For total EE , correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices .
For active EE , correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices .
The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome .
Triaxial and multisensor devices tend to be more valid monitors .
The assessment of physical activity in healthy population s and in those with chronic diseases is challenging .
The aim of this systematic review was to identify whether available activity monitors ( AM ) have been appropriately vali date d for use in assessing physical activity in these groups .
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 % grade s , stair ascent/descent , moving a box , and two r and omly 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
For total EE , correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices .
For active EE , correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices .
The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome .
Triaxial and multisensor devices tend to be more valid monitors .
The assessment of physical activity in healthy population s and in those with chronic diseases is challenging .
The aim of this systematic review was to identify whether available activity monitors ( AM ) have been appropriately vali date d for use in assessing physical activity in these groups .