Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods.

ABSTRACT:

Introduction

Machine learning (ML) accelerometer data processing methods have potential to improve the accuracy of device-based assessments of physical activity (PA) in young children. Yet the uptake of ML methods by health researchers has been minimal and the use of cut-points (CP) continues to be the norm, despite evidence of significant misclassification error. The lack of studies demonstrating a relative advantage for ML approaches over CP methods maybe a key contributing factor.

Purpose

The current study compared the accuracy of PA intensity predictions provided by ML classification models and previously published CPs for preschool-aged children.

Methods

In a free-living study, 31 preschool-aged children (mean age = 4.0 ± 0.9 y) wore wrist and hip ActiGraph GT3X+

SUBMITTER: Ahmadi MN 

PROVIDER: S-EPMC9007358 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature