Next Article in Journal
Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South
Next Article in Special Issue
A Digital-Health Program Based on Comprehensive Geriatric Assessment for the Management of Older People at Their Home: Final Recommendations from the MULTIPLAT_AGE Network Project
Previous Article in Journal
Seasonal Pattern of Cerebrovascular Fatalities in Cancer Patients
Previous Article in Special Issue
Collaborative Interprofessional Health Science Student Led Realistic Mass Casualty Incident Simulation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training

by
Elba Mauriz
1,2,*,
Sandra Caloca-Amber
1 and
Ana M. Vázquez-Casares
1
1
Department of Nursing and Physiotherapy, Universidad de León, Campus de Vegazana, s/n, 24071 León, Spain
2
Institute of Food Science and Technology (ICTAL), La Serna 58, 24007 León, Spain
*
Author to whom correspondence should be addressed.
Healthcare 2023, 11(4), 455; https://doi.org/10.3390/healthcare11040455
Submission received: 4 January 2023 / Revised: 1 February 2023 / Accepted: 2 February 2023 / Published: 4 February 2023
(This article belongs to the Special Issue Innovations in Interprofessional Care and Training)

Abstract

Training in healthcare skills can be affected by trainees’ workload when completing a task. Due to cognitive processing demands being negatively correlated to clinical performance, assessing mental workload through objective measures is crucial. This study aimed to investigate task-evoked changes in pupil size as reliable markers of mental workload and clinical performance. A sample of 49 nursing students participated in a cardiac arrest simulation-based practice. Measurements of cognitive demands (NASA-Task Load Index), physiological parameters (blood pressure, oxygen saturation, and heart rate), and pupil responses (minimum, maximum, and difference diameters) throughout revealed statistically significant differences according to performance scores. The analysis of a multiple regression model produced a statistically significant pattern between pupil diameter differences and heart rate, systolic blood pressure, workload, and performance (R2 = 0.280; F (6, 41) = 2.660; p < 0.028; d = 2.042). Findings suggest that pupil variations are promising markers to complement physiological metrics for predicting mental workload and clinical performance in medical practice.
Keywords: pupil response; mental workload; clinical performance; emergency care; simulation practice pupil response; mental workload; clinical performance; emergency care; simulation practice

Share and Cite

MDPI and ACS Style

Mauriz, E.; Caloca-Amber, S.; Vázquez-Casares, A.M. Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training. Healthcare 2023, 11, 455. https://doi.org/10.3390/healthcare11040455

AMA Style

Mauriz E, Caloca-Amber S, Vázquez-Casares AM. Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training. Healthcare. 2023; 11(4):455. https://doi.org/10.3390/healthcare11040455

Chicago/Turabian Style

Mauriz, Elba, Sandra Caloca-Amber, and Ana M. Vázquez-Casares. 2023. "Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training" Healthcare 11, no. 4: 455. https://doi.org/10.3390/healthcare11040455

APA Style

Mauriz, E., Caloca-Amber, S., & Vázquez-Casares, A. M. (2023). Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training. Healthcare, 11(4), 455. https://doi.org/10.3390/healthcare11040455

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop