A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults.
A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults.
复制标题
DOI：
10.3928/00989134-20150420-01
复制
发表时间：
2015-07
影响因子：
1.3
通讯作者：
Bakken S
中科院分区：
文献类型：
--
作者：
Yoon S;Suero-Tejeda N;Bakken S
本研究将创新的数据挖掘技术应用于社区调查数据集，以开发老年（主要是西班牙裔）城市成年人样本（N= 2514）身体活动（主动交通和屏幕时间）两个方面的预测模型。主动运输的主要预测因子(精度=69.29%，精度。67、回忆。69)是移民身份，高度焦虑，有地方进行体育活动，并愿意腾出时间进行体育活动。主要预测因子为屏幕时间(准确度=63.13%，精密度。60、回忆。63)是否愿意抽出时间进行锻炼，是否有锻炼的地方，年龄，以及是否有家人支持在互联网上查找健康信息。数据挖掘方法有助于确定干预目标，并为定制干预的设计提供信息。
This study applied innovative data mining techniques to a community survey dataset to develop prediction models for two aspects of physical activity (active transport and screen time) in sample of older, primarily Hispanic, urban adults (N=2, 514). Main predictors for active transport (accuracy=69.29%, precision .67, recall .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 .60, recall .63) were willingness to make time for exercise, having a place for exercise, age, and availability of family support to look up health information on the Internet. Data mining methods were useful to identify intervention targets and inform design of customized interventions.