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Int J Sports Med 2013; 34(11): 975-982
DOI: 10.1055/s-0033-1337945
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Actigraph GT3X: Validation and Determination of Physical Activity Intensity Cut Points

A. Santos-Lozano
1   Faculty of Health Science, Department of Biomedical Sciences, University of León, Spain
2   Department of Physioteraphy and Nursing, Universidad de Zaragoza, Huesca, Spain
,
F. Santín-Medeiros
2   Department of Physioteraphy and Nursing, Universidad de Zaragoza, Huesca, Spain
,
G. Cardon
3   Department of Movement and Sports Sciences, Ghent University, Gent, Belgium
,
G. Torres-Luque
4   Faculty of Science of Education, University of Jaén, Spain
,
R. Bailón
5   Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Spain
6   CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain
,
C. Bergmeir
7   Department of Computer Science and Artificial Intelligence, E.T.S. de Ingenierías Informática y de Telecomunicación, University of Granada, Spain
,
J. R. Ruiz
8   Physical Education and Sport, University of Granada, Spain
,
A. Lucia
9   Physiology, Universidad Europea De Madrid, Spain
,
N. Garatachea
2   Department of Physioteraphy and Nursing, Universidad de Zaragoza, Huesca, Spain
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accepted after revision 13. Februar 2013

Publikationsdatum:
22. Mai 2013 (online)

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Abstract

The aims of this study were: to compare energy expenditure (EE) estimated from the existing GT3X accelerometer equations and EE measured with indirect calorimetry; to define new equations for EE estimation with the GT3X in youth, adults and older people; and to define GT3X vector magnitude (VM) cut points allowing to classify PA intensity in the aforementioned age-groups. The study comprised 31 youth, 31 adults and 35 older people. Participants wore the GT3X (setup: 1-s epoch) over their right hip during 6 conditions of 10-min duration each: resting, treadmill walking/running at 3, 5, 7, and 9 km · h−1, and repeated sit-stands (30 times · min−1). The GT3X proved to be a good tool to predict EE in youth and adults (able to discriminate between the aforementioned conditions), but not in the elderly. We defined the following equations: for all age-groups combined, EE (METs)=2.7406+0.00056 · VM activity counts (counts · min−1)−0.008542 · age (years)−0.01380 ·  body mass (kg); for youth, METs=1.546618+0.000658 · VM activity counts (counts · min−1); for adults, METs=2.8323+0.00054 · VM activity counts (counts · min−1)−0.059123 · body mass (kg)+1.4410 · gender (women=1, men=2); and for the elderly, METs=2.5878+0.00047 · VM activity counts (counts · min−1)−0.6453 · gender (women=1, men=2). Activity counts derived from the VM yielded a more accurate EE estimation than those derived from the Y-axis. The GT3X represents a step forward in triaxial technology estimating EE. However, age-specific equations must be used to ensure the correct use of this device.

Key words

activity monitor - physical activity intensity - energy expenditure
 

  • References

  • 1 Abel MG, Hannon JC, Sell K, Lillie T, Conlin G, Anderson D. Validation of the Kenz Lifecorder EX and ActiGraph GT1M accelerometers for walking and running in adults. Appl Physiol Nutr Metab 2008; 33: 1155-1164
    CrossrefPubMedSuche in Google Scholar
  • 2 Atkinson G, Davison RC, Nevill AM. Performance characteristics of gas analysis systems: what we know and what we need to know. Int J Sports Med 2005; 26 (Suppl. 01) S2-S10
    Thieme ConnectPubMedSuche in Google Scholar
  • 3 Bassett Jr DR, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA. Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 2000; 32: S471-S480
    PubMedSuche in Google Scholar
  • 4 Bergmeir C, Benítez J. Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. J Stat Softw 2012; 46
    PubMedSuche in Google Scholar
  • 5 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1: 307-310
    PubMedSuche in Google Scholar
  • 6 Bouten CV, Westerterp KR, Verduin M, Janssen JD. Assessment of energy expenditure for physical activity using a triaxial accelerometer. Med Sci Sports Exerc 1994; 26: 1516-1523
    PubMedSuche in Google Scholar
  • 7 Butte NF, Ekelund U, Westerterp KR. Assessing physical activity using wearable monitors: measures of physical activity. Med Sci Sports Exerc 2012; 44: S5-S12
    CrossrefPubMedSuche in Google Scholar
  • 8 CareFusion C. Jaeger Oxycon Pro manual. Hoechberg, Germany: Care Fusion; 2009
    Suche in Google Scholar
  • 9 Chen KY, Bassett Jr DR. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc 2005; 37: S490-S500
    CrossrefPubMedSuche in Google Scholar
  • 10 Chen KY, Sun M. Improving energy expenditure estimation by using a triaxial accelerometer. J Appl Physiol 1997; 83: 2112-2122
    PubMedSuche in Google Scholar
  • 11 Colley RC, Tremblay MS. Moderate and vigorous physical activity intensity cut-points for the Actical accelerometer. J Sports Sci 2011; 29: 783-789
    CrossrefPubMedSuche in Google Scholar
  • 12 Crouter SE, Bassett Jr DR. A new 2-regression model for the Actical accelerometer. Br J Sports Med 2008; 42: 217-224
    CrossrefPubMedSuche in Google Scholar
  • 13 Crouter SE, Churilla JR, Bassett Jr DR. Estimating energy expenditure using accelerometers. Eur J Appl Physiol 2006; 98: 601-612
    CrossrefPubMedSuche in Google Scholar
  • 14 Crouter SE, Kuffel E, Haas JD, Frongillo EA, Bassett Jr DR. Refined two-regression model for the ActiGraph accelerometer. Med Sci Sports Exerc 2010; 42: 1029-1037
    PubMedSuche in Google Scholar
  • 15 Engineering/Marketing A . ActiLife users manual. Pensacola, FL: ActiGraph; 2009
    Suche in Google Scholar
  • 16 Fehling PC, Smith DL, Warner SE, Dalsky GP. Comparison of accelerometers with oxygen consumption in older adults during exercise. Med Sci Sports Exerc 1999; 31: 171-175
    PubMedSuche in Google Scholar
  • 17 Freedson P, Bowles HR, Troiano R, Haskell W. Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc 2012; 44: S1-S4
    PubMedSuche in Google Scholar
  • 18 Freedson P, Pober D, Janz KF. Calibration of accelerometer output for children. Med Sci Sports Exerc 2005; 37: S523-S530
    PubMedSuche in Google Scholar
  • 19 Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc 1998; 30: 777-781
    CrossrefPubMedSuche in Google Scholar
  • 20 Hall KS, Howe CA, Rana SR, Martin CL, Morey MC. METs and Accelerometry of Walking in Older Adults: Standard vs. Measured Energy Cost. Med Sci Sports Exerc 2012; in press
    PubMedSuche in Google Scholar
  • 21 Hanggi JM, Phillips LR, Rowlands AV. Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. J Sci Med Sport 2013; 16: 40-44
    CrossrefPubMedSuche in Google Scholar
  • 22 Harriss DJ, Atkinson G. Update – ethical standards in sport and exercise science research. Int J Sports Med 2013; 16: 819-821
    PubMedSuche in Google Scholar
  • 23 Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007; 39: 1423-1434
    CrossrefPubMedSuche in Google Scholar
  • 24 Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc 2000; 32: S442-S449
    PubMedSuche in Google Scholar
  • 25 Hooker SP, Feeney A, Hutto B, Pfeiffer KA, McIver K, Heil DP, Vena JE, Lamonte MJ, Blair SN. Validation of the actical activity monitor in middle-aged and older adults. J Phys Act Health 2011; 8: 372-381
    PubMedSuche in Google Scholar
  • 26 Howe CA, Staudenmayer JW, Freedson PS. Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis. Med Sci Sports Exerc 2009; 41: 2199-2206
    PubMedSuche in Google Scholar
  • 27 Hussey J, Bennett K, Dwyer JO, Langford S, Bell C, Gormley J. Validation of the RT3 in the measurement of physical activity in children. J Sci Med Sport 2009; 12: 130-133
    CrossrefPubMedSuche in Google Scholar
  • 28 Kozey SL, Lyden K, Howe CA, Staudenmayer JW, Freedson PS. Accelerometer output and MET values of common physical activities. Med Sci Sports Exerc 2010; 42: 1776-1784
    PubMedSuche in Google Scholar
  • 29 Lamarra N, Whipp BJ, Ward SA, Wasserman K. Effect of interbreath fluctuations on characterizing exercise gas exchange kinetics. J Appl Physiol 1987; 62: 2003-2012
    PubMedSuche in Google Scholar
  • 30 Maddison R, Jiang Y, Hoorn SV, Mhurchu CN, Lawes CM, Rodgers A, Rush E. Estimating energy expenditure with the RT3 triaxial accelerometer. Res Q Exerc Sport 2009; 80: 249-256
    CrossrefPubMedSuche in Google Scholar
  • 31 Matthew CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc 2005; 37: S512-S522
    CrossrefPubMedSuche in Google Scholar
  • 32 Mattocks C, Leary S, Ness A, Deere K, Saunders J, Tilling K, Kirkby J, Blair SN, Riddoch C. Calibration of an accelerometer during free-living activities in children. Int J Pediatr Obes 2007; 2: 218-226
    CrossrefPubMedSuche in Google Scholar
  • 33 Puyau MR, Adolph AL, Vohra FA, Butte NF. Validation and calibration of physical activity monitors in children. Obes Res 2002; 10: 150-157
    CrossrefPubMedSuche in Google Scholar
  • 34 Rothney MP, Brychta RJ, Meade NN, Chen KY, Buchowski MS. Validation of the ActiGraph two-regression model for predicting energy expenditure. Med Sci Sports Exerc 2010; 42: 1785-1792
    PubMedSuche in Google Scholar
  • 35 Ruiz JR, Ramirez-Lechuga J, Ortega FB, Castro-Pinero J, Benitez JM, Arauzo-Azofra A, Sanchez C, Sjostrom M, Castillo MJ, Gutierrez A, Zabala M, Group HS. Artificial neural network-based equation for estimating VO2max from the 20 m shuttle run test in adolescents. Artif Intell Med 2008; 44: 233-245
    CrossrefPubMedSuche in Google Scholar
  • 36 Rumelhart D, Hinton G, Williams R. Learning representations of back-propagation errors. Nature 1985; 323: 3
    PubMedSuche in Google Scholar
  • 37 Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport 2011; 14: 411-416
    CrossrefPubMedSuche in Google Scholar
  • 38 Staudenmayer J, Zhu W, Catellier DJ. Statistical considerations in the analysis of accelerometry-based activity monitor data. Med Sci Sports Exerc 2012; 44: S61-S67
    PubMedSuche in Google Scholar
  • 39 Strath SJ, Bassett Jr DR, Swartz AM. Comparison of MTI accelerometer cut-points for predicting time spent in physical activity. Int J Sports Med 2003; 24: 298-303
    Thieme ConnectPubMedSuche in Google Scholar
  • 40 Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc 2011; 43: 1360-1368
    PubMedSuche in Google Scholar
  • 41 Welk GJ, Eisenmann JC, Schaben J, Trost SG, Dale D. Calibration of the biotrainer pro activity monitor in children. Pediatr Exerc Sci 2007; 19: 145-158
    PubMedSuche in Google Scholar
  • 42 Welk GJ, McClain J, Ainsworth BE. Protocols for evaluating equivalency of accelerometry-based activity monitors. Med Sci Sports Exerc 2012; 44: S39-S49
    PubMedSuche in Google Scholar
  • 43 Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993; 39: 561-577
    PubMedSuche in Google Scholar
  • 44 Strath SJ, Pfeiffer KA, Whitt-Glover MC. Accelerometer use with children, older adults, and adults with functional limitations.. Med Sci Sports Exerc 2012; 44: S77-S85
    PubMedSuche in Google Scholar