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

机译：智能手机的使用环境和合理的模式可以预测未来的久坐行为

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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：在现代社会中，久坐不动的行为，例如长时间的职业和休闲时间，现在很普遍。久坐时间与肥胖，糖尿病，心血管疾病和全因死亡率增加的风险呈正相关。智能手机可以感知其用户执行的久坐行为以及久坐行为发生的环境（情境）。在本文中，我们探讨了用户智能手机可以感知的环境是否可以可靠地预测他们未来的久坐行为。我们分析了一项针对49位大学生的长期研究中收集的数据，以发现他们与久坐状态密切相关的久坐行为模式和语境。预测久坐行为的能力将促进基于计划行为理论的更有效的计算机驱动的干预措施。使用Logistic回归，我们能够对用户上下文变量（例如位置，时间和应用使用情况）进行分类，以预测用户在接下来的一个小时内是否会“非常久坐”，其准确性为73.1％（召回率为87.7％）。

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