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Predicting Emotional Exhaustion with Multimodal Sensor Data During Return-to-Work Trajectories: A 6-Month

Lea Berkemeier1,2,3, Wim Kamphuis4, Hilbrand Oldenhuis5

  • 1Department of Psychology, Health and Technology, Centre for eHealth Research and Wellbeing, University of Twente, 7522NB, Enschede, The Netherlands. l.berkemeier@utwente.nl.

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Summary

Multimodal sensor data can modestly predict emotional exhaustion during return-to-work. Individual patterns of emotional exhaustion (EE) vary, requiring personalized return-to-work strategies.

Keywords:
Emotional exhaustionEmployeeLongitudinalReturn-to-workSick-listedWearable sensors

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Area of Science:

  • Occupational Health
  • Digital Health
  • Psychology

Background:

  • Emotional exhaustion (EE) is central to burnout and affects return-to-work (RTW) after sick leave.
  • Previous studies linked EE to sleep, activity, mobility, and smartphone use, but individual variations over time remain unclear.
  • Monitoring and predicting EE during RTW using sensor data requires further investigation.

Purpose of the Study:

  • To examine how emotional exhaustion (EE) can be monitored and predicted using multimodal data from smartphones and smart rings.
  • To investigate within- and between-person associations of EE with sensor-derived data during RTW trajectories.
  • To assess the predictive performance of multimodal features for EE.

Main Methods:

  • Eighteen employees on sick leave for burnout/stress provided daily Ecological Momentary Assessments (EMAs) of EE.
  • Multimodal sensor data on sleep, physical activity (Oura ring), mobility, and phone usage (Avicenna app) were collected for six months.
  • Correlational analyses, linear mixed models, and subject-dependent Random Forest (RF) models were used to analyze associations and predictive performance.

Main Results:

  • Subject-dependent RF models achieved an average predictive accuracy (Spearman's ρ̄) of 0.38.
  • Sleep and physical activity features showed more consistent associations with EE than mobility and smartphone usage.
  • Significant inter- and intra-individual variability was observed in EE patterns and predictive features.

Conclusions:

  • Multimodal sensor data offer modest predictive capabilities for emotional exhaustion, with notable individual differences.
  • The high variability in EE patterns underscores the need for tailored return-to-work counseling.
  • Future research should focus on longer monitoring periods and exploring inter- and intra-individual variability in EE.