A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults.
The current study applied innovative data mining techniques to a community survey dataset to develop prediction models for two aspects of physical activity (i.e., active transport and screen time) in a sample of urban, primarily Hispanic, older adults (N=2,514). Main predictors for active transport (accuracy=69.29%, precision=0.67, recall=0.69) were immigrant status, high level of anxiety, having a place for physical activity, and willingness to make time for physical activity. The main predictors for screen time (accuracy=63.13%, precision=0.60, recall=0.63) were willingness to make time for exercise, having a place for exercise, age, and availability of family support to access health information on the Internet. Data mining methods were useful to identify intervention targets and inform design of customized interventions.
Sunmoo
S
Yoon
Niurka
N
Suero-Tejeda
Suzanne
S
Bakken
D000368:Aged; D000369:Aged, 80 and over; D057225:Data Mining; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D014505:Urban Population