No abstract

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.

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. There are many smartphone-based applications (apps) for cardiopulmonary resuscitation (CPR) training. We investigated the conformity and the learnability/usability of these apps for CPR training and real-life supports. Methods. We conducted a mixed-method, sequential explanatory study to assess CPR training apps downloaded on two apps stores in South Korea. Apps were collected with inclusion criteria as follows, Korean-language instruction, training features, and emergency supports for real-life incidents, and analyzed with two tests; 15 medical experts evaluated the apps' contents according to current Basic Life Support guidelines in conformity test, and 15 nonmedical individuals examined the apps using System Usability Scale (SUS) in the learnability/usability test. Results. Out of 79 selected apps, five apps were included and analyzed. For conformity (ICC, 0.95, p < 0.001), means of all apps were greater than 12 of 20 points, indicating that they were well designed according to current guidelines. Three of the five apps yielded acceptable level (greater than 68 of 100 points) for learnability/usability. Conclusion. All the included apps followed current BLS guidelines and a majority offered acceptable learnability/usability for layperson. Current and developmental smartphone-based CPR training apps should include accurate CPR information and be easy to use for laypersons that are potential rescuers in real-life incidents. For Clinical Trials. This is a clinical trial, registered at the Clinical Research Information Service (CRIS, cris.nih.go.kr), number KCT0001840.

BackgroundThis study aimed to investigate the relationship between body mass index (BMI) and sufficient chest compression depth (CCD) in obese patients by a mathematical model.Methods and ResultsThis retrospective analysis was performed with chest computed tomography images conducted between 2006 and 2018. We classified the selected individuals into underweight (<18.5), normal weight (≥18.5, <25), overweight (≥25, <30), and obese (≥30) groups according to BMI (kg/m2). We defined heart compression fraction (HCF) as [heart anteroposterior diameter ‐ (internal chest anteroposterior diameter ‐ proposed CCD)]heart anteroposterior diameter×100 and estimated under‐HCF (the value of HCF <20%), and over‐HCF (the residual depth <2 cm after simulation with chest compression depth 5 and 6 cm). We compared these outcomes between BMI groups. Of 30 342 individuals, 8856 were selected and classified into 4 BMI groups from a database. We randomly selected 100 individuals in each group and analyzed a total of 400 individuals’ cases. Higher BMI groups had a significantly decreased HCF with both 5 and 6 cm depth (P<0.001). The proportion of under‐HCF with both depths increased according to BMI group, whereas the proportion of over‐HCF decreased except for the 5 cm depth (P<0.001). The adjusted odds ratio of under‐HCF, according to BMI group after adjustment of age and sex, was 7.325 (95% CI, 3.412–15.726; P<0.001), with 5 cm and 10.517 (95% CI, 2.353–47.001; P=0.002) with 6 cm depth, respectively.ConclusionsThe recommended chest compression depth of 5 to 6 cm in the current international guideline is unlikely to provide sufficient ejection fraction during cardiopulmonary resuscitation in obese patients.