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How does day-to-day stress appraisal relate to coping among office workers in academia? An ecological momentary assessment study.

Stephanie HulinLarissa BolligerJunoš LukanAnneleen CaluwaertsRosalie De NeveMitja LustrekDirk De BacquerEsther Van Poel
Published in: Stress and health : journal of the International Society for the Investigation of Stress (2023)
Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day-to-day threat and challenge appraisal and day-to-day problem-focused coping, emotion-focused coping, and seeking social support among academic office workers. This study is based on an Ecological Momentary Assessment (EMA) design with a 15-working day data collection period utilising our self-developed STRAW smartphone application. A total of 55 office workers from academic institutions in Belgium (n = 29) and Slovenia (n = 26) were included and 3665 item measurements were analysed. Participants were asked approximately every 90 min about their appraisal of stressful events (experienced during the working day) and their coping styles. For data analysis, we used an unstructured covariance matrix in our linear mixed models. Challenge appraisal predicted problem-focused coping and threat appraisal predicted emotion-focused coping. Our findings suggest an association between threat appraisal as well as challenge appraisal and seeking social support. Younger and female workers chose social support more often as a coping style. While working from home, participants were less likely to seek social support. The findings of our EMA study confirm previous research on the relationship between stress appraisal and coping with stress. Participants reported seeking social support less while working from home compared to working at the office, making the work location an aspect that deserves further research.
Keyphrases
  • social support
  • depressive symptoms
  • data analysis
  • healthcare
  • mental health
  • systematic review
  • climate change
  • machine learning
  • heat stress
  • borderline personality disorder