Key result
Physiological features including HRV predict laparoscopic task completion time and difficulty with ~0.84 correlation.
Why the study?
Surgeon cognitive load in laparoscopic surgery affects patient safety and healthcare economics, and robust assessment via physiological data could improve training and certification procedures.
Do physiological features (HR, HRV, BR) correlate with cognitive load and performance in novices during laparoscopic surgery training?
Observational (n=31)
No
Do physiological features (HR, HRV, BR) correlate with cognitive load and performance in novices during laparoscopic surgery training?
Physiological features such as heart rate, heart rate variability, and blink rate can predict cognitive load and task performance in novice laparoscopic surgeons.
May support objective assessment of novice laparoscopic performance; hypothesis-generating and requires prospective validation before clinical use.
Laparoscopic surgery can be exhausting and frustrating, and the cognitive load experienced by surgeons may have a major impact on patient safety as well as healthcare economics. As cognitive load decreases with increasing proficiency, its robust assessment through physiological data can help to develop more effective training and certification procedures in this area. We measured data from 31 novices during laparoscopic exercises to extract features based on cardiac and ocular variables. These were compared with traditional behavioural and subjective measures in a dual-task setting. We found significant correlations between the features and the traditional measures. The subjective task difficulty, reaction time, and completion time were well predicted by the physiology features. Reaction times to randomly timed auditory stimuli were correlated with the mean of the heart rate ([Formula: see text]) and heart rate variability ([Formula: see text]). Completion times were correlated with the physiologically predicted values with a correlation coefficient of 0.84. We found that the multi-modal set of physiology features was a better predictor than any individual feature and artificial neural networks performed better than linear regression. The physiological correlates studied in this paper, translated into technological products, could help develop standardised and more easily regulated frameworks for training and certification.
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Zakeri et al. (2020) conducted an observational in Healthy adult volunteers (novices in laparoscopic surgery) (n=31). Laparoscopic surgery training tasks (Ring Transfer and Threading) vs. Rest / Baseline (secondary task alone) was evaluated on Correlation between physiologically predicted values (using artificial neural networks) and actual task completion times (RCV 0.84). A multi-modal set of physiological features, including heart rate and heart rate variability, accurately predicted laparoscopic task completion time (RCV 0.84) and subjective task difficulty in novice subjects.
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