Having a Bad Day? Detecting the Impact of Atypical Life Events Using Wearable Sensors
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2021Pattern Discovery in Time Series with Byte Pair Encodingcited by 1
- 2022Zero-shot meta-learning for small-scale data from human subjectscited by 1
- 2023Rare Life Event Detection via Mobile Sensing Using Multi-Task Learningcited by 0
- 2024Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Studycited by 0
- 2024Improving Engagement and Efficacy of mHealth Micro-Interventions for Stress Coping: an In-The-Wild Study16 Jul 2024
- 2023Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning31 May 2023
- 2022Zero-shot meta-learning for small-scale data from human subjects29 Mar 2022
- 2021Pattern Discovery in Time Series with Byte Pair Encoding30 May 2021