The stressed heart: Validity evidence supporting mobile heart rate variability applications to detect psychological stress in healthcare learners
Bibliographic record
Abstract
Abstract Purpose The stress experienced by healthcare learners and practitioners can impact learning, delivery of care, and mental health. Heightened awareness regarding this impact of stress has sparked the growing use of mobile health technologies for real‐time tracking of stress in health professions education. The purpose of this study was to evaluate what can accurately be interpreted by the scores generated by mobile technology, regarding psychological stress levels in medical learners. Methods In a quasi‐experimental within‐subjects design, 10 Emergency Medicine residents experienced two rest periods and two stress‐inducing simulation scenarios. Heart rate variability (HRV) parameters produced by a mobile HRV application were compared to a reference standard analysis software, and with traditional stress measures (salivary cortisol and self‐report measures) using Pearson correlation coefficients. To determine whether HRV parameters from the mobile application differentiate between rest & psychological stress conditions, a Multivariate Analysis of Variance (MANOVA), with condition (rest, stress) as the independent variable, was calculated for the HRV, cortisol and self‐report measures. Results The mobile application's time‐domain HRV parameters correlate strongly with the reference software (r values: 0.93 to 0.99, all p < 0.01), salivary cortisol levels (r = −0.54 to −63, all p <. 0.01) and self‐reported stress (r = −0.46 to −0.49, all p < 0.01). These time‐domain HRV parameters also accurately differentiated between rest and stress periods (eta 2 = 0.43–0.70, all p < 0.01). In contrast, the frequency‐domain parameter (LF/HF) of the mobile application showed weaker associations with the reference software (r = 0.10, p = 0.58) and other measures of stress (r = 0.11 to −0.16, NS), and did not differentiate between rest and stress periods (eta 2 = 0.07, p = 0.25). Conclusion This study provides some validity evidence for the use of a subset of time‐domain HRV metrics, captured through a mobile application, for the detection of psychological stress responses in simulated clinical settings. The results also highlight the heterogeneity in HRV metrics produced by various programs. Despite the promise of mobile technologies for the detection of stress in learners and health professionals, further validity research is needed to support their use to detect stress in the health professions.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".