诸事不顺？利用可穿戴传感器检测非典型生活事件的影响
Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors
AI总结：
本研究通过多月中纵向研究和因果推断，揭示积极与消极生活事件对心理状态的不同影响，并验证可穿戴传感器结合嵌入学习模型能在真实环境中检测非典型生活事件，为自动化干预提供可行性依据。
摘要：
生活事件会显著影响我们的心理状态和工作表现。例如，压力已被证实与职业不满、焦虑加剧以及职场倦怠相关。我们通过对医院和航空航天工作人员进行为期数月的纵向研究，探讨了积极和消极生活事件对多种心理构念的影响。通过因果推断，我们证明积极生活事件会增加积极情绪，而消极事件会增加压力、焦虑和消极情绪。虽然大多数事件对心理状态的影响是短暂的，但重大消极事件（如生病或参加葬礼）会持续数天降低积极情绪。接下来，我们评估这些事件是否可以通过可穿戴传感器检测到，这类传感器能够低成本、非侵入性地监测健康相关因素。我们表明，这些传感器与基于嵌入的学习模型相结合，可以在“真实环境”中捕捉两个数据集中数百名工作人员的非典型生活事件。总体而言，我们的结果表明，基于生理传感的自动化干预可能是可行的，有助于工作人员调节生活事件带来的负面影响。
英文摘要：
Life events can dramatically affect our psychological state and work performance. Stress, for example, has been linked to professional dissatisfaction, increased anxiety, and workplace burnout. We explore the impact of positive and negative life events on a number of psychological constructs through a multi-month longitudinal study of hospital and aerospace workers. Through causal inference, we demonstrate that positive life events increase positive affect, while negative events increase stress, anxiety and negative affect. While most events have a transient effect on psychological states, major negative events, like illness or attending a funeral, can reduce positive affect for multiple days. Next, we assess whether these events can be detected through wearable sensors, which can cheaply and unobtrusively monitor health-related factors. We show that these sensors paired with embedding-based learning models can be used ``in the wild'' to capture atypical life events in hundreds of workers across both datasets. Overall our results suggest that automated interventions based on physiological sensing may be feasible to help workers regulate the negative effects of life events.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
