Authors:
Bethany K. Bracken
1
;
Colette Houssan
2
;
John Broach
2
;
Andrew Milsten
2
;
Calvin Leather
1
;
Sean Tobyne
1
;
Aaron Winder
1
and
Mike Farry
1
Affiliations:
1
Charles River Analytics, 625 Mount Auburn St, Cambridge, MA, U.S.A.
;
2
University of Massachusetts Medical School, 55 N Lake Ave, Worcester, MA, U.S.A.
Keyword(s):
Cognitive Workload, Functional Near Infrared Spectroscopy (fNIRS), Medical Simulation, Training, Ecologically Valid, Real World, Disaster Medicine Training.
Abstract:
Medical personnel and first responders are often deployed to dangerous environments where their success at saving lives depends on their ability to act quickly and effectively. During training, non-invasive measurement of cognitive performance can provide trainers with insight into medical students’ skill mastery. Functional Near-Infrared Spectroscopy (fNIRS) is a direct and quantitative method to measure ongoing changes in brain blood oxygenation (HbO) in response to a person’s evolving cognitive state (i.e., cognitive workload or mental effort) that has only recently received significant attention for use in the real world. The work presented here includes data collection with a new, more portable, rugged design of an fNIRS sensor to test the functionality of this new sensor design and our ability to measure cognitive workload in a medical simulation training environment. To assess sensor and model accuracy, during breaks from the training, participants completed a gold-standard, l
aboratory task and during training in a medical simulation environment. Linear mixed model ANOVA showed that when we accounted for fixed effects of intercept and slope in our model, there was a significant difference in the HbR Ch1 model for n-back load (coef=0.009, p=0.034), intercept (coef=0.96, p=1.21e-07***), and load (slope) (coef=-0.09, p=0.03). Future work will present results of the data collected during the disaster response medical simulation training.
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