A Systematic Review of Heart Rate Variability as a Measure of Stress in Medical Professionals
Bibliographic record
Abstract
Understanding the physiological effects of responding to crises is a critical component in understanding how to manage and prepare medical professionals to be crisis responders. Heart rate variability (HRV) is the variation in rate between a succession of R-R intervals. This variation is not only affected by physiological processes such as respiration or metabolic rate but is also directly controlled by the autonomic nervous system. As such, heart rate variability has been proposed as a noninvasive tool to measure the physiological stress response. The aim of this systematic review is to consolidate heart rate variability literature in the context of medical emergencies to determine if heart rate variability changes predictably from baseline when responding to medical crises. This may demonstrate utility as an objective, noninvasive measure of stress response. A systematic literature review of six databases yielded 413 articles, 17 of which met our inclusion criteria of being written in English, measuring HRV in healthcare providers, and measuring HRV in real or simulated medical resuscitations or procedures. Articles were then analyzed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) scoring system. Out of the 17 articles reviewed, 11 demonstrated statistically significant results showing heart rate variability responding in a predictable manner to stress. Three articles utilized a medical simulation as the stressor, six used medical procedures, and eight used medical emergencies encountered during clinical work. Overall, a predictable trend in heart rate variability metrics of standard deviation from the mean value of normal-to-normal (N-N) intervals (SDNN), root mean square of the successive differences (RMSSD), mean number of times per time interval in which the change in successive normal sinus (N-N) intervals exceeds 50 ms (PNN50), low frequency % (LF%), and low-frequency-to-high-frequency ratio (LF/HF) was observed when responding to stress. This systematic literature review showed that heart rate variability among healthcare providers responding to stressful scenarios follows a predictable pattern of change and expands our understanding of the physiology of stress in healthcare providers. This review supports the use of HRV to monitor stress during high-fidelity simulation to ensure that appropriate physiological arousal is achieved during the training of medical personnel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".