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Estimating energy expenditure using accelerometers

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Abstract

The purpose of this study was to examine the validity of published regression equations designed to predict energy expenditure (EE) from accelerometers (Actigraph, Actical, and AMP-331) compared to indirect calorimetry, over a wide range of activities. Forty-eight participants (age: 35 ± 11.4 years) performed various activities that ranged from sedentary behaviors (lying, sitting) to vigorous exercise. The activities were split into three routines of six activities, and each participant performed at least one routine. The participants wore three devices (Actigraph, Actical, and AMP-331) and simultaneously, EE was measured with a portable metabolic system. For the Actigraph, 15 previously published equations were used to estimate EE from the accelerometer counts. For the Actical, two published equations were used to estimate EE from the accelerometer counts. For the AMP-331 we used the manufacturer’s equation to estimate EE. The Actigraph and Actical regressions tended to overestimate walking and sedentary activities and underestimate most other activities. The AMP-331 gave a close estimate of EE during walking, but overestimated sedentary/light activities and underestimated all other activities. The only equation not significantly different from actual time spent in both light and moderate physical activity was the Actigraph Freedson kcal equation. All equations significantly underestimated time spent in vigorous physical activity (P < 0.05). In conclusion, no single regression equation works well across a wide range of activities for the prediction of EE or time spent in light, moderate, and vigorous physical activity.

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References

  • Bassett DR Jr, Ainsworth BE, Swartz AM, Strath SJ, O’Brien WL, King GA (2000) Validity of four motion sensors in measuring moderate intensity physical activity. Med Sci Sports Exerc 32:S471–480

    Article  PubMed  Google Scholar 

  • Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

    PubMed  CAS  Google Scholar 

  • Brage S, Wedderkopp N, Franks PW, Andersen LB, Froberg K (2003) Reexamination of validity and reliability of the CSA monitor in walking and running. Med Sci Sports Exerc 35:1447–1454

    Article  PubMed  Google Scholar 

  • Brooks AG, Gunn SM, Withers RT, Gore CJ, Plummer JL (2005) Predicting walking METs and energy expenditure from speed or accelerometry. Med Sci Sports Exerc 37:1216–1223

    Article  PubMed  Google Scholar 

  • Crouter SE, Clowers KG, Bassett DR Jr (2006) A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 100:1324–1331

    Article  PubMed  Google Scholar 

  • Freedson PS, Melanson E, Sirard J (1998) Calibration of the Computer science and applications, Inc. accelerometer. Med Sci Sports Exerc 30:777–781

    Article  PubMed  CAS  Google Scholar 

  • Gildenhuys A, MacDonald P, Fyfe K, Stergiou P (2004) Accuracy of a new activity monitor for assessing exercise intensity during walking. Med Sci Sports Exerc 36:S197

    Article  Google Scholar 

  • Harris JA, Benedict FG (1919) A biometric study of basal metabolism in men. Carnegie Institute of Washington, Washington, DC, p 266

    Google Scholar 

  • Heil DP (2006) Predicting activity energy expenditure using the Actical activity monitor. Res Q Exerc Sport 77:64–80

    PubMed  Google Scholar 

  • Heil DP, Klippel NJ (2003) Validation of energy expenditure prediction algorithms in adolescents and teens using the Actical activity monitor. Med Sci Sports Exerc 35:S285

    Article  Google Scholar 

  • Heil DP, Higginson BK, Keller CP, Juergens CA (2003) Body size as a determinant of activity monitor output during overground walking. JEPonline 6:1–11

    Google Scholar 

  • Hendelman D, Miller K, Baggett C, Debold E, Freedson P (2000) Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc 32:S442–449

    Article  PubMed  CAS  Google Scholar 

  • Karabulut M, Crouter SE, Bassett DR Jr (2005) Comparison of two waist-mounted and two ankle-mounted electronic pedometers. Eur J Appl Physiol 95:335–343

    Article  PubMed  Google Scholar 

  • Klippel NJ, Heil DP (2003) Validation of energy expenditure prediction algorithms in adults using the Actical electronic activity monitor. Med Sci Sports Exerc 35:S284

    Article  Google Scholar 

  • Leenders NY, Sherman WM, Nagaraja HN (2000) Comparisons of four methods of estimating physical activity in adult women. Med Sci Sports Exerc 32:1320–1326

    Article  Google Scholar 

  • Leenders NY, Sherman WM, Nagaraja HN, Kien CL (2001) Evaluation of methods to assess physical activity in free-living conditions. Med Sci Sports Exerc 33:1233–1240

    PubMed  CAS  Google Scholar 

  • Leenders NY, Nelson TE, Sherman WM (2003) Ability of different physical activity monitors to detect movement during treadmill walking. Int J Sports Med 24:43–50

    Article  PubMed  CAS  Google Scholar 

  • McLaughlin JE, King GA, Howley ET, Bassett DR Jr, Ainsworth BE (2001) Validation of the COSMED K4b2 portable metabolic system. Int J Sports Med 22:280–284

    Article  PubMed  CAS  Google Scholar 

  • MTI Health Services (2004) Actisoft analysis software 3.2 user’s manual. MTI Health Services, Fort Walton Beach

    Google Scholar 

  • Nichols JF, Morgan CG, Chabot LE, Sallis JF, Calfas KJ (2000) Assessment of physical activity with the Computer Science and Applications, Inc., accelerometer: laboratory versus field validation. Res Q Exerc Sport 71:36–43

    PubMed  CAS  Google Scholar 

  • Pober DM, Raphael C, Freedson P (2004) Novel technique for assessing physical activity using accelerometer data. Med Sci Sports Exerc 36:S198

    Article  Google Scholar 

  • Puyau MR, Adolph AL, Vohra FA, Zakeri I, Butte NF (2004) Prediction of activity energy expenditure using accelerometers in children. Med Sci Sports Exerc 36:1625–1631

    PubMed  Google Scholar 

  • Strath SJ, Bassett DR Jr, Swartz AM (2003) Comparison of MTI accelerometer cut-points for predicting time spent in physical activity. Int J Sports Med 24:298–303

    Article  PubMed  CAS  Google Scholar 

  • Swartz AM, Strath SJ, Bassett DR Jr, O’Brien WL, King GA, Ainsworth BE (2000) Estimation of energy expenditure using CSA accelerometers at hip and wrist sites. Med Sci Sports Exerc 32:S450–S456

    Article  PubMed  CAS  Google Scholar 

  • Welk GJ, Blair SN, Wood K, Jones S, Thompson RW (2000) A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc 32:S489–S497

    Article  PubMed  CAS  Google Scholar 

  • Welk GJ, Schaben JA, Morrow JR Jr (2004) Reliability of accelerometry-based activity monitors: a generalizability study. Med Sci Sports Exerc 36:1637–1645

    PubMed  Google Scholar 

  • Yngve A, Nilsson A, Sjostrom M, Ekelund U (2003) Effect of monitor placement and of activity setting on the MTI accelerometer output. Med Sci Sports Exerc 35:320–326

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors would like to thank Cary Springer (UTK Statistical Consulting Services) for assisting with the statistical analyses. We are grateful to Lisa Jahns, Ph.D. in the Department of Nutrition at the University of Tennessee, Knoxville who loaned the Actical equipment for the duration of the study. This research was supported in part by the Charlie and Mai Coffey Endowment in Exercise Science. Dynastream Innovations, Inc. loaned the AMP-331 equipment for the duration of the study. No financial support was received from any of the activity monitor manufacturers, importers, or retailers.

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Authors and Affiliations

  1. Department of Exercise, Sport, and Leisure Studies, The University of Tennessee, Knoxville, TN, USA

    Scott E. Crouter, James R. Churilla & David R. Bassett Jr

  2. Division of Nutritional Sciences, Cornell University, 279 MVR, Ithaca, NY, 14853, USA

    Scott E. Crouter

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  1. Scott E. Crouter
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  2. James R. Churilla
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  3. David R. Bassett Jr
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Correspondence to Scott E. Crouter.

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Crouter, S.E., Churilla, J.R. & Bassett, D.R. Estimating energy expenditure using accelerometers. Eur J Appl Physiol 98, 601–612 (2006). https://doi.org/10.1007/s00421-006-0307-5

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  • DOI: https://doi.org/10.1007/s00421-006-0307-5

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