Comparison of MTI Accelerometer Cut-Points for Predicting Time Spent in Physical Activity (original) (raw)

Int J Sports Med 2003; 24(4): 298-303
DOI: 10.1055/s-2003-39504

Training & Testing

1Department of Health and Exercise Science, The University of Tennessee, Knoxville, Tennessee, USA

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.

Key words

Energy expenditure - accelerometer - oxygen consumption - accuracy.

References

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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