Using decision-tree machine learning to identify worker movement typologies

Reasons for the study

Although physical activity has important health benefits, a minority of Canadians are regularly physically active. There are many barriers to physical activity, and these barriers are complex, operating at both individual and environmental levels. Work factors, such as job demands and social and physical working conditions, are also barriers to physical activity for many of the 18.5 million employed Canadians who spend most of their days at work.

Yet, many interventions to increase physical activity focus on specific factors. Few studies have explored the complexity of interrelated and, potentially, synergistic relationships. These include relationships among factors related to socioeconomics, health status, gender, social responsibilities, physical environment and work. This study aims to fill this gap.

Objectives of the study

Target audience

These findings will be of interest to public health experts, employers, workers and people generally who are interested in knowing how to measure movement patterns of workers at and outside of work and the relationship of these patterns to health.