Deriving objectively-measured sedentary indices from free-living accelerometry data in rural and urban African settings: a cost effective approach

Objectives To investigate the agreement between two data reduction approaches for detecting sedentary breaks from uni-axial accelerometry data collected in human participants. Free-living, uni-axial accelerometer data (n = 318) were examined for sedentary breaks using two different methods (Healy–Matthews; MAH/UFFE). The data were cleaned and reduced using MAH/UFFE Analyzer software and custom Microsoft Excel macro’s, such that the average daily sedentary break number were calculated for each data record, for both methods. Results The Healy–Matthews and MAH/UFFE average daily break number correlated closely (R 2 = 99.9%) and there was high agreement (mean difference: + 0.7 breaks/day; 95% limits of agreement: − 0.06 to + 1.4 breaks/day). A slight bias of approximately + 1 break/day for the MAH/UFFE Analyzer was evident for both the regression and agreement analyses. At a group level there were no statistically or practically significant differences within sample groups between the two methods.