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Vigilance refers to an individual’s ability to maintain attention over time. Vigilance decrement is particularly concerning in clinical environments where shift work and long working hours are common. This study identifies significant factors and indicators for predicting and monitoring individuals’ vigilance decrement. We enrolled 11 participants and measured their vigilance levels by recording their reaction times while completing the Psychomotor Vigilance Test. Additionally, we measured participants’ physiological responses and collected their sleep deprivation data, demographic information, and self-reported anxiety levels. Using repeated-measures correlation analysis, we found that decreased vigilance levels, indicated by longer reaction times, were associated with higher electrodermal activity ( p < .01), lower skin temperature ( p < .001), shorter fixation durations ( p < .05), and increased saccade frequency ( p < .05). Moreover, sleep deprivation significantly affected vigilance decrement ( p < .001). Our findings provide the potential to develop a predictive model of vigilance decrements using physiological signals collected from non-intrusive devices, as an alternative to current behavior-based methods.
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This content will become publicly available on September 3, 2026
A Bayesian Network Approach for Modeling Resident Physician Vigilance
Vigilance, the ability to sustain attention, is critical in healthcare, yet resident physicians face significant sleep deprivation, increasing their risk of vigilance decrement and medical errors. This study aimed to develop a predictive model of vigilance in this population using contextual factors, physiological measures, and eye-tracking data. Fifteen resident physicians participated in psychomotor vigilance tests (PVT) under sleep-deprived and non-sleep-deprived conditions, and completed questionnaires assessing sleep, anxiety, and workload. Bayesian Networks (BN) were employed to model vigilance, featuring layers for contextual factors (sleep, anxiety), performance (PVT reaction time), and observable features (eye movement, physiological responses). The three-layered BN integrating both contextual and multi-sensor (eye-tracking and physiological) data demonstrated the best prediction accuracy, compared to BNs with fewer layers and/or only one sensor type. This demonstrates that combining continuous physiological and eye-tracking data with contextual information enhances the prediction of vigilance decrement in resident physicians. This study contributes to the development of predictive tools for mitigating vigilance decrement and the future design of intervention strategies in demanding clinical settings.
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- Award ID(s):
- 2237661
- PAR ID:
- 10656552
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 69
- Issue:
- 1
- ISSN:
- 1071-1813
- Format(s):
- Medium: X Size: p. 548-552
- Size(s):
- p. 548-552
- Sponsoring Org:
- National Science Foundation
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