The purpose of this study was to examine the validity of seven child-specific ActiGraph prediction equations/cut points (Crouter vector magnitude two-regression model [Cvm2RM], Crouter vertical axis two-regression model [Cva2RM], Freedson child equation, Treuth equation, Trost equation, Puyau equation, and Evenson equation) for estimating energy expenditure and time spent in sedentary behaviors, light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA).Methods
Forty boys and 32 girls (mean ± SD; age = 12 ± 0.8 yr) participated in the study. Participants performed eight structured activities and approximately 2 h of free-living activity. Activity data were collected using an ActiGraph GT3X+, positioned on the right hip, and energy expenditure (METRMR; activity V˙O2 divided by resting V˙O2) was measured using a Cosmed K4b2. ActiGraph prediction equations were compared against the Cosmed for METRMR and time spent in sedentary behaviors, LPA, MPA, VPA, and moderate and vigorous physical activity.Results
For the structured activities, all prediction methods were significantly different from measured METRMR for three activities or more (P < 0.05); however, all provided close estimates of METRMR during walking. On average, participants were monitored for 95.0 ± 36.5 min during the free-living measurement. The Cvm2RM and the Puyau methods were within 0.9 METRMR of measured free-living METRMR (P > 0.05); all other methods significantly underestimated measured METRMR (P < 0.05). The Cva2RM was within 9.7 min of measured time spent in sedentary behaviors, LPA, MPA, and moderate and vigorous physical activity, which was the best of the methods examined. All prediction equations underestimated VPA by 6.0–13.6 min.Conclusion
Compared with the Cosmed, the Cvm2RM and the Puyau methods provided the best estimate of METRMR and the Cva2RM provided the closest estimate of time spent in each intensity category during the free-living measurement. Lastly, all prediction methods had large individual prediction errors.