Skip to main content
Springer Nature Link
Log in

Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations

  • Systematic Review
  • Published:
Sports Medicine Aims and scope Submit manuscript

Abstract

Background

Accelerometers are widely used to measure sedentary time, physical activity, physical activity energy expenditure (PAEE), and sleep-related behaviors, with the ActiGraph being the most frequently used brand by researchers. However, data collection and processing criteria have evolved in a myriad of ways out of the need to answer unique research questions; as a result there is no consensus.

Objectives

The purpose of this review was to: (1) compile and classify existing studies assessing sedentary time, physical activity, energy expenditure, or sleep using the ActiGraph GT3X/+ through data collection and processing criteria to improve data comparability and (2) review data collection and processing criteria when using GT3X/+ and provide age-specific practical considerations based on the validation/calibration studies identified.

Methods

Two independent researchers conducted the search in PubMed and Web of Science. We included all original studies in which the GT3X/+ was used in laboratory, controlled, or free-living conditions published from 1 January 2010 to the 31 December 2015.

Results

The present systematic review provides key information about the following data collection and processing criteria: placement, sampling frequency, filter, epoch length, non-wear-time, what constitutes a valid day and a valid week, cut-points for sedentary time and physical activity intensity classification, and algorithms to estimate PAEE and sleep-related behaviors. The information is organized by age group, since criteria are usually age-specific.

Conclusion

This review will help researchers and practitioners to make better decisions before (i.e., device placement and sampling frequency) and after (i.e., data processing criteria) data collection using the GT3X/+ accelerometer, in order to obtain more valid and comparable data.

PROSPERO registration number

CRD42016039991.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Warburton DER, Nicol CW, Bredin SSD. Health benefits of physical activity: the evidence. CMAJ. 2006;174:801–9.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Fletcher G, Balady G, Blair S, et al. Statement on exercise: benefits and recommendations for physical activity programs for all Americans. Circulation. 1996;94:857–62.

    Article  CAS  PubMed  Google Scholar 

  3. Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7:40.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Warren JM, Ekelund U, Besson H, et al. Assessment of physical activity—a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. Eur J Cardiovasc Prev Rehabil. 2010;17:127–39.

    Article  PubMed  Google Scholar 

  5. Bassett DR, Rowlands A, Trost SG. Calibration and validation of wearable monitors. Med Sci Sports Exerc. 2012;44:32–8.

    Article  Google Scholar 

  6. Rothney MP, Brychta RJ, Meade NN, et al. Validation of the ActiGraph two-regression model for predicting energy expenditure. Med Sci Sports Exerc. 2010;42:1785–92.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14:411–6.

    Article  PubMed  Google Scholar 

  8. Baranowski T, Dworkin RJ, Cieslik CJ, et al. Reliability and validity of self-report of aerobic activity—Family Health Project. Res Q Exerc Sport. 1984;55:309–17.

    Article  Google Scholar 

  9. Sallis JF. Self-report measures of children’s physical activity. J Sch Health. 1991;61:215–9.

    Article  CAS  PubMed  Google Scholar 

  10. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001;31:439–54.

    Article  CAS  PubMed  Google Scholar 

  11. Freedson PS, Melanson E, Sirard JR. Calibration of the computer science and applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30:777–81.

    Article  CAS  PubMed  Google Scholar 

  12. Evenson KR, Catellier DJ, Gill K, et al. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26:1557–65.

    Article  PubMed  Google Scholar 

  13. Hänggi JM, Phillips LRS, Rowlands AV. Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. J Sci Med Sport. 2013;16:40–4. doi:10.1016/j.jsams.2012.05.012.

    Article  PubMed  Google Scholar 

  14. Copeland JL, Esliger DW. Accelerometer assessment of physical activity in active, healthy older adults. J Aging Phys Act. 2009;17:17–30.

    Article  PubMed  Google Scholar 

  15. Chandler JL, Brazendale K, Beets MW, et al. Classification of physical activity intensities using a wrist-worn accelerometer in 8- to 12-year-old children. Pediatr Obes. 2015;11(2):120–7. doi:10.1111/ijpo.12033.

    Article  PubMed  Google Scholar 

  16. Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: an empirical test of methodological issues. Sleep. 1994;17(3):201–6.

    Article  CAS  PubMed  Google Scholar 

  17. Cole RJ, Kripke DF, Gruen W, et al. Automatic sleep/wake identification from wrist activity. Sleep. 1992;15:461–9.

    Article  CAS  PubMed  Google Scholar 

  18. Tudor-Locke C, Barreira TV, Schuna JM, et al. Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab. 2014;39:53–7.

    Article  PubMed  Google Scholar 

  19. Barreira TV, Schuna JM, Mire EF, et al. Identifying children’s nocturnal sleep using 24-h waist accelerometry. Med Sci Sports Exerc. 2015. doi:10.1249/MSS.0000000000000486.

    Google Scholar 

  20. Crouter SE, Horton M, Bassett DR. Use of a two-regression model for estimating energy expenditure in children. Med Sci Sports Exerc. 2012;44:1177–85.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Trost SG, Ward DS, Moorehead SM, et al. Validity of the computer science and applications (CSA) activity monitor in children. Med Sci Sports Exerc. 1998;30:629–33.

    Article  CAS  PubMed  Google Scholar 

  22. Hildebrand M, Van Hees VT, Hansen BH, et al. Age-group comparability of raw accelerometer output from wrist- and hip-worn monitors. Med Sci Sports Exerc. 2014;46(9):1816–24. doi:10.1249/MSS.0000000000000289.

    Article  PubMed  Google Scholar 

  23. Fairclough SJ, Noonan R, Rowlands AV, et al. Wear compliance and activity in children wearing wrist and hip-mounted accelerometers. Med Sci Sports Exerc. 2016;48(2):245–53. doi:10.1249/MSS.0000000000000771.

    Article  PubMed  Google Scholar 

  24. Staudenmayer J, He S, Hickey A, et al. Methods to estimate aspects of physical activity and sedentary behavior from high frequency wrist accelerometer measurements. J Appl Physiol. 2015. doi:10.1152/japplphysiol.00026.2015.

    PubMed  PubMed Central  Google Scholar 

  25. Wijndaele K, Westgate K, Stephens SK, et al. Utilization and harmonization of adult accelerometry data. Med Sci Sports Exerc. 2015;47(10):2129–39.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Aguilar-Farias N, Brown WJ, Peeters GM. ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport. 2014;17:293–6. doi:10.1016/j.jsams.2013.07.002.

    Article  PubMed  Google Scholar 

  27. Santos-Lozano A, Santín-Medeiros F, Cardon G, et al. Actigraph GT3X: validation and determination of physical activity intensity cut points. Int J Sports Med. 2013;34:975–82.

    Article  CAS  PubMed  Google Scholar 

  28. Ellis K, Kerr J, Godbole S, et al. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiol Meas. 2014;35:2191–203.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Kim Y, Lee JM, Peters BP, et al. Examination of different accelerometer cut-points for assessing sedentary behaviors in children. PLoS One. 2014;9:1–8.

    Google Scholar 

  30. Keadle SK, Shiroma EJ, Freedson PS, 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(1):1210.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Cain KL, Sallis JF, Conway TL, et al. Using accelerometers in youth physical activity studies: a review of methods. J Phys Act Health. 2013;10:437–50.

    Article  PubMed  Google Scholar 

  32. Booth A, Clarke M, Dooley G, et al. The nuts and bolts of PROSPERO: an international prospective register of systematic reviews. Syst Rev. 2012;1:2.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (Reprinted from Annals of Internal Medicine). Phys Ther. 2009;89:873–80.

    PubMed  Google Scholar 

  34. Johansson E, Ekelund U, Nero H, et al. Calibration and cross-validation of a wrist-worn ActiGraph in young preschoolers. Pediatr Obes. 2015;10(1):1–6. doi:10.1111/j.2047-6310.2013.00213.x.

    Article  CAS  PubMed  Google Scholar 

  35. Costa S, Barber SE, Cameron N, et al. Calibration and validation of the ActiGraph GT3X+ in 2–3 year olds. J Sci Med Sport. 2013;17:617–22. doi:10.1016/j.jsams.2013.11.005.

    Article  PubMed  Google Scholar 

  36. Tudor-Locke C, Barreira TV, Schuna JM, et al. Improving wear-time compliance with a 24-h waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act. 2015;12(1):1–9.

    Article  Google Scholar 

  37. Crouter SE, Flynn JI, Bassett DR. Estimating physical activity in youth using a wrist accelerometer. Med Sci Sports Exerc. 2015;47:944–7.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Romanzini M, Petroski EL, Ohara D, et al. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2014;14(1):91–9. doi:10.1080/17461391.2012.732614.

    Article  PubMed  Google Scholar 

  39. Hjorth MF, Chaput JP, Damsgaard CT, et al. Measure of sleep and physical activity by a single accelerometer: can a waist-worn ActiGraph adequately measure sleep in children? Sleep Biol Rhythms. 2012;10:328–35.

    Article  Google Scholar 

  40. Aadland E, Ylvisåker E. Reliability of the ActiGraph GT3X+ accelerometer in adults under free-living conditions. PLoS One. 2015;10:e0134606. doi:10.1371/journal.pone.0134606.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Ozemek C, Kirschner MM, Wilkerson BS, et al. Intermonitor reliability of the GT3X + accelerometer at hip, wrist and ankle sites during activities of daily living. Physiol Meas. 2014;35:129–38.

    Article  PubMed  Google Scholar 

  42. Ellis K, Kerr J, Godbole S, et al. Hip and wrist accelerometer algorithms for free-living behavior classification. Med Sci Sports Exerc. 2016;48(5):933–40. doi:10.1249/MSS.0000000000000840.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Stec MJ, Rawson ES. Estimation of resistance exercise energy expenditure using triaxial accelerometry. J Strength Cond Res. 2012;26:1413–22.

    Article  PubMed  Google Scholar 

  44. Tudor-Locke C, Barreira TV, Schuna JM. Comparison of step outputs for waist and wrist accelerometer attachment sites. Med Sci Sports Exerc. 2014;47(4):839.

    Article  Google Scholar 

  45. Choi L, Ward SC, Schnelle JF, et al. Assessment of wear/nonwear-time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44:2009–16.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Brønd C, Arvidsson D. Sampling frequency affects the processing of ActiGraph raw acceleration data to activity counts. J Appl Physiol. 2016;120(3):362–9. doi:10.1152/japplphysiol.00628.2015.

    Article  PubMed  Google Scholar 

  47. Toftager M, Kristensen PL, Oliver M, et al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10:140.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Freedson PS, John D. Comment on “Estimating activity and sedentary behavior from an accelerometer on the hip and wrist”. Med Sci Sports Exerc. 2013;45:962–3.

    Article  PubMed  Google Scholar 

  49. Aadland E, Ylvisåker E. Reliability of objectively measured sedentary time and physical activity in adults. PLoS One. 2015;10:e0133296. doi:10.1371/journal.pone.0133296.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Donaldson SC, Montoye AHK, Tuttle MS, et al. Variability of objectively measured sedentary behavior. Med Sci Sports Exerc. 2016;48(4):755–61. doi:10.1249/MSS.0000000000000828.

    Article  PubMed  Google Scholar 

  51. Lyden K, Kozey Keadle SL, Staudenmayer JW, et al. Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc. 2012;44:2243–52.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ried-Larsen M, Brønd JC, Brage S, et al. Mechanical and free living comparisons of four generations of the ActiGraph activity monitor. Int J Behav Nutr Phys Act. 2012;9:1–10. doi:10.1186/1479-5868-9-113.

    Article  Google Scholar 

  53. Cain KL, Conway TL, Adams MA, et al. Comparison of older and newer generations of ActiGraph accelerometers with the normal filter and the low frequency extension. Int J Behav Nutr Phys Act. 2013;10:51.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Cellini N, Buman MP, McDevitt EA, et al. Direct comparison of two actigraphy devices with polysomnographically recorded naps in healthy young adults. Chronobiol Int. 2013;30:691–8.

    Article  PubMed  Google Scholar 

  55. Wanner M, Martin BW, Meier F, et al. Effects of filter choice in GT3X accelerometer assessments of free-living activity. Med Sci Sports Exerc. 2013;45:170–7.

    Article  PubMed  Google Scholar 

  56. Barreira TV, Brouillette RM, Foil HC, et al. Comparison of older adults steps/day using NL-1000 pedometer and two GTX+ accelerometer filters. J Aging Phys Act. 2012;21:402–16.

    Article  PubMed  Google Scholar 

  57. Jimmy G, Seiler R, Mäder U. Development and validation of GT3X accelerometer cut-off points in 5- to 9-year-old children based on indirect calorimetry measurements. Schweizerische Zeitschrift fur Sport und Sport. 2013;61:37–43.

    Google Scholar 

  58. Kahan D, Nicaise V, Reuben K. Convergent validity of four accelerometer cutpoints with direct observation of preschool children’s outdoor physical activity. Res Q Exerc Sport. 2013;84:59–67. doi:10.1080/02701367.2013.762294.

    Article  PubMed  Google Scholar 

  59. Aibar A, Bois JE, Zaragoza J, et al. Do epoch lengths affect adolescents’ compliance with physical activity guidelines? J Sports Med Phys Fit. 2014;54:255–63.

    Google Scholar 

  60. Peeters G, van Gellecum Y, Ryde 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;16:515–9. doi:10.1016/j.jsams.2012.12.002.

    Article  PubMed  Google Scholar 

  61. Troiano RP, Berrigan D, Dodd KW, et al. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–8.

    Article  PubMed  Google Scholar 

  62. Choi L, Liu Z, Matthews E, et al. Validation of accelerometer wear and nonwear-time classification algorithm. Med Sci Sports Exerc. 2012;43:357–64.

    Article  Google Scholar 

  63. Katzmarzyk PT, Barreira TV, Broyles ST, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health. 2013;13:900–13.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Janssen X, Cliff DP, Reilly JJ, et al. Predictive validity and classification accuracy of ActiGraph energy expenditure equations and cut-points in young children. PLoS One. 2013;8(11):e79124–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sirard JR, Trost SG, Pfeiffer KA, et al. Calibration and evaluation of an objective measure of physical activity in preschool children. J Phys Act Health. 2005;2:345–57.

    Article  Google Scholar 

  66. Reilly JJ, Coyle J, Kelly L, et al. An objective method for measurement of sedentary behavior in 3- to 4-year olds. Obes Res. 2003;11:1155–8.

    Article  PubMed  Google Scholar 

  67. Pate RR, Almeida MJ, McIver KL, et al. Validation and calibration of an accelerometer in preschool children. Obesity (Silver Spring). 2006;14:2000–6.

    Article  PubMed  Google Scholar 

  68. Puyau MR, Adolph AL, Vohra FA, et al. Validation and calibration of physical activity monitors in children. Obes Res. 2002;10:150–7.

    Article  PubMed  Google Scholar 

  69. Van Cauwenberghe E, Gubbels J, De Bourdeaudhuij I, et al. Feasibility and validity of accelerometer measurements to assess physical activity in toddlers. Int J Behav Nutr Phys Act. 2011;8:67.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Pulakka A, Cheung YB, Ashorn U, et al. Feasibility and validity of the ActiGraph GT3X accelerometer in measuring physical activity of Malawian toddlers. Acta Paediatr Int J Paediatr. 2013;102:1192–8.

    Article  CAS  Google Scholar 

  71. Butte NF, Wong WW, Lee JS, et al. Prediction of energy expenditure and physical activity in preschoolers. Med Sci Sports Exerc. 2014;46:1216–26.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Zhu Z, Chen P, Zhuang J. Intensity classification accuracy of accelerometer-measured physical activities in Chinese children and youth. Res Q Exerc Sport. 2013;84:S4–11. doi:10.1080/02701367.2013.850919.

    Article  PubMed  Google Scholar 

  73. Vanhelst J, Béghin L, Turck D, et al. New validated thresholds for various intensities of physical activity in adolescents using the ActiGraph accelerometer. Int J Rehabil Res. 2011;34:175–7.

    Article  PubMed  Google Scholar 

  74. Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc. 2005;37:523–30.

    Article  Google Scholar 

  75. Mattocks C, Leary S, Ness A, et al. Calibration of an accelerometer during free-living activities in children. Int J Pediatr Obes. 2007;2:218–26.

    Article  PubMed  Google Scholar 

  76. Peterson NE, Sirard JR, Kulbok PA, et al. Validation of accelerometer thresholds and inclinometry for measurement of sedentary behavior in young adult university students. Res Nurs Health. 2015;38:492–8. doi:10.1002/nur.21694.

    Article  PubMed  Google Scholar 

  77. Rowlands AV, Rennie K, Kozarski R, et al. Children’s physical activity assessed with wrist- and hip-worn accelerometers. Med Sci Sports Exerc. 2014;2006:2308–16.

    Article  Google Scholar 

  78. Aittasalo M, Vähä-Ypyä H, Vasankari T, et al. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7:18. doi:10.1186/s13102-015-0010-0.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Kozey-Keadle S, Libertine A, Lyden K, et al. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43:1561–7.

    Article  PubMed  Google Scholar 

  80. Vähä-Ypyä H, Vasankari T, Husu P, et al. A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging. 2015;35(1):64–70. doi:10.1111/cpf.12127.

    Article  PubMed  Google Scholar 

  81. Zakeri IF, Adolph AL, Puyau MR, et al. Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers. J Nutr. 2013;143:114–22.

    Article  CAS  PubMed  Google Scholar 

  82. Zhu Z, Chen P, Zhuang J. Predicting Chinese children and youth’s energy expenditure using ActiGraph accelerometers: a calibration and cross-validation study. Res Q Exerc Sport. 2013;84:S56–63. doi:10.1080/02701367.2013.850989.

    Article  PubMed  Google Scholar 

  83. Meredith-Jones K, Williams S, Galland B, et al. 24 h accelerometry: impact of sleep-screening methods on estimates of sedentary behaviour and physical activity while awake. J Sports Sci. 2015;414:1–7. doi:10.1080/02640414.2015.1068438.

    Google Scholar 

  84. Rosenberger ME, Buman MP, Haskell WL, et al. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc. 2016;48(3):457–65. doi:10.1249/MSS.0000000000000778.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Kaplan RF, Wang Y, Loparo KA, et al. Performance evaluation of an automated single-channel sleep-wake detection algorithm. Nat Sci Sleep. 2014;6:113–22. doi:10.2147/NSS.S71159.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Slater JA, Botsis T, Walsh J, et al. Assessing sleep using hip and wrist actigraphy. Sleep Biol Rhythms. 2015;13(2):172–8. doi:10.1111/sbr.12103.

    Article  Google Scholar 

  87. Zinkhan M, Berger K, Hense S, et al. Agreement of different methods for assessing sleep characteristics: a comparison of two actigraphs, wrist and hip placement, and self-report with polysomnography. Sleep Med. 2014;15(9):1107–14. doi:10.1016/j.sleep.2014.04.015.

    Article  PubMed  Google Scholar 

  88. Trost SG, Mciver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37:531–43.

    Article  Google Scholar 

  89. Tryon WW, Williams R. Fully proportional actigraphy: a new instrument. Behav Res Methods Instru Comput. 1996;28:392–403.

    Article  Google Scholar 

  90. Cavagna GA, Franzetti P. The determinants of the step frequency in walking in humans. J Physiol. 1986;373:235–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Cavagna GA, Willems PA, Franzetti P, et al. The two power limits conditioning step frequency in human running. J Physiol. 1991;437:95–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. John D, Miller R, Kozey-Keadle S, et al. Biomechanical examination of the “plateau phenomenon” in ActiGraph vertical activity counts. Physiol Meas. 2012;33:219–30.

    Article  PubMed  PubMed Central  Google Scholar 

  93. Robusto KM, Trost SG. Comparison of three generations of ActiGraph™ activity monitors in children and adolescents. J Sports Sci. 2012;30:1429–35.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Grydeland M, Hansen BH, Ried-Larsen M, et al. Comparison of three generations of ActiGraph activity monitors under free-living conditions: do they provide comparable assessments of overall physical activity in 9-year old children? BMC Sports Sci Med Rehabil. 2014;6:26.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Treuth MS, Schmitz K, Catellier DJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc. 2004;36:1259–66.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Ridgers ND, Salmon J, Ridley K, et al. Agreement between activPAL and ActiGraph for assessing children’s sedentary time. Int J Behav Nutr Phys Act. 2012;9:15.

    Article  PubMed  PubMed Central  Google Scholar 

  97. 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;167:875–81.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Metzger JS, Catellier DJ, Evenson KR, et al. Patterns of objectively measured physical activity in the United States. Med Sci Sports Exerc. 2008;40:630–8.

    Article  PubMed  Google Scholar 

  99. Davis MG, Fox KR. Physical activity patterns assessed by accelerometry in older people. Eur J Appl Physiol. 2007;100:581–9.

    Article  PubMed  Google Scholar 

  100. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Yearb Sport Med. 2006;368:299–304.

    Google Scholar 

  101. Van Cauwenberghe E, Labarque V, Trost SG, et al. Calibration and comparison of accelerometer cut points in preschool children. Int J Pediatr Obes. 2011;6:e582–9.

    Article  PubMed  Google Scholar 

  102. Grydeland M, Bergh IH, Bjelland M, et al. Correlates of weight status among Norwegian 11-year-olds: the HEIA study. BMC Public Health. 2012;12:1053.

    Article  PubMed  PubMed Central  Google Scholar 

  103. Matthews C. Calibration for accelerometer output for adults. Med Sci Sports Exerc. 2005;S512:S512–22.

    Article  Google Scholar 

  104. Pruitt LA, Glynn NW, King AC, et al. Use of accelerometry to measure physical activity in older adults at risk for mobility disability. J Aging Phys Act. 2008;16:416–34.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Zisko N, Carlsen T, Salvesen Ø, et al. New relative intensity ambulatory accelerometer thresholds for elderly men and women: the Generation 100 study. BMC Geriatr. 2015;15:97.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Crouter SE, Churilla JR, Bassett DR. Estimating energy expenditure using accelerometers. Eur J Appl Physiol. 2006;98:601–12.

    Article  PubMed  Google Scholar 

  107. Crouter SE, Kuffel E, Haas JD, et al. Refined two-regression model for the ActiGraph accelerometer. Med Sci Sports Exerc. 2010;42:1029–37.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Liu S, Gao RX, Freedson PS. Computational methods for estimating energy expenditure in human physical activities. Med Sci Sports Exerc. 2012;44:2138–46.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Schmitz KH, Treuth M, Hannan P, et al. Predicting energy expenditure from accelerometry counts in adolescents girls. Med Sci Sports Exerc. 2005;37:155–61.

    Article  PubMed  PubMed Central  Google Scholar 

  110. 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.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We are deeply thankful to Patty Freedson, Professor (University of Massachusetts/Amherst, USA) and Catrine Tudor-Locke, PhD (University of Massachusetts/Amherst, USA), for their comments on an earlier draft. This is part of a PhD Thesis conducted in the Biomedicine Doctoral Studies at the University of Granada, Spain.

Author information

Authors and Affiliations

  1. PROFITH “PROmoting FITness and Health through physical activity” Research Group, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Ctra. Alfacar s/n, 18011, Granada, Spain

    Jairo H. Migueles, Cristina Cadenas-Sanchez, Jose Mora-Gonzalez, Jonatan R. Ruiz & Francisco B. Ortega

  2. Department of Sport Medicine, Norwegian School of Sport Sciences, Oslo, Norway

    Ulf Ekelund

  3. MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrooke’s Hospital Hills Road, Cambridge, UK

    Ulf Ekelund

  4. Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden

    Christine Delisle Nyström, Marie Löf, Jonatan R. Ruiz & Francisco B. Ortega

  5. Department of Clinical and Experimental Medicine, Faculty of the Health Sciences, Linköping University, Linköping, Sweden

    Marie Löf

  6. Department of Nutrition and Food Science, University of the Basque Country, UPV-EHU, Vitoria-Gasteiz, Spain

    Idoia Labayen

Authors
  1. Jairo H. Migueles
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Cristina Cadenas-Sanchez
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ulf Ekelund
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Christine Delisle Nyström
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jose Mora-Gonzalez
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Marie Löf
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Idoia Labayen
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Jonatan R. Ruiz
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Francisco B. Ortega
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jairo H. Migueles.

Ethics declarations

Funding

This review was conducted under the umbrella of the ActiveBrains project (DEP2013-47540). Jairo H. Migueles is supported by a Grant from the Spanish Ministry of Education, Culture and Sport (FPU15/02645). Cristina Cadenas-Sanchez is supported by a Grant from the Spanish Ministry of Economy and Competitiveness (BES-2014-068829). Jose Mora-Gonzalez is supported by a Grant from the Spanish Ministry of Education, Culture and Sport (FPU14/06837). Francisco B. Ortega and Jonatan R. Ruiz are supported by Grants from the Spanish Ministry of Science and Innovation (RYC-2011-09011 and RYC-2010-05957, respectively). Ulf Ekelund is supported by Grants from the Research Council of Norway (249932/F20) and the UK Medical Research Council (MC_UU_12015/3). Additional funding was obtained from the University of Granada, Plan Propio de Investigación 2016, Excellence actions: Units of Excellence; Unit of Excellence on Exercise and Health (UCEES). In addition, funding was provided by the SAMID III network, RETICS, funded by the PN I + D+I 2017-2021 (Spain), ISCIII- Sub-Directorate General for Research Assessment and Promotion, the European Regional Development Fund (ERDF) (Ref. RD16/0022) and the EXERNET Research Network on Exercise and Health in Special Populations (DEP2005-00046/ACTI).

Conflict of interest

Jairo H. Migueles, Cristina Cadenas-Sanchez, Ulf Ekelund, Christine Delisle Nyström, Jose Mora-Gonzalez, Marie Löf, Idoia Labayen, Jonatan R. Ruiz, and Francisco B. Ortega declare that they have no conflicts of interest relevant to the content of this review.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Migueles, J.H., Cadenas-Sanchez, C., Ekelund, U. et al. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med 47, 1821–1845 (2017). https://doi.org/10.1007/s40279-017-0716-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40279-017-0716-0

Keywords

Profiles

  1. Marie Löf View author profile