With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone sensing data using the Beiwe platform from two groups of undergraduate students at a major North American university, one from January to March of 2020 (74 participants), the other from May to August (52 participants), to observe the differences in students' daily life patterns before and after the start of the pandemic. In this paper, we focus on the mobility patterns evidenced by GPS signal tracking from the students' smartphones and report findings using several analytical methods including principal component analysis, circadian rhythm analysis, and predictive modeling of perceived sadness levels using mobility-based digital metrics. Our findings suggest that compared to the pre-COVID group, students in the mid-COVID group generally (1) registered a greater amount of midday movement than movement in the morning (8-10am) and in the evening (7-9pm), as opposed to the other way around; (2) exhibited significantly less intradaily variability in their daily movement, and (3) had a significant lower correlation between their mobility patterns and negative mood.
翻译:由于2020年爆发COVID-19大流行,大多数学院和大学在2020年爆发COVID-19大流行后,开始限制校园活动、减少室内聚会和在线教学,这些变化要求学生相应调整和改变日常活动。为了调查与这些行为变化有关的模式,我们从北美一所大大学的两组本科生收集了使用Beiwe平台的智能感测数据,一个是2020年1月至3月的Beiwe平台(74人参加),另一个是5月至8月的Beiwe平台(52人参加),以观察学生在大流行开始前后的日常生活模式差异。在本文中,我们侧重于从学生智能手机的全球定位系统信号跟踪所显示的流动模式,并使用几种分析方法报告调查结果,包括主要组成部分分析、气动节奏分析,以及利用基于流动性的数字计量的感知悲伤程度的预测模型。我们的研究结果表明,与COVID前小组相比,中组的学生一般(1个中学生的中午运动次数大于上午(8-10am)和晚间运动(7-9-9pm),而不是周围的其他方式。 (2),他们每天的情绪变化变化明显减少。