Int J Sports Med 2003; 24(4): 298-303
DOI: 10.1055/s-2003-39504
Training & Testing
© Georg Thieme Verlag Stuttgart · New York

Comparison of MTI Accelerometer Cut-Points for Predicting Time Spent in Physical Activity

S.  J.  Strath1 , D.  R.  Bassett1  Jr. , A.  M.  Swartz1
  • 1Department of Health and Exercise Science, The University of Tennessee, Knoxville, Tennessee, USA
Further Information

Publication History

Accepted after revision: January 8, 2003

Publication Date:
04 June 2003 (online)

Abstract

The purpose of this study was to establish the accuracy of five published accelerometer regression equations that predict time spent in different intensity classifications during free-living activities. Ten participants completed physical tasks in a field setting for a near-continuous 5 - 6 h-period while oxygen uptake and accelerometer data were collected. The amount of time spent in resting/light, moderate and hard activity was computed from 3 and 6 MET cut-points associated with five existing regression formulas relating accelerometer counts × min-1 to energy expenditure. The Freedson cut-points over-estimated resting/light activity by 34 min (13 %) and under-estimated moderate activity by 38 min (60 %). The Hendelman cut-points for all activities underestimated resting/light activity by 77 min (29 %), and overestimated moderate activity by 77 min (120 %). The Hendelman cut-points developed from walking activities over-estimated resting/light activity by 37 min (14 %) and under-estimated moderate activity by 38 min (60 %). Estimates from the Swartz cut-points for estimating time spent in resting/light, moderate and hard intensity activity were not different from the criterion measure. The Nichols cut-points over-estimated resting/light activity by 31 min (12 %) and under-estimated moderate activity by 35 min (55 %). Even though the Swartz method did not differ from measured time spent in moderate activity on a group basis, on an individual basis, large errors were seen. This was true for all regression formulas. These errors highlight some of the limitations to using hip-mounted accelerometers to reflect physical activity patterns. The finding that different accelerometer cut-points gave substantially different estimates of time spent data has important implications for researchers using accelerometers to predict time spent in different intensity categories.

References

  • 1 Ainsworth B E, Bassett DR Jr, Strath S J, Swartz A M, O’Brien W L, Thompson R W, Jones D A, Macera C A, Kimsey C D. Comparison of three methods for measuring the time spent in physical activity.  Med Sci Sports Exerc. 2000;  32 S457-S464
  • 2 Bassett DR Jr, Ainsworth B E, Swartz A M, Strath S J, O’Brien W L, King G A, Howley E T. Validity of four motion sensors in measuring moderate intensity physical activity.  Med Sci Sports Exerc. 2000;  32 S471-S480
  • 3 Durstine J L, Haskell W L. Effects of exercise training on plasma lipids and lipoproteins.  ESSR. 1994;  22 477-522
  • 4 Freedson P, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer.  Med Sci Sports Exerc. 1998;  30 777-781
  • 5 Helmrich S P, Ragland D R, Leung W R, Paffenbarger RS Jr. Physical activity and reduced occurrence of non-insulin dependent diabetes mellitus.  N Eng J Med. 1991;  325 147-152
  • 6 Hendelman D, Miller K, Bagget 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
  • 7 Lee I M. Physical activity, fitness and cancer. In: Bouchard C, Shepard RJ, Stephens T (eds). Physical Activity, Fitness, and Health Champaign, IL; Human Kinetics Inc. 1994: 814-831
  • 8 Leenders N Y, Sherman W M, Nagaraja H N, Kien C L. Evaluation of methods to assess physical activity in free-living conditions.  Med Sci Sports Exerc. 2001;  33 1233-1240
  • 9 McLaughlin J E, King G A, Howley E T, Bassett DR Jr, Ainsworth B E. Validation of the Cosmed K4b2 portable metabolic system.  Int J Sports Med. 2001;  22 280-283
  • 10 Morris J N, Clayton D G, Everitt M G, Semmence A M, Burgess E H. Exercise in leisure time: Coronary attack and death rates.  Br Heart J. 1990;  63 325-334
  • 11 Nichols J F, Morgan C G, Chabot L E, Sallis JF, Calfas K J. Assessment of physical activity with the Computer Science and Applications, Inc. accelerometer: Laboratory versus field validation.  RQES. 2000;  71 36-43
  • 12 Paffenbarger RS Jr, Wing A L, Hyde R T, Jung D L. Physical activity and incidence of hypertension in college alumni.  Am J Epidemiol. 1983;  117 245-257
  • 13 Paffenbarger RS Jr, Hyde R T, Wing A L, Hsieh C. Physical activity, all-cause mortality, and longevity of college alumni.  N Eng J Med. 1986;  314 605-613
  • 14 Pate R, Pratt M, Blair S, Haskell W, Macera C, Bouchard C, Buchner D, Ettinger W, Heath G, King A, Kriska A, Leon A, Marcus B, Morris J, Paffenbarger R Jr, Patrick K, Pollock M, Rippe J, Sallis J, Wilmore J. Physical activity and public health.  JAMA. 1995;  273 402-407
  • 15 Strath S J, Bassett DR Jr, Swartz A M, Thompson D L. Simultaneous heart rate-motion sensor technique to estimate energy expenditure.  Med Sci Sports Exerc. 2001;  33 2118-2123
  • 16 Swartz A M, Strath S J, Bassett DR Jr, O’Brien W L, King G A, Ainsworth B E. Estimation of energy expenditure using CSA accelerometers at hip and wrist sites.  Med Sci Sports Exerc. 2000;  32 S450-S456
  • 17 U.S. Department of Health and Human Services .Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion,. 1996: 3

D. R. Bassett, Jr.

Department of Health and Exercise Science

The University of Tennessee · 1914 Andy Holt Avenue · Knoxville, TN 37996-2700 · USA ·

Phone: (865) 974-8883

Fax: (865) 974-8981

Email: dbassett@utk.edu

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