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

Abstract

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 (R² = 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.

Keywords: Accelerometer; Measurement; Movement monitor; Sedentarism.

Fig. 1

Diagrammatic representation of the data structure and algorithms. a Data format of an individual, cleaned Excel data file (column A–D) including the Healy–Matthews algorithm (column E–H); b cell functions for the Healy–Matthews algorithm; c cell content and function for a Microsoft Excel Array algorithm; d sedentary break identification for two algorithms

Fig. 2

Agreement analyses between the outputs of the two algorithms. a Bland–Altman plot between the Healy–Matthews algorithm and the MAH/UFFE for average breaks/day; b Linear regression plot between the Healy–Matthews algorithm and MAH/UFFE for average breaks/day