4 h·wk−1.

3. Participation in recreational sports, heavy gardening, etc., at least 4 h·wk−1 (including walking or cycling to place of work, Sunday walking, etc.).

4. Participation in hard training or sports competitions regularly several times a week.

In the present study, the subjects were assigned to groups according to physical activity level; level 1 is denoted Sedentary, level 2 Moderately active, level 3 Active, and level 4 Highly active.

The question regarding physical activity used in the Tromsø Study was originally initiated by Saltin and Grimby (33) 40 yr ago and further developed for self-reporting by Wilhelmsen et al. (43a). The question has been widely used in population studies (4,5,16,21,22,31,37). This question measured by test-retest administration after 4-6 wk showed substantial reliability, with a κ value of 0.69 (6) and 86% agreement (35).

Back to Top | Article Outline
Measurement of covariates.

As previously reported (45), the Tromsø Study questionnaires also include questions about educational level (yr), smoking habits (yes/no), and prevalent cardiovascular diseases (yes/no). At the physical examinations, height and weight were measured to the nearest centimeter and half-kilogram, respectively, with subjects wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight (kg) per squared height (m2).

Back to Top | Article Outline
Statistical analyses.

We first calculated the proportion of subjects who maintained their physical activity level from examination 1 to examinations 2 and 3 compared with the expected proportions. The expected proportion of subjects maintaining the same level between two examinations, given no tracking, can be calculated using one of two assumptions: 1) by expecting that the participants are randomly, uniformly distributed at examination 2 (i.e., 25% in each physical activity level) or 2) by assuming the same distribution at the second examination as observed in the first examination (i.e., 19%, 56%, 22%, 3%). The expected proportion of no tracking is 25% for assumption 1 and 39.5% for assumption 2. In this article, the second assumption was chosen as the most likely situation. To compare the observed proportions of agreement with the proportions expected by chance, we used the Cohen weighted κ analyses. Four-level weighted κ statistics was used to measure agreement between examinations 1 and 2 and examinations 1 and 3, with physical activity as an ordinal variable. The κ weights were applied using Fleiss-Cohen weighting (10). Various scales for the evaluation of κ values exist; in this article, the interpretation of the κ coefficient was done according to the guidelines by Munoz and Bangdiwala (28) as follows: <0.00 indicates poor strength of agreement, 0.00-0.20 fair agreement, 0.21-0.45 moderate agreement, 0.46-0.75 substantial agreement, and 0.76-1.0 indicates almost perfect agreement.

Second, the degree of tracking of physical activity was assessed by the Spearman correlation coefficients for physical activity between pairwise examinations, overall and sex specific.

Third, we used generalized estimating equations (GEE) models with a logit link function and an exchangeable working correlation matrix to measure tracking in terms of predictability of later values from earlier measurements, using physical activity in examination 1 as the independent variable and physical activity in examinations 2 and 3 as the dependent variable. Baseline age, BMI, smoking, and education were included in the models as covariates. Tracking was estimated by the odds ratio (OR) of being at a specific physical activity level at later examinations, given belonging to the same level at examination 1, relative to any other baseline physical activity level. Furthermore, we estimated the OR of being nonsedentary (i.e., moderately active, active, or highly active) at later examinations according to the physical activity level at examination 1, with the dependent variable dichotomized into sedentary/nonsedentary. Testing for interactions showed no significant interactions by sex or age (P ≥ 0.08). Analyses using ordinary logistic regression were done between pairwise examinations, and the results were similar to GEE.

Two-sided P values < 0.05 were considered statistically significant. All analyses were performed using SPSS (version 16; Statistical Package for Social Sciences, Chicago, IL). Because weighted κ analysis is not available in SPSS, we used a syntax made available on (36), using data generated from cross-tabulation of physical activity levels.

Back to Top | Article Outline


Back to Top | Article Outline
Baseline characteristics.

In total, 5432 women and men with a baseline age of 35.9 yr (SD = 7.3, range = 20-54 yr) in 1979-1980 participated in all three examinations. Baseline characteristics are shown in Table 1. Age, BMI (except in women), current smoking, and educational level were significantly associated with baseline levels of physical activity (P < 0.05).

Table 1
Table 1
Image Tools
Back to Top | Article Outline
Prevalence of physical activity.

The proportion of sedentary subjects was stable throughout all three examinations, 19.2%, 21.4%, and 18.5% at examinations 1, 2, and 3, respectively (Table 2). The proportion of subjects who were moderately active increased, whereas the proportion who were active or highly active decreased. Similar results were observed after stratifying by sex.

Table 2
Table 2
Image Tools
Back to Top | Article Outline
Maintenance of relative rank over time.

A higher-than-expected proportion of subjects maintained their physical activity level from examination 1 to examinations 2 (58%) and 3 (53%) compared with the expected proportion (39.5%) given no tracking (P < 0.05; Table 3). More women (62% and 60%, respectively) than men (53% and 47%, respectively) remained at their baseline physical activity level during the period. During 28 yr, the total proportion of subjects who decreased their physical activity level (27%) was higher than the proportion who increased their level of physical activity (20%; Table 3). The proportions of subjects who remained at their baseline level at examinations 2 and 3, respectively, varied considerably between the physical activity levels; from 47% and 37% in the sedentary group, to 72% and 71% in the moderate group, 36% and 29% in the active group, and 21% and 8% in the highly active group (results not shown). Only minor differences were observed when the proportions were stratified by sex.

Table 3
Table 3
Image Tools

κ statistics comparing the observed proportions of agreement with the proportions expected by chance showed coefficients of 0.41 between examinations 1 and 2 and 0.29 between examinations 1 and 3 (Table 3). Values for men were almost identical (0.43 and 0.29, respectively), whereas the women had slightly lower values (0.31 and 0.24, respectively).

The Spearman correlation coefficient between pairwise examinations ranged from 0.31 to 0.40 (P < 0.01; Table 4). Table 4 also shows the correlation coefficients according to sex and age groups, which ranged from 0.25 to 0.43 (P < 0.01).

Table 4
Table 4
Image Tools
Back to Top | Article Outline
Predictability of later values from earlier measurements.

Belonging to a specific physical activity level in examination 1 significantly increased the odds of belonging to the same category at later examinations, relative to any other baseline physical activity level (P ≤ 0.001; Table 5). The OR varied between 2 and 5, except in the highly active group, which had considerably higher OR. Furthermore, compared with the sedentary subjects, individuals who were nonsedentary (i.e., moderately active, active, or highly active) at examination 1 had significantly higher odds of being nonsedentary at later examinations (P ≤ 0.001; Table 5). The odds of being nonsedentary increased with physical activity level. The OR for women and men separately varied slightly from the combined results with no specific pattern. Analyses adjusted for baseline age, smoking, and BMI showed slightly lower OR (Table 5).

Table 5
Table 5
Image Tools
Back to Top | Article Outline


This population-based study from Northern Norway demonstrates tracking of leisure time physical activity during 28 yr in a cohort of adult men and women. We found that physical activity level at examination 1 was a strong predictor of physical activity levels 7 and 28 yr later. In terms of maintenance of relative rank, we found that a higher-than-expected proportion of subjects (53%) remained at their baseline level of physical activity after 28 yr. κ statistics showed moderate agreement of physical activity level between examinations.

In the present study, we used various effect measures of tracking, which can be interpreted somewhat differently. Our analyses of maintenance of relative physical activity level indicate that 58% and 53% remained at their baseline physical activity level at examinations 2 and 3, respectively. Few previous studies have examined stability of relative level of physical activity this way. Kirjonen et al. (18) found that more subjects changed from being inactive to active than the opposite, in contrast to our findings showing that a larger proportion became inactive. Furthermore, our findings indicate that 47% and 37% of the sedentary remained sedentary after 7 and 28 yr, respectively. Comparable studies during 1-9 yr found slightly lower proportions of 27%-37% (11,41) or higher proportions of 55%-62% (47) that reported consistent inactivity. However, studies of tracking of physical activity have been performed with different categories and cutoffs and with varying time span and methods; therefore, comparison with previous research is not straightforward.

Correlation between repeated measurements is the most common measure of tracking of physical activity. Previous studies have found low to moderate tracking of physical activity (3,7,11,18,23,24,32,38,41). In our study, the correlation coefficients ranged from 0.31 to 0.43, a finding that is comparable to the reported correlation coefficients of 0.2 to 0.4 found in most previous studies of adults (7,11,18,23,24,32,38). Furthermore, we used weighted κ statistics to compare observed and expected agreement between examinations, resulting in similar coefficients. Both the Spearman correlation and the Cohen weighted κ are highly influenced by the time factor (43), and interpretation of coefficients must take into account not only guidelines for cutoff values presented in the literature (25,28) but also the period and reliability of the measurements (43). In this context, moderate correlation or agreement during 28 yr, as shown in this study, can be interpreted as indication of tracking. P values for correlation and agreement are of less value because they only indicate that the coefficient is significantly different from no agreement, which is an unlikely situation.

Few studies have predicted later physical activity levels from earlier adulthood levels. In our study, being nonsedentary at baseline was a strong predictor of being nonsedentary at later examinations. Similar results were found by Kirjonen et al. (18), who reported that low baseline activity was a strong predictor of activity 5-28 yr later, with the inactive persons at baseline having 2.3 to 5.7 times higher odds of being inactive at follow-up than the highly active at baseline. In accordance with Kirjonen et al. (18), we found that those who were sedentary at baseline had almost four times higher odds of being sedentary at later examinations, relative to all other categories.

In addition, to add to the sparse information about tracking of physical activity throughout adulthood, this study also aimed at extending the use of statistical methods in this research area by using GEE models to assess predictability of later values from earlier measurements. GEE models have advantages over logistic regression models used in previous studies, in that GEE models use all available data and allow for repeated observations and adjustment for both time-dependent and time-independent covariates. Another strength of this study is the relatively large cohort, which gives the opportunity to examine the data with more statistical power.

On the other hand, observational designs are prone to bias. Although complete birth cohorts from 1925 to 1959 were invited to participate in the study and the participation rate was as high as 78%, it seems reasonable to speculate that persons with poor health did not participate; thus, selection bias cannot be excluded. However, the baseline characteristics of participants in the 1979-1980 survey who were not included in the analyses because of death or relocation or because they did not meet the inclusion criteria for further participation (by design) were similar to those included in the analytical cohort, except for a lower prevalence of smokers in the analytical cohort.

Assessment of physical activity was based on self-administered questionnaires, which makes information bias likely. To our knowledge, the criterion-based validity of our physical activity question is unknown. On the basis of validation against METs (1,12) and International Physical Activity Questionnaire (12), the construct validity of our question seems to be satisfying, which is in accordance with the conclusion of Anderssen et al. (4). Nevertheless, it is a rough measure of physical activity and some misclassification must be expected. Moreover, we limited our study to leisure time physical activity, knowing that high physical activity at work may result in less physical activity in leisure time (13,17,26).

High levels of tracking can be interpreted as both advantageous and unfortunate because habitual physical activity is beneficial to health, whereas inactivity is undesirable. Obviously, sedentary individuals should be target of strategies to encourage physical activity. However, our findings that more than 25% decreased their physical activity level over time indicate that effort should also be directed at continuing an active lifestyle. We found that an active lifestyle in young adulthood increased the odds of being active later in life; therefore, young adulthood may be an important life stage for intervention.

In conclusion, this study demonstrated tracking of leisure time physical activity during 28 yr in a cohort of adults, substantiated by physical activity levels in early adulthood being a strong predictor of an active lifestyle later in life and by moderate agreement between repeated measurements.

This work was funded by the Research Council of Norway.

The authors have no conflict of interest.

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

Back to Top | Article Outline


1. Aadahl M, Kjær M, Jørgensen T. Associations between overall physical activity level and cardiovascular risk factors in an adult population. Eur J Epidemiol. 2007;22(6):369-78.

2. Albright A, Franz M, Hornsby G, et al. American College of Sports Medicine. Position Stand: exercise and type 2 diabetes. Med Sci Sports Exerc. 2000;32(7):1345-60.

3. Anderssen N, Jacobs DR Jr, Sidney S, et al. Change and secular trends in physical activity patterns in young adults: a seven-year longitudinal follow-up in the Coronary Artery Risk Development in Young Adults Study (CARDIA). Am J Epidemiol. 1996;143(4):351-62.

4. Anderssen SA, Engeland A, Søgaard AJ, Nystad W, Graff-Iversen S, Holme I. Changes in physical activity behavior and the development of body mass index during the last 30 years in Norway. Scand J Med Sci Sports. 2008;18(3):309-17.

5. Barengo NC, Nissinen A, Pekkarinen H, Halonen P, Tuomilehto J. Twenty-five-year trends in lifestyle and socioeconomic characteristics in Eastern Finland. Scand J Public Health. 2006;34(4):437-44.

6. Batty D. Reliability of a physical activity questionnaire in middle-aged men. Public Health. 2000;114:474-6.

7. De Bourdeaudhuij I, Sallis J, Vandelanotte C. Tracking and explanation of physical activity in young adults over a 7-year period. Res Q Exerc Sport. 2002;73(4):376-85.

8. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. American College of Sports Medicine. Position Stand: appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc. 2009;41(2):459-71.

9. Erlichman J, Kerbey AL, James WP. Physical activity and its impact on health outcomes. Paper 1: the impact of physical activity on cardiovascular disease and all-cause mortality: an historical perspective. Obes Rev. 2002;3(4):257-71.

10. Fleiss J, Cohen J. The equivalence of weighted κ and the intraclass correlation coefficient as measures of reliability. Educ Psychol Meas. 1973;33(3):613.

11. Fortier MD, Katzmarzyk PT, Malina RM, Bouchard C. Seven-year stability of physical activity and musculoskeletal fitness in the Canadian population. Med Sci Sports Exerc. 2001;33(11):1905-11.

12. Graff-Iversen S, Anderssen SA, Holme IM, Jenum AK, Raastad T. Two short questionnaires on leisure-time physical activity compared with serum lipids, anthropometric measurements and aerobic power in a suburban population from Oslo, Norway. Eur J Epidemiol. 2008;23(3):167-74.

13. Haglund BJ. Geographical and socioeconomic distribution of physical activity at work and leisure time and its relation to morbidity in a Swedish rural county. Scand J Soc Med. 1984;12(4):155-64.

14. 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.

15. Haase A, Steptoe A, Sallis JF, Wardle J. Leisure-time physical activity in university students from 23 countries: associations with health beliefs, risk awareness, and national economic development. Prev Med. 2004;39(1):182-90.

16. Holme I, Helgeland A, Hjermann I, Leren P, Lund-Larsen PG. Physical activity at work and at leisure in relation to coronary risk factors and social class: a 4-year mortality follow-up: the Oslo Study. Acta Med Scand. 1981;209(4):277-83.

17. Kaleta D, Jegier A. Occupational energy expenditure and leisure-time physical activity. Int J Occup Med Environ Health. 2005;18(4):351-6.

18. Kirjonen J, Telama R, Luukkonen R, Kaaria S, Kaila-Kangas L, Leino-Arjas P. Stability and prediction of physical activity in 5-, 10-, and 28-year follow-up studies among industrial employees. Scand J Med Sci Sports. 2006;16(3):201-8.

19. Kohrt WM, Bloomfield SA, Little KD, Nelson ME, Yingling VR, American College of Sports Medicine. Position Stand: physical activity and bone health. Med Sci Sports Exerc. 2004;36(11):1985-96.

20. Kokkinos P. Physical activity and cardiovascular disease prevention: current recommendations. Angiology. 2008;59(suppl 2):26S-9S.

21. Kurtze N, Gundersen KT, Holmen J. Self-reported physical activity in population studies-a methodological problem. Nor J Epidemiol. 2003;13(1):163-70.

22. Deleted in proof.

23. Lee IM, Paffenbarger RS Jr, Hsieh CC. Time trends in physical activity among college alumni, 1962-1988. Am J Epidemiol. 1992;135(8):915-25.

24. Lefevre J, Philippaerts RM, Delvaux K, et al. Daily physical activity and physical fitness from adolescence to adulthood: a longitudinal study. Am J Hum Biol. 2000;12(4):487-97.

25. Malina RM. Tracking of physical activity and physical fitness across the lifespan. Res Q Exerc Sport. 1996;67(suppl 3):S48-57.

26. Marquez DX, Neighbors CJ, Bustamante EE. Leisure time and occupational physical activity among racial/ethnic minorities. Med Sci Sports Exerc. 2010;42(6):1086-93.

27. Matton L, Thomis M, Wijndaele K, et al. Tracking of physical fitness and physical activity from youth to adulthood in females. Med Sci Sports Exerc. 2006;38(6):1114-20.

28. Munoz S, Bangdiwala S. Interpretation of κ and B statistics measures of agreement. J Appl Stat. 1997;24(1):105-12.

29. Paffenbarger RS Jr, Hyde RT. Exercise in the prevention of coronary heart disease. Prev Med. 1984;13(1):3-22.

30. Paffenbarger RS Jr, Hyde RT, Wing AL, Hsieh CC. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med. 1986;314(10):605-13.

31. 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.

32. Sallis JF, Greenlee L, McKenzie TL, et al. Changes and tracking of physical activity across seven years in Mexican-American and European-American mothers. Women Health. 2001;34(4):1-14.

33. Saltin B, Grimby G. Physiological analysis of middle-aged and old former athletes. Comparison with still active athletes of the same ages. Circulation. 1968;38(6):1104-15.

34. Sisson SB, Katzmarzyk PT. International prevalence of physical activity in youth and adults. Obes Rev. 2008;9(6):606-14.

35. Sjøl A, Thomsen KK, Schroll M, Andersen LB. Secular trends in acute myocardial infarction in relation to physical activity in the general Danish population. Scand J Med Sci Sports. 2003;13(4):224-30.

36. SPSS Technical Support Web site [Internet]. Chicago (IL): SPSS Inc.; [cited 2010 Nov 1]. Available from:

37. 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):644-50.

38. Telama R, Yang X, Viikari J, Valimaki I, Wanne O, Raitakari O. Physical activity from childhood to adulthood: a 21-year tracking study. Am J Prev Med. 2005;28(3):267-73.

39. Thune I, Furberg AS. Physical activity and cancer risk: dose-response and cancer, all sites and site-specific. Med Sci Sports Exerc. 2001;33(suppl 6):S530-50; discussion S609-10.

40. Trudeau F, Laurencelle L, Shephard RJ. Tracking of physical activity from childhood to adulthood. Med Sci Sports Exerc. 2004;36(11):1937-43.

41. Tudor-Locke C, Giles-Corti B, Knuiman M, McCormack G. Tracking of pedometer-determined physical activity in adults who relocate: results from RESIDE. Int J Behav Nutr Phys Act. 2008;5:39.

42. Twisk JW, Staal BJ, Brinkman MN, Kemper HC, van Mechelen W. Tracking of lung function parameters and the longitudinal relationship with lifestyle. Eur Respir J. 1998;12(3):627-34.

43. Twisk JWR. Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide. Cambridge (UK): Cambridge University Press; 2003. p. xvi, 301.

43a. Wilhelmsen L, Tibblin G, Aurell M, Bjure J, Ekström-Jodal B, Grimby G. Physical activity, physical fitness and risk of myocardial infarction. Adv Cardiol. 1976;18(0):217-30.

44. Wilsgaard T, Brenn T, Arnesen E. Tracking of blood pressure in adult men: the Tromsø Study 1974-1986. J Epidemiol Biostat. 1998;3(3):269-76.

45. Wilsgaard T, Jacobsen BK, Mathiesen EB, Njølstad I. Weight loss and mortality: a gender-specific analysis of the Tromsø Study. Gend Med. 2009;6(4):575-86.

46. Wilsgaard T, Jacobsen BK, Schirmer H, et al. Tracking of cardiovascular risk factors: the Tromsø Study, 1979-1995. Am J Epidemiol. 2001;154(5):418-26.

47. Yang X, Telama R, Hirvensalo M, Mattsson N, Viikari JS, Raitakari OT. The longitudinal effects of physical activity history on metabolic syndrome. Med Sci Sports Exerc. 2008;40(8):1424-31.

Cited By:

This article has been cited 2 time(s).

Paediatria Croatica
Tracking of Physical Activity and Cardiorespiratory Fitness From Childhood to Adulthood
Soric, M
Paediatria Croatica, 56(4): 349-353.

Journal of Physical Activity & Health
Associations Between Compulsory Physical Activity During Military Service and Activity in Later Adulthood Among Male Veterans Compared With Nonveterans
Littman, AJ; Forsberg, CW; Boyko, EJ
Journal of Physical Activity & Health, 10(6): 784-791.

Back to Top | Article Outline


©2011The American College of Sports Medicine


Article Tools



Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.

Connect With Us