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Updated: Mar 19, 2026

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
Published on: March 7, 2019
A Data Mining Approach for Exploring Correlates of Self-Reported Comparative Physical Activity Levels of Urban
Sunmoo Yoon1, Manuel C Co1, Niurka Suero-Tejeda1
1School of Nursing, Columbia University, NY, USA.
Abstract:
We applied data mining techniques to a community-based behavioral dataset to build prediction models to gain insights about physical activity levels as the foundation for future interventions for urban Latinos. Our application of data mining strategies identified environment factors including having a convenient location for physical activity and psychological factors including depression as the strongest correlates of self-reported comparative physical activity among hundreds of variables. The data mining methods were useful to build prediction models to gain insights about perceptions of physical activity behavior as compared to peers.
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