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Observational Study
Optimal wearable camera mount locations for medical supervision during simulated out-of-hospital cardiopulmonary resuscitation.
- Joo Jeong, Kyoung-Jun Song, Jung Chan Lee, Sang Do Shin, and Yu Jin Kim.
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Medicine (Baltimore). 2024 Dec 20; 103 (51): e40973e40973.
AbstractThe quality of the visual information transmitted from a scene is crucial for effective medical supervision in prehospital settings. This study investigated the influence of wearable camera mount locations on visibility during simulated out-of-hospital cardiopulmonary resuscitation. A prospective, observational, non-randomized simulation study was conducted to replicate a cardiac arrest scenario adhering to an advanced life support (ALS) protocol. Seven advanced emergency medical technicians (AEMTs) participated, and 5 camera mount locations were tested: the sternum, forehead, lateral side of the eyelid, mid-nasal, and glabella. Video recordings were captured from the Airway, Intravenous (IV), and Leading providers. Five experienced medical directors independently evaluated visibility scores (1-5) for each procedure with optimal visibility defined as a score of 4 to 5. Glabella mount demonstrated the highest median visibility score and interquartile range (5 [4-5]) and proportion of optimal visibility (77.5%) for most procedures across provider positions. Mixed models revealed significant estimates for the lateral side of the eyelid, mid-nasal, and glabella mounts compared to the sternum, with glabella having the largest effect size (estimate = 1.62). Generalized linear mixed models showed that the glabella mount had the highest odds ratio (OR = 8.07, 95% confidence interval [CI]: 3.01-21.6) to achieve optimal visibility. Wearable camera mount location significantly affected visibility during simulated resuscitation. Mounting cameras closer to eye level provided the most accurate visual data. Further research using objective measures, such as artificial intelligence, and evaluating the visibility of wearable cameras in real-world situations is warranted to optimize simulation-based training for prehospital care.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
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