A new 2-regression model for the Actical accelerometer.
OBJECTIVE
The objective of this study was to develop a new 2-regression model relating Actical activity counts to METs.


METHODS
Forty-eight participants (mean (SD) age 35 (11.4) years) performed 10 min bouts of various activities ranging from sedentary behaviours to vigorous physical activities. Eighteen activities were split into three routines with each routine being performed by 20 individuals. Forty-five routines were randomly selected for the development of a new 2-regression model and 15 tests were used to cross-validate the new 2-regression model and compare it against existing equations. During each routine, the participant wore an Actical accelerometer on the hip and oxygen consumption was simultaneously measured by a portable metabolic system. The coefficient of variation (CV) of four consecutive 15 s epochs was calculated for each minute. For each activity, the average CV and the counts min(-1) were calculated for minutes 4-9. If the CV was < or =13% a walk/run regression equation was used and if the CV was >13% a lifestyle/leisure time physical activity regression was used.


RESULTS
An exponential regression line (R(2) = 0.912; standard error of the estimate (SEE) = 0.149) was used for activities with a CV< or =13%, and a cubic regression line (R(2) = 0.884, SEE = 0.804) was used for activities with a CV>13%. In the cross-validation group the mean estimates, using the new 2-regression model with an inactivity threshold, were within 0.56 METs of measured METs for each of the activities performed (p> or =0.05), except cycling (p<0.05).


CONCLUSIONS
For most activities examined the new 2-regression model predicted METs more accurately than currently available equations for the Actical accelerometer.
Crouter
S E
SE
Division of Nutritional Sciences, Cornell University, Ithaca, NY,USA. Scott.crouter@umb.edu
Bassett
D R
DR
D000054:Acceleration; D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D004734:Energy Metabolism / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D010101:Oxygen Consumption / Q000502:physiology; D012044:Regression Analysis; D012420:Running / Q000502:physiology; D012680:Sensitivity and Specificity; D016138:Walking / Q000502:physiology