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Correlates of physical activity behavior in adults: a data mining approach
Vahid Farrahi1, Maisa Niemelä2, Mikko Kärmeniemi3,4,5
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, P.O. 5000, FI-90014, Oulu, Finland. Vahid.farrahi@oulu.fi.
Summary
This study used data mining to identify 36 key factors predicting physical activity (PA) behavior in adults. The findings help in designing targeted interventions for different population subgroups.
Area of Science:
- Epidemiology
- Data Science
- Public Health
Background:
- Physical activity (PA) behavior is crucial for health.
- Identifying correlates of PA is essential for effective interventions.
- Previous studies often lack a comprehensive, data-driven approach to PA prediction.
Purpose of the Study:
- To apply a data mining approach to predict physical activity (PA) behavior.
- To establish a multilevel hierarchy of factors influencing PA.
- To identify correlates of PA behavior using a data-driven methodology.
Main Methods:
- Utilized cross-sectional data from the Northern Finland Birth Cohort 1966.
- Employed the chi-square automatic interaction detection (CHAID) decision tree technique.
- Defined PA behavior (active/inactive) based on accelerometer-measured activity profiles and analyzed correlates using generalized linear mixed models.
Main Results:
- A decision tree model identified 36 significant factors from 168 input variables, forming 54 participant subgroups.
- Key predictors included body fat percentage, heart rate recovery, sitting time, and extravagance score.
- These factors were associated with sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA) levels.
Conclusions:
- A data-driven model with 36 factors was established to predict PA behavior.
- The model can inform the design of multilevel interventions for specific subgroups.
- The study identified a broad set of factors for future PA correlates research.
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