• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Healthcare Innovation Point-of-Care Technologies Conference >Smartphone usage contexts and sensable patterns as predictors of future sedentary behaviors
【24h】

Smartphone usage contexts and sensable patterns as predictors of future sedentary behaviors

机译：智能手机使用情况和可感性模式作为未来久坐行为的预测因子

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sedentary behaviors such as prolonged occupational and leisure-time sitting are now ubiquitous in modern societies. Sedentary time is positively associated with increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. Smartphones can sense the sedentary behaviors performed by their users, as well as the contexts (situations) in which sedentary behaviors occur. In this paper, we explore whether the contexts that can be sensed by users' smartphones can be used to predict their future sedentary behaviors reliably. We analyze data gathered in a term-long study of 49 college students in order to discover their sedentary behavior patterns and contexts strongly correlated with sedentary states. The ability to predict sedentary behaviors will facilitate more effective computer-driven interventions based on the theory of planned behavior. Using logistic regression, we are able to classify user context variables such as location, time, and app usage to predict if the user will be "very sedentary" in the next hour with a precision of 73.1% (recall of 87.7%).
机译：久坐行为，如长期的职业和休闲时间坐在现代社会中普遍存在。久坐时间与肥胖，糖尿病，心血管疾病和全导致死亡率的风险增加呈正相关。智能手机可以感知用户执行的久坐行为，以及所发生久坐行为的上下文（情况）。在本文中，我们探讨了用户智能手机可以感知的上下文是否可用于可靠地预测其未来的久坐行为。我们分析了在49名大学生一年期间收集的数据，以发现他们的久坐行为模式和背景与久坐状态强烈相关。基于计划行为理论，预测久坐行为的能力将促进更有效的计算机驱动干预措施。使用Logistic回归，我们能够对用户上下文变量进行分类，例如位置，时间和应用程序使用，以预测下一小时的“非常久坐”，精度为73.1％（召回87.7％）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号