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 .
Calibration of the Computer Science and Applications, Inc. accelerometer.
PURPOSE We established accelerometer count ranges for the Computer Science and Applications , Inc. ( CSA ) activity monitor corresponding to commonly employed MET categories . METHODS Data were obtained from 50 adults ( 25 males , 25 females ) during treadmill exercise at three different speeds ( 4.8 , 6.4 , and 9.7 km x h(-1 ) ) . RESULTS Activity counts and steady-state oxygen consumption were highly correlated ( r = 0.88 ) , and count ranges corresponding to light , moderate , hard , and very hard intensity levels were < or = 1951 , 1952 - 5724 , 5725 - 9498 , > or = 9499 cnts x min(-1 ) , respectively . A model to predict energy expenditure from activity counts and body mass was developed using data from a r and om sample of 35 subjects ( r2 = 0.82 , SEE = 1.40 kcal x min(-1 ) ) . Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed ( SEE = 0.50 - 1.40 kcal x min(-1 ) ) . CONCLUSIONS These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer
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 .