Correlates of physical activity behavior in adults: a data mining approach.

ABSTRACT:

Purpose

A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior.

Methods

Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting con

SUBMITTER: Farrahi V 

PROVIDER: S-EPMC7376928 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature