Accelerometer prediction of energy expenditure: vector magnitude versus vertical axis.

UNLABELLED: It is suggested that triaxial accelerometers (RT3) are superior to single-plane accelerometers for predicting energy expenditure (EE).
PURPOSE: To compare the RT3 uniaxial and triaxial prediction of activity EE (AEE) during treadmill activities (TM) and activities of daily living (ADL).
METHODS: Two hundred and twelve subjects (aged 20-60 yr) completed TM speeds of 1.34, 1.56, and 2.23 m x s(-1) at 0% and 3% grades, stair ascent/descent, moving a box, and two randomly assigned ADL. Subjects wore a portable indirect calorimeter to measure EE to calculate AEE by subtracting resting metabolic rate. Acceleration counts in the vertical (V), medial-lateral, and anterior-posterior planes were collected in a single RT3 secured to the hip. Predicted AEE (RT3AEE) was estimated from vector magnitude (VM) counts using a proprietary algorithm. A paired t-test compared RT3AEE versus AEE. The relationship among V and VM counts and AEE was examined using linear regression analyses.
RESULTS: RT3 overestimated AEE for all activities combined, overestimated for TM (9.0%), and underestimated for ADL (34.3%; P < 0.001). The R2 values between RT3AEE and AEE for TM and ADL were R2 = 0.78 and R2 = 0.15, respectively. The RT3 underestimated activity with greater upper body movements by 24.4%-64.5% (P < 0.001). V and VM counts were similarly related to AEE (R2 = 0.35) and RT3AEE (R2 = 0.83-0.89).
CONCLUSIONS: Although the RT3 did not accurately predict AEE from accelerometer counts, stronger relationships existed between predicted and measured AEE for TM compared with ADL. Compared with V counts, using VM counts to predict AEE did not significantly improve the relationship between counts and AEE. Analytic techniques beyond linear regression with VM as a covariate or with counts from each axis entering the model separately may improve estimates of AEE from triaxial accelerometers.

RCT Entities:   Population Interventions Outcomes