The Feasibility and Reliability of Upper Arm–Worn Apple Watch Heart Rate Monitoring for Surgeons During Surgery: Observational Study
Bibliographic record
Abstract
BACKGROUND: Health care professionals, particularly those in surgical settings, face high stress levels, impacting their well-being. Traditional monitoring methods, like using Holter electrocardiogram monitors, are impractical in the operating room, limiting the assessment of physicians' health. Wrist-worn heart rate monitors, like the Apple Watch, offer promise but are restricted in surgeries due to sterility issues. OBJECTIVE: This study aims to assess the feasibility and accuracy of using an upper arm-worn Apple Watch for heart rate monitoring during robotic-assisted surgeries, comparing its performance with that of a wrist-worn device to establish a reliable alternative monitoring site. METHODS: This study used 2 identical Apple Watch Series 8 devices to monitor the heart rate of surgeons during robotic-assisted surgery. Heart rate data were collected from the wrist-worn and the upper arm-worn devices. Statistical analyses included calculating the mean difference and SD of difference between the 2 devices, constructing Bland-Altman plots, assessing accuracy based on mean absolute error and mean absolute percentage error, and calculating the intraclass correlation coefficient. RESULTS: The mean absolute errors for the whole group and for participants A, B, C, and D were 3.63, 3.58, 2.70, 3.93, and 4.28, respectively, and the mean absolute percentage errors were 3.58%, 3.34%, 2.42%, 4.58%, and 4.00%, respectively. Bland-Altman plots and scatter plots showed no systematic error when comparing the heart rate measurements obtained from the upper arm-worn and the wrist-worn Apple Watches. The intraclass correlation coefficients for participants A, B, C, and D were 0.559, 0.651, 0.508, and 0.563, respectively, with a significance level of P<.001, indicating moderate reliability. CONCLUSIONS: The findings of this study suggest that the upper arm is a viable alternative site for monitoring heart rate during surgery using an Apple Watch. The agreement and reliability between the measurements obtained from the upper arm-worn and the wrist-worn devices were good, with no systematic error and a high level of accuracy. These findings have important implications for improving data collection and management of the physical and mental demands of operating room staff during surgery, where wearing a watch on the wrist may not be feasible.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".