Biosensors, Biomarkers and Biometrics: a Bootcamp Perspective

Abstract

IntroductionCompetitive physical performance is routinely monitored by wearable technology (biosensors), yet professional healthcare is not, despite high prevalence of trainee stress and burnout, notwithstanding the corresponding risk to patient safety. This study aimed to document the physiological stress response of UK Core Surgical Trainees (CSTs) during simulation training.MethodsCSTs (n=20, 10 male) were fitted with Vital Scout Wellness Monitors (VivaLNK, Campbell, California, USA) for an intensive 3-day training bootcamp. In addition to physiological parameters, CST demographics, event diaries and Maslach Burnout Inventory scores were recorded prospectively during exposure to three scenarios: interactive lectures, clinical skills simulation and non-technical (communication) training.ResultsBaseline heart rate (BHR, 60 bpm (range 39–81 bpm)) and baseline respiratory rate (14/min (11–18/min)) varied considerably and did not correlate (rho 0.076, p=0.772). BHR was associated with weekly exercise performed (66 bpm (<1 hour) vs 43 bpm (>5 hour), rho −0.663, p=0.004). Trainee response (standardised median heart rate vs BHR) revealed heart rate was related proportionately to lectures (71 bpm, p<0.001), non-technical skills training (79 bpm, p<0.001) and clinical skills simulation (88 bpm, p<0.001). Respiratory rate responded similarly (p<0.001 in each case). Heart rate during clinical skills simulation was associated with emotional exhaustion (rho 0.493, p=0.044), but maximum heart rate was unrelated to CSTs’ perceived peak stressors.DiscussionStress response, as derived from positive sympathetic heart rate drive varied over two-fold, with a direct implication on oxygen uptake and energy expenditure, and highlighting the daily physical demands placed upon clinicians.

Surgical Sabermetrics

“…For example, Wu et al found that using cognitive and behavioral metrics through EEG and eye-metrics, with machine-learning can predict training outcomes with a 72.5% accuracy 110 . EEG features can also detect high CogL and altered performance during training 44,57,110–113 . Optimizing CogL in trainees can improve their capacity for learning and progression 114–116 .…”

“…110 EEG features can also detect high CogL and altered performance during training. 44,57,[110][111][112][113] Optimizing CogL in trainees can improve their capacity for learning and progression. [114][115][116] In one example, Maimon et al (2022) used CogL levels via EEG in simulation to assess if a trainee was "ready" to operate on a live patient.…”

Trainee burnout: when does the fire start?

“…8 James et al have recently demonstrated that such devices are practical for assessment of physiological variables among Core Surgical Trainees, during a 3-day training boot camp (including simulated practical tasks), and therefore this gives an indication that wearable technology could offer a practical solution to monitoring physiological parameters of medical professionals in the clinical setting. 9…”