Comparison of MTI accelerometer cut-points for predicting time spent in physical activity

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 x 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 cutpoints gave substantially different estimates of time spent data has important implications for researchers using accelerometers to predict time spent in different intensity categories.