title: A Data Mining Approach for Exploring Correlates of Self-Reported Comparative Physical Activity Levels of Urban Latinos.
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.
authors.affiliation: School of Nursing, Columbia University, NY, USA.
authors.forename: Sunmoo
authors.initials: S
authors.lastname: Yoon
authors.affiliation: School of Nursing, Columbia University, NY, USA.
authors.forename: Manuel C
authors.initials: MC
authors.lastname: Co
authors.affiliation: School of Nursing, Columbia University, NY, USA.
authors.forename: Niurka
authors.initials: N
authors.lastname: Suero-Tejeda
authors.affiliation: School of Nursing, Columbia University, NY, USA.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Bakken
mesh_terms: D000328:Adult; D000368:Aged; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D003863:Depression / Q000208:ethnology; D015444:Exercise; D005260:Female; D015438:Health Behavior / Q000208:ethnology*; D006630:Hispanic or Latino / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009518:New York; D015995:Prevalence; D012307:Risk Factors; D040242:Risk Reduction Behavior; D057185:Sedentary Behavior / Q000208:ethnology*; D057566:Self Report; D011795:Surveys and Questionnaires; D014481:United States / Q000208:ethnology; D014505:Urban Population / Q000706:statistics & numerical data; D055815:Young Adult