A method to estimate free-living active and sedentary behavior from an accelerometer.

ABSTRACT:

Introduction

Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings.

Purpose

The purpose of this study was to develop and validate two novel machine-learning methods (Sojourn-1 Axis [soj-1x] and Sojourn-3 Axis [soj-3x]) in a free-living setting.

Methods

Participants were directly observed in their natural environment for 10 consecutive hours on three separate occasions. Physical activity and SB estimated from soj-1x, soj-3x, and a neural network previously calibrated in the laboratory (lab-nnet) were compared with direct observation.

Results

Compared with lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias [95% confidence interval] = 33.1 [25.9 to 40.4]

SUBMITTER: Lyden K 

PROVIDER: S-EPMC4527685 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature