Tsung-Chien Lu1, Yao-Ting Chang2, Te-Wei Ho3, Yi Chen4, Yi-Ting Lee4, Yu-Siang Wang4, Yen-Pin Chen5, Chu-Lin Tsai5, Matthew Huei-Ming Ma5, Cheng-Chung Fang5, Feipei Lai6, Hendrika W Meischke7, Anne M Turner8. 1. Dept. of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan; Dept. of Biomedical Informatics and Medical Education, University of Washington, Seattle, USA. 2. Dept. of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan. 3. Graduate Inst. of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. 4. Dept. of Physics, National Taiwan University, Taipei, Taiwan. 5. Dept. of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan. 6. Dept. of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Graduate Inst. of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan. 7. Dept. of Health Services, University of Washington, Seattle, USA. 8. Dept. of Biomedical Informatics and Medical Education, University of Washington, Seattle, USA; Dept. of Health Services, University of Washington, Seattle, USA. Electronic address: amturner@uw.edu.
Abstract
AIM: Cardiopulmonary resuscitation (CPR) quality affects survival after cardiac arrest. We aimed to investigate if a smartwatch with real-time feedback can improve CPR quality by healthcare professionals. METHODS: An app providing real-time audiovisual feedback was developed for a smartwatch. Emergency Department (ED) professionals were recruited and randomly allocated to either the intervention group wearing a smartwatch with the preinstalled app, or to a control group. All participants were asked to perform a two-minute CPR on a manikin at a 30:2 compression-ventilation ratio. Primary outcomes were the mean CCR and CCD measured on the manikin. A secondary outcome was the percentage of chest compressions meeting both the guideline-recommended rate (100-120 min-1) and depth (50-60 mm) of high-quality CPR during a 2-min period. Differences between groups were evaluated with t-test, Chi-Square test, or Mann-Whitney U test depending on the distribution. RESULTS:Eighty participants were recruited. 40 people were assigned to the intervention and 40 to the control group. The compression rates (mean ± SD, min-1) were significantly faster (but above the guideline recommendation, P < 0.001) in the control (129.1 ± 14.9) than in the intervention group (112.0 ± 3.5). The compression depths (mean ± SD, mm) were significantly deeper (P < 0.001) in the intervention (50.9 ± 6.6) than in the control group (39.0 ± 8.7). The percentage (%) of high-quality CPR was significantly higher (P < 0.001) in the intervention (median 39.4, IQR 27.1-50.1) than in the control group (median 0.0, IQR 0.0-0.0). CONCLUSION: Without real-time feedback, chest compressions tend to be too fast and too shallow. CPR quality can be improved with the assistance of a smartwatch providing real-time feedback.
RCT Entities:
AIM: Cardiopulmonary resuscitation (CPR) quality affects survival after cardiac arrest. We aimed to investigate if a smartwatch with real-time feedback can improve CPR quality by healthcare professionals. METHODS: An app providing real-time audiovisual feedback was developed for a smartwatch. Emergency Department (ED) professionals were recruited and randomly allocated to either the intervention group wearing a smartwatch with the preinstalled app, or to a control group. All participants were asked to perform a two-minute CPR on a manikin at a 30:2 compression-ventilation ratio. Primary outcomes were the mean CCR and CCD measured on the manikin. A secondary outcome was the percentage of chest compressions meeting both the guideline-recommended rate (100-120 min-1) and depth (50-60 mm) of high-quality CPR during a 2-min period. Differences between groups were evaluated with t-test, Chi-Square test, or Mann-Whitney U test depending on the distribution. RESULTS: Eighty participants were recruited. 40 people were assigned to the intervention and 40 to the control group. The compression rates (mean ± SD, min-1) were significantly faster (but above the guideline recommendation, P < 0.001) in the control (129.1 ± 14.9) than in the intervention group (112.0 ± 3.5). The compression depths (mean ± SD, mm) were significantly deeper (P < 0.001) in the intervention (50.9 ± 6.6) than in the control group (39.0 ± 8.7). The percentage (%) of high-quality CPR was significantly higher (P < 0.001) in the intervention (median 39.4, IQR 27.1-50.1) than in the control group (median 0.0, IQR 0.0-0.0). CONCLUSION: Without real-time feedback, chest compressions tend to be too fast and too shallow. CPR quality can be improved with the assistance of a smartwatch providing real-time feedback.
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