Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers.
OBJECTIVE
Accelerometers are promising tools for characterizing physical activity (PA) patterns in free-living persons. To date, validation of energy expenditure (EE) predictions from accelerometers has been restricted to short laboratory or simulated free-living protocols. This study seeks to determine the capabilities of eight previously published regression equations for three commercially available accelerometers to predict summary measures of daily EE.


METHODS
Study participants were outfitted with ActiGraph, Actical, and RT3 accelerometers, while measurements were simultaneously made during overnight stays in a room calorimeter, which provided minute-by-minute EE measurements, in a diverse subject population (n = 85). Regression equations for each device were used to predict the minute-by-minute metabolic equivalents (METs) along with the daily PA level (PAL).


RESULTS
Two RT3 regressions and one ActiGraph regression were not significantly different from calorimeter measured PAL. When data from the entire visit were divided into four intensity categories-sedentary, light, moderate, and vigorous-significant (P < 0.001) over- and underpredictions were detected in numerous regression equations and intensity categories.


CONCLUSIONS
Most EE prediction equations showed differences of <2% in the moderate and vigorous intensity categories. These differences, though small in magnitude, may limit the ability of these regressions to accurately characterize whether specific PA goals have been met in the field setting. New regression equations should be developed if more accurate prediction of the daily PAL or higher precision in determining the time spent in specific PA intensity categories is desired.
Rothney
Megan P
MP
Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA. rothneym@niddk.nih.gov
Schaefer
Emily V
EV
Neumann
Megan M
MM
Choi
Leena
L
Chen
Kong Y
KY
D000054:Acceleration; D000293:Adolescent; D000328:Adult; D000368:Aged; D002151:Calorimetry; D004734:Energy Metabolism; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D009043:Motor Activity / Q000502:physiology*; D011237:Predictive Value of Tests; D012044:Regression Analysis