Previous studies have demonstrated the potential for using smartwatches with a built-in accelerometer as feedback devices for high-quality chest compression during cardiopulmonary resuscitation. However, to the best of our knowledge, no previous study has reported the effects of this feedback on chest compressions in action. A randomized, parallel controlled study of 40 senior medical students was conducted to examine the effect of chest compression feedback via a smartwatch during cardiopulmonary resuscitation of manikins. A feedback application was developed for the smartwatch, in which visual feedback was provided for chest compression depth and rate. Vibrations from smartwatch were used to indicate the chest compression rate. The participants were randomly allocated to the intervention and control groups, and they performed chest compressions on manikins for 2 min continuously with or without feedback, respectively. The proportion of accurate chest compression depth (≥5 cm and ≤6 cm) was assessed as the primary outcome, and the chest compression depth, chest compression rate, and the proportion of complete chest decompression (≤1 cm of residual leaning) were recorded as secondary outcomes. The proportion of accurate chest compression depth in the intervention group was significantly higher than that in the control group (64.6±7.8% versus 43.1±28.3%; p = 0.02). The mean compression depth and rate and the proportion of complete chest decompressions did not differ significantly between the two groups (all p>0.05). Cardiopulmonary resuscitation-related feedback via a smartwatch could provide assistance with respect to the ideal range of chest compression depth, and this can easily be applied to patients with out-of-hospital arrest by rescuers who wear smartwatches.

Background: Although many smartphone application (app) programs provide education and guidance for basic life support, they do not commonly provide feedback on the chest compression depth (CCD) and rate. The validation of its accuracy has not been reported to date. This study was a feasibility assessment of use of the smartphone as a CCD feedback device. In this study, we proposed the concept of a new real-time CCD estimation algorithm using a smartphone and evaluated the accuracy of the algorithm. Materials and Methods: Using the double integration of the acceleration signal, which was obtained from the accelerometer in the smartphone, we estimated the CCD in real time. Based on its periodicity, we removed the bias error from the accelerometer. To evaluate this instrument's accuracy, we used a potentiometer as the reference depth measurement. The evaluation experiments included three levels of CCD (insufficient, adequate, and excessive) and four types of grasping orientations with various compression directions. We used the difference between the reference measurement and the estimated depth as the error. The error was calculated for each compression. Results: When chest compressions were performed with adequate depth for the patient who was lying on a flat floor, the mean (standard deviation) of the errors was 1.43 (1.00) mm. When the patient was lying on an oblique floor, the mean (standard deviation) of the errors was 3.13 (1.88) mm. Conclusions: The error of the CCD estimation was tolerable for the algorithm to be used in the smartphone-based CCD feedback app to compress more than 51 mm, which is the

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Rescuers who receive feedback of CC parameters from a smartwatch could perform adequate CC during infant CPR.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/.

Objective:Healthcare providers in emergency departments should wear respirators for infection protection. However, the wearer's vigorous movements during cardiopulmonary resuscitation may affect the protective performance of the respirator. Herein, we aimed to assess the effects of chest compressions (CCs) on the protective performance of respirators.Methods:This crossover study evaluated 30 healthcare providers from 1 emergency department who performed CC with real-time feedback. The first, second, and third groups started with a cup-type, fold-type, and valve-type respirator, respectively, after which the respirators were randomized for each group. The fit factors were measured using a quantitative fit testing device before and during the CC in each experiment. The protection rate was defined as the proportion of respirators achieving a fit factor ≥100.Results:The fold-type respirator had a significantly greater protection rate at baseline (100.0% ± 0.0%) compared to the cup-type (73.6% ± 39.6%, P = .003) and valve-type respirators (87.5% ± 30.3%, P = .012). During the CC, the fit factor values significantly decreased for the cup-type (44.9% ± 42.8%, P < .001) and valve-type respirators (59.5% ± 41.7%, P = .002), but not for the fold-type respirator (93.2% ± 21.7%, P = .095).Conclusions:The protective performances of respirators may be influenced by CC. Healthcare providers should identify the respirator that provides the best fit for their intended tasks.