A smartwatch-based CPR feedback device improves chest compression quality among health care professionals and lay rescuers
Bibliographic record
Abstract
Background: Cardiopulmonary resuscitation (CPR) quality significantly impacts patient outcomes during cardiac arrests. With advancements in health care technology, smartwatch-based CPR feedback devices have emerged as potential tools to enhance CPR delivery. Objective: This study evaluated a novel smartwatch-based CPR feedback device in enhancing chest compression quality among health care professionals and lay rescuers. Methods: A single-center, open-label, randomized crossover study was conducted with 30 subjects categorized into 3 groups based on rescuer category. The Relay Response BLS smartwatch application was compared to a defibrillator-based feedback device (Zoll OneStep CPR Pads). Following an introduction to the technology, subjects performed chest compressions in 3 modules: baseline unaided, aided by the smartwatch-based feedback device, and aided by the defibrillator-based feedback device. Outcome measures included effectiveness, learnability, and usability. Results: < .05), compared to unaided compressions. Compression variability was significantly reduced with the smartwatch-based device (coefficient of variation: 14.9% vs 26.6%), indicating more consistent performance. Fifteen of 20 professional rescuers reached effective compressions using the smartwatch-based device in an average 2.6 seconds. A usability questionnaire revealed strong preference for the smartwatch-based device over the defibrillator-based device. Conclusion: The smartwatch-based device enhances the quality of CPR delivery by keeping compressions within recommended ranges and reducing performance variability. Its user-friendliness and rapid learnability suggest potential for widespread adoption in both professional and lay rescuer scenarios, contributing positively to CPR training and real-life emergency responses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".