Physical activity levels in adults and elderly from triaxial and uniaxial accelerometry. The Tromsø Study

Autoři: Edvard H. Sagelv aff001;  Ulf Ekelund aff002;  Sigurd Pedersen aff001;  Søren Brage aff004;  Bjørge H. Hansen aff006;  Jonas Johansson aff007;  Sameline Grimsgaard aff007;  Anna Nordström aff001;  Alexander Horsch aff009;  Laila A. Hopstock aff007;  Bente Morseth aff001
Působiště autorů: School of Sport Sciences, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway aff001;  Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, Norway aff002;  Department of Chronic Diseases and Ageing, the Norwegian Institute for Public Health, Oslo, Norway aff003;  MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom aff004;  Department of Sports Science and Clinical Biomechanics, Faculty of Health Sciences, Southern Denmark University, Odense, Denmark aff005;  Department of Sport Science and Physical Education, Faculty of Health Sciences, University of Agder, Agder, Norway aff006;  Department of Community Medicine, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway aff007;  Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden aff008;  Department of Computer Science, Faculty of Natural Sciences, UiT the Arctic University of Norway, Tromsø, Norway aff009
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article



Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences these estimates, in a large population-based cohort of Norwegian adults.


This cross-sectional study included 5918 women and men aged 40–84 years who participated in the seventh wave of the Tromsø Study (2015–16). The participants wore an ActiGraph wGT3X-BT accelerometer attached to the hip for 24 hours per day over seven consecutive days. Accelerometry variables were expressed as volume (counts·minute-1 and steps·day-1) and as minutes per day in sedentary, light physical activity and moderate and vigorous physical activity (MVPA).


From triaxial accelerometry data, 22% (95% confidence interval (CI): 21–23%) of the participants fulfilled the current global recommendations for physical activity (≥150 minutes of MVPA per week in ≥10-minute bouts), while 70% (95% CI: 69–71%) accumulated ≥150 minutes of non-bouted MVPA per week. When analysing uniaxial data, 18% fulfilled the current recommendations (i.e. 20% difference compared with triaxial data), and 55% (95% CI: 53–56%) accumulated ≥150 minutes of non-bouted MVPA per week. We observed approximately 100 less minutes of sedentary time and 90 minutes more of light physical activity from triaxial data compared with uniaxial data (p<0.001).


The prevalence estimates of sufficiently active adults and elderly are more than three times higher (22% vs. 70%) when comparing triaxial bouted and non-bouted MVPA. Physical activity estimates are highly dependent on accelerometry data processing criteria and on different definitions of physical activity recommendations, which may influence prevalence estimates and tracking of physical activity patterns over time.

Klíčová slova:

Accelerometers – Body Mass Index – Data processing – Educational attainment – Exercise – Physical activity – Schools – Walking


1. Organization W.H., Global Status Report on Noncommunicable Diseases 2014, in World Health Organization, Geneva, W.H.O. WHO, Editor. 2014:

2. Lee I.M., et al., Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet, 2012. 380(9838): p. 219–29. doi: 10.1016/S0140-6736(12)61031-9 22818936

3. Lear S.A., et al., The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study. Lancet, 2017. 390(10113): p. 2643–2654. doi: 10.1016/S0140-6736(17)31634-3 28943267

4. Knuth A.G. and Hallal P.C., Temporal trends in physical activity: a systematic review. J Phys Act Health, 2009. 6(5): p. 548–59. doi: 10.1123/jpah.6.5.548 19953831

5. Sallis J.F. and Saelens B.E., Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport, 2000. 71(2 Suppl): p. S1–14. 10925819

6. John D., Tyo B., and Bassett D.R., Comparison of four ActiGraph accelerometers during walking and running. Med Sci Sports Exerc, 2010. 42(2): p. 368–74. doi: 10.1249/MSS.0b013e3181b3af49 19927022

7. Migueles J.H., et al., Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med, 2017. 47(9): p. 1821–1845. doi: 10.1007/s40279-017-0716-0 28303543

8. Keadle S.K., et al., Impact of accelerometer data processing decisions on the sample size, wear time and physical activity level of a large cohort study. BMC Public Health, 2014. 14: p. 1210. doi: 10.1186/1471-2458-14-1210 25421941

9. Chen K.Y. and Bassett D.R. Jr., The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc, 2005. 37(11 Suppl): p. S490–500. doi: 10.1249/01.mss.0000185571.49104.82 16294112

10. Baptista F., et al., Prevalence of the Portuguese population attaining sufficient physical activity. Med Sci Sports Exerc, 2012. 44(3): p. 466–73. doi: 10.1249/MSS.0b013e318230e441 21844823

11. Troiano R.P., et al., Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc, 2008. 40(1): p. 181–8. doi: 10.1249/mss.0b013e31815a51b3 18091006

12. Hagstromer M., et al., Levels and patterns of objectively assessed physical activity—a comparison between Sweden and the United States. Am J Epidemiol, 2010. 171(10): p. 1055–64. doi: 10.1093/aje/kwq069 20406758

13. Peters T.M., et al., Accelerometer-measured physical activity in Chinese adults. Am J Prev Med, 2010. 38(6): p. 583–91. doi: 10.1016/j.amepre.2010.02.012 20494234

14. Hansen B.H., et al., Accelerometer-determined physical activity in adults and older people. Med Sci Sports Exerc, 2012. 44(2): p. 266–72. doi: 10.1249/MSS.0b013e31822cb354 21796052

15. Berkemeyer K., et al., The descriptive epidemiology of accelerometer-measured physical activity in older adults. Int J Behav Nutr Phys Act, 2016. 13: p. 2. doi: 10.1186/s12966-015-0316-z 26739758

16. Ayen T.G., Estimation of energy expenditure with a simulated three-dimensional accelerometer. J Amb Monitor, 1988. 1: p. 293–301.

17. Smith M.P., et al., Accelerometric estimates of physical activity vary unstably with data handling. PLoS One, 2017. 12(11): p. e0187706. doi: 10.1371/journal.pone.0187706 29108029

18. Kelly L.A., et al., Validity of actigraphs uniaxial and triaxial accelerometers for assessment of physical activity in adults in laboratory conditions. BMC Med Phys, 2013. 13(1): p. 5. doi: 10.1186/1756-6649-13-5 24279826

19. Smith M.P., et al., Uni- and triaxial accelerometric signals agree during daily routine, but show differences between sports. Sci Rep, 2018. 8(1): p. 15055. doi: 10.1038/s41598-018-33288-z 30305651

20. WHO, W.H.O., Global Recommendations on Physical Activity for Health, in World Health Organization, Geneva, W.H. Organization, Editor. 2010:

21. Piercy K.L., et al., The physical activity guidelines for americans. JAMA, 2018. 320(19): p. 2020–2028. doi: 10.1001/jama.2018.14854 30418471

22. Jefferis B.J., et al., Adherence to physical activity guidelines in older adults, using objectively measured physical activity in a population-based study. BMC Public Health, 2014. 14: p. 382. doi: 10.1186/1471-2458-14-382 24745369

23. Hagstromer M., Oja P., and Sjostrom M., Physical activity and inactivity in an adult population assessed by accelerometry. Med Sci Sports Exerc, 2007. 39(9): p. 1502–8. doi: 10.1249/mss.0b013e3180a76de5 17805081

24. Jacobsen B.K., et al., Cohort profile: the Tromso Study. Int J Epidemiol, 2012. 41(4): p. 961–7. doi: 10.1093/ije/dyr049 21422063

25. Tryon W.W. and Williams R., Fully proportional actigraphy: A new instrument. Behav Res Methods Instr Comp, 1996. 28(3): p. 392–403.

26. Hecht A., et al., Methodology for using long-term accelerometry monitoring to describe daily activity patterns in COPD. COPD, 2009. 6(2): p. 121–9. doi: 10.1080/15412550902755044 19378225

27. Mâsse L.C., et al., Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. Med Sci Sports Exerc, 2005. 37(11 Suppl): p. S544–54. doi: 10.1249/01.mss.0000185674.09066.8a 16294117

28. Peterson N.E., et al., Validation of accelerometer thresholds and inclinometry for measurement of sedentary behavior in young adult University students. Res Nurs Health, 2015. 38.

29. Sasaki J.E., John D., and Freedson P.S., Validation and comparison of ActiGraph activity monitors. J Sci Med Sport, 2011. 14(5): p. 411–6. doi: 10.1016/j.jsams.2011.04.003 21616714

30. Treuth M.S., et al., Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc, 2004. 36(7): p. 1259–66. 15235335

31. Matthews C.E., et al., Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol, 2008. 167(7): p. 875–881. doi: 10.1093/aje/kwm390 18303006

32. Freedson P.S., Melanson E., and Sirard J., Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc, 1998. 30(5): p. 777–781. doi: 10.1097/00005768-199805000-00021 9588623

33. NESSTAR. NESSTAR WebView tool. 2018 [cited 2018 24.10.2018]; Available from:

34. Committee, T.T.S.D.a.P. The Tromsø Study Web Page. 2019; Available from:

35. Guthold R., et al., Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health, 2018. 6(10): p. e1077–e1086. doi: 10.1016/S2214-109X(18)30357-7 30193830

36. Prince S.A., et al., 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: p. 56. doi: 10.1186/1479-5868-5-56 18990237

37. Matthews C.E., et al., Measurement of Active and Sedentary Behavior in Context of Large Epidemiologic Studies. Med Sci Sports Exerc, 2018. 50(2): p. 266–276. doi: 10.1249/MSS.0000000000001428 28930863

38. Stamatakis E., et al., Short and sporadic bouts in the 2018 US physical activity guidelines: is high-intensity incidental physical activity the new HIIT? British Journal of Sports Medicine, 2019. 53(18): p. 1137–1139. doi: 10.1136/bjsports-2018-100397 30786998

39. Hagstromer M., Oja P., and Sjostrom M., Physical Activity and Inactivity in an Adult Population Assessed by Accelerometry. Med Sci Sports Exer, 2007. 39.

40. Ekelund U., et al., Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ, 2019. 366: p. l4570. doi: 10.1136/bmj.l4570 31434697

41. Stamatakis E., et al., Sitting Time, Physical Activity, and Risk of Mortality in Adults. J Am Coll Cardiol, 2019. 73(16): p. 2062–2072. doi: 10.1016/j.jacc.2019.02.031 31023430

42. Luzak A., et al., Physical activity levels, duration pattern and adherence to WHO recommendations in German adults. PLoS One, 2017. 12(2): p. e0172503. doi: 10.1371/journal.pone.0172503 28245253

43. Hansen B.H., et al., Monitoring population levels of physical activity and sedentary time in Norway across the lifespan. Scand J Med Sci Sports, 2019. 29(1): p. 105–112. doi: 10.1111/sms.13314 30276928

44. Hagströmer M., Oja P., and Sjöström M., Physical activity and inactivity in an adult population assessed by accelerometry. Med Sci Sports Exerc, 2007. 39(9): p. 1502–1508. doi: 10.1249/mss.0b013e3180a76de5 17805081

45. Ortlieb S., et al., Associations between multiple accelerometry-assessed physical activity parameters and selected health outcomes in elderly people—results from the KORA-age study. PLoS One, 2014. 9(11): p. e111206. doi: 10.1371/journal.pone.0111206 25372399

46. Jakicic J.M., et al., Physical Activity and the Prevention of Weight Gain in Adults: A Systematic Review. Med Sci Sports Exerc, 2019. 51(6): p. 1262–1269. doi: 10.1249/MSS.0000000000001938 31095083

47. Jones P.R. and Ekelund U., Physical Activity in the Prevention of Weight Gain: the Impact of Measurement and Interpretation of Associations. Curr Obes Rep, 2019. 8(2): p. 66–76. doi: 10.1007/s13679-019-00337-1 30905041

48. Kesmodel U.S., Cross-sectional studies—what are they good for? Acta Obstet Gynecol Scand, 2018. 97(4): p. 388–393. doi: 10.1111/aogs.13331 29453895

49. Kantomaa M.T., et al., Accelerometer-Measured Physical Activity and Sedentary Time Differ According to Education Level in Young Adults. PLoS One, 2016. 11(7): p. e0158902. doi: 10.1371/journal.pone.0158902 27403958

50. Hansen B.H., et al., Correlates of objectively measured physical activity in adults and older people: a cross-sectional study of population-based sample of adults and older people living in Norway. Int J Public Health, 2014. 59(2): p. 221–30. doi: 10.1007/s00038-013-0472-3 23619723

51. Droomers M., Schrijvers C.T., and Mackenbach J.P., Educational level and decreases in leisure time physical activity: predictors from the longitudinal GLOBE study. J Epidemiol Community Health, 2001. 55(8): p. 562–8. doi: 10.1136/jech.55.8.562 11449013

52. Thorp A.A., et al., Prolonged sedentary time and physical activity in workplace and non-work contexts: a cross-sectional study of office, customer service and call centre employees. Int J Behav Nutr Phys Act, 2012. 9: p. 128. doi: 10.1186/1479-5868-9-128 23101767

53. Holtermann A., et al., The physical activity paradox: six reasons why occupational physical activity (OPA) does not confer the cardiovascular health benefits that leisure time physical activity does. Br J Sports Med, 2018. 52(3): p. 149–150. doi: 10.1136/bjsports-2017-097965 28798040

54. Holtermann A., et al., The health paradox of occupational and leisure-time physical activity. Br J Sports Med, 2012. 46(4): p. 291–295. doi: 10.1136/bjsm.2010.079582 21459873

55. Mansoubi M., et al., Using Sit-to-Stand Workstations in Offices: Is There a Compensation Effect? Med Sci Sports Exerc, 2015.

56. Gomersall S.R., et al., The ActivityStat hypothesis: the concept, the evidence and the methodologies. Sports Med, 2013. 43(2): p. 135–49. doi: 10.1007/s40279-012-0008-7 23329607

57. Howe C.A., Staudenmayer J.W., and Freedson P.S., Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis. Med Sci Sports Exerc, 2009. 41(12): p. 2199–206. doi: 10.1249/MSS.0b013e3181aa3a0e 19915498

58. Wilson T.M. and Tanaka H., Meta-analysis of the age-associated decline in maximal aerobic capacity in men: relation to training status. Am J Physiol Heart Circ Physiol, 2000. 278(3): p. H829–34. doi: 10.1152/ajpheart.2000.278.3.H829 10710351

59. Edvardsen E., et al., Reference values for cardiorespiratory response and fitness on the treadmill in a 20- to 85-year-old population. Chest, 2013. 144(1): p. 241–248. doi: 10.1378/chest.12-1458 23287878

60. Aspenes S.T., et al., Peak oxygen uptake and cardiovascular risk factors in 4631 healthy women and men. Med Sci Sports Exerc, 2011. 43(8): p. 1465–73. doi: 10.1249/MSS.0b013e31820ca81c 21228724

61. Peeters G., et al., Is the pain of activity log-books worth the gain in precision when distinguishing wear and non-wear time for tri-axial accelerometers? J Sci Med Sport, 2013. 16(6): p. 515–9. doi: 10.1016/j.jsams.2012.12.002 23294696

62. Dössegger A., et al., Reactivity to accelerometer measurement of children and adolescents. Med Sci Sports Exerc, 2014. 46(6): p. 1140–6. doi: 10.1249/MSS.0000000000000215 24219978

63. Davis R.E. and Loprinzi P.D., Examination of Accelerometer Reactivity Among a Population Sample of Children, Adolescents, and Adults. J Phys Act Health, 2016. 13(12): p. 1325–1332. doi: 10.1123/jpah.2015-0703 27633616

64. Vanhelst J., et al., Awareness of wearing an accelerometer does not affect physical activity in youth. BMC Med Res Methodol, 2017. 17(1): p. 99. doi: 10.1186/s12874-017-0378-5 28693500

65. McCarthy M. and Grey M., Motion Sensor Use for Physical Activity Data: Methodological Considerations. Nurs Res, 2015. 64(4): p. 320–7. doi: 10.1097/NNR.0000000000000098 26126065

66. Behrens T.K. and Dinger M.K., Motion sensor reactivity in physically active young adults. Res Q Exerc Sport, 2007. 78(2): p. 1–8. doi: 10.1080/02701367.2007.10762229 17479568

67. Grimes D.A. and Schulz K.F., Bias and causal associations in observational research. Lancet, 2002. 359(9302): p. 248–52. doi: 10.1016/S0140-6736(02)07451-2 11812579

68. Hills A.P., Mokhtar N., and Byrne N.M., Assessment of physical activity and energy expenditure: an overview of objective measures. Front Nutr, 2014. 1: p. 5. doi: 10.3389/fnut.2014.00005 25988109

69. Plasqui G. and Westerterp K.R., Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity (Silver Spring), 2007. 15(10): p. 2371–9.

70. Chomistek A.K., et al., Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water. Med Sci Sports Exerc, 2017. 49(9): p. 1935–1944. doi: 10.1249/MSS.0000000000001299 28419028

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