A new chest compression depth feedback algorithm for high-quality CPR based on smartphone

Design and Evaluation of CPR Emergency Equipment for Non-Professionals

Sudden cardiac death is a sudden and highly fatal condition. Implementing high-quality emergency cardiopulmonary resuscitation (CPR) early on is an effective rescue method for this disease. However, the rescue steps of CPR are complicated and difficult to remember, and the quantitative indicators are difficult to control, which leads to a poor quality of CPR emergency actions outside the hospital setting. Therefore, we have developed CPR emergency equipment with a multisensory feedback function, aiming to guide rescuers in performing CPR through visual, auditory, and tactile interaction. This equipment consists of three components: first aid clothing, an audio-visual integrated terminal, and a vital sign detector. These three components are based on a micro-power WiFi-Mesh network, enabling the long-term wireless transmission of the multisensor data. To evaluate the impact of the multisensory feedback CPR emergency equipment on nonprofessionals, we conducted a controlled experiment involving 32 nonmedical subjects. Each subject was assigned to either the experimental group, which used the equipment, or the control group, which did not. The main evaluation criteria were the chest compression (CC) depth, the CC rate, the precise depth of the CC ratio (5-6 cm), and the precise rate of the CC ratio -(100-120 times/min). The results indicated that the average CC depth in the experimental group was 51.5 ± 1.3 mm, which was significantly better than that of the control group (50.2 ± 2.2 mm, p = 0.012). Moreover, the average CC rate in the experimental group (110.1 ± 6.2 times/min) was significantly higher than that of the control group (100.4 ± 6.6 times/min) (p < 0.001). Compared to the control group (66.37%), the experimental group showed a higher proportion of precise CC depth (82.11%), which is closer to the standard CPR rate of 100%. In addition, the CC ratio of the precise rate was 93.75% in the experimental group, which was significantly better than that of 56.52% in the control group (p = 0.024). Following the experiment, the revised System Availability Scale (SUS) was utilized to evaluate the equipment's usability. The average total SUS score was 78.594, indicating that the equipment's acceptability range was evaluated as 'acceptable', and the overall adjective rating was 'good'. In conclusion, the multisensory feedback CPR emergency equipment significantly enhances the CC performance (CC depth, CC rate, the precise depth of CC ratio, the precise rate of CC ratio) of nonprofessionals during CPR, and the majority of participants perceive the equipment as being easy to use.

An evolutionary approach to continuously estimate CPR quality parameters from a wrist-worn inertial sensor

Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model’s precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of $$\pm 2.22$$ ± 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training.

Impact of a Smart-Ring-Based Feedback System on the Quality of Chest Compressions in Adult Cardiac Arrest: A Randomized Preliminary Study

This study aimed to assess the effectiveness of a novel chest compression (CC) smart-ring-based feedback system in a manikin simulation. In this randomized, crossover, controlled study, we evaluated the effect of smart-ring CC feedback on cardiopulmonary resuscitation (CPR). The learnability and usability of the tool were evaluated with the System Usability Scale (SUS). Participants were divided into two groups and each performed CCs with and without feedback 2 weeks apart, using different orders. The primary outcome was compression depth; the proportion of accurate-depth (5–6 cm) CCs, CC rate, and the proportion of complete CCs (≤1 cm of residual leaning) were assessed additionally. The feedback group and the non-feedback group showed significant differences in compression depth (52.1 (46.3–54.8) vs. 47.1 (40.5–49.9) mm, p = 0.021). The proportion of accurate-depth CCs was significantly higher in the interventional than in the control condition (88.7 (30.0–99.1) vs. 22.6 (0.0–58.5%), p = 0.033). The mean SUS score was 83.9 ± 8.7 points. The acceptability ranges were ‘acceptable’, and the adjective rating was ‘excellent’. CCs with smart-ring feedback could help achieve the ideal range of depth during CPR. The smart-ring may be a valuable source of CPR feedback.

Effects of the non-contact cardiopulmonary resuscitation training using smart technology

Aims

Accurate cardiopulmonary resuscitation (CPR) performance is an essential skill for nursing students so they need to learn the skill correctly from the beginning and carry that forward with them into their clinical practice. For the new normal after coronavirus disease 2019 (COVID-19), safe training modules should be developed. This study aimed to develop non-contact CPR training using smart technology for nursing students and to examine its effects, focusing on the accuracy of their performance. The study used a prospective, single-blind, randomized, and controlled trial with repeated measures.

Methods and results

The non-contact CPR training with smart technology consisted of a 40-min theoretical online lecture session and an 80-min non-contact practice session with real-time feedback devices and monitoring cameras. Sixty-four nursing students were randomly assigned to either an experimental group (n = 31) using non-contact training or a control group (n = 33) using general training. The accuracy of chest compression and mouth-to-mouth ventilation, and overall performance ability were measured at pretest, right after training, and at a 4-week post-test. The non-contact CPR training significantly increased the accuracy of chest compression (F = 63.57, P < 0.001) and mouth-to-mouth ventilation (F = 33.83, P < 0.001), and the overall performance ability (F = 35.98, P < 0.001) compared to the general CPR training over time.

Conclusions

The non-contact CPR training using smart technology help nursing students develop their techniques by self-adjusting compression depth, rate, release and hand position, and ventilation volume and rate in real time. Nursing students can learn CPR correctly through the training allowing real-time correction in safe learning environments without face-to-face contact.

Development of Smart-Ring-Based Chest Compression Depth Feedback Device for High Quality Chest Compressions: A Proof-of-Concept Study

Recently, a smart-device-based chest compression depth (CCD) feedback system that helps ensure that chest compressions have adequate depth during cardiopulmonary resuscitation (CPR) was developed. However, no CCD feedback device has been developed for infants, and many feedback systems are inconvenient to use. In this paper, we report the development of a smart-ring-based CCD feedback device for CPR based on an inertial measurement unit, and propose a high-quality chest compression depth estimation algorithm that considers the orientation of the device. The performance of the proposed feedback system was evaluated by comparing it with a linear variable differential transformer in three CPR situations. The experimental results showed compression depth errors of 2.0 ± 1.1, 2.2 ± 0.9, and 1.4 ± 1.1 mm in the three situations. In addition, we conducted a pilot test with an adult/infant mannequin. The results of the experiments show that the proposed smart-ring-based CCD feedback system is applicable to various chest compression methods based on real CPR situations.

Recognition of Abnormal Chest Compression Depth Using One-Dimensional Convolutional Neural Networks

When the displacement of an object is evaluated using sensor data, its movement back to the starting point can be used to correct the measurement error of the sensor. In medicine, the movements of chest compressions also involve a reciprocating movement back to the starting point. The traditional method of evaluating the effects of chest compression depth (CCD) is to use an acceleration sensor or gyroscope to obtain chest compression movement data; from these data, the displacement value can be calculated and the CCD effect evaluated. However, this evaluation procedure suffers from sensor errors and environmental interference, limiting its applicability. Our objective is to reduce the auxiliary computing devices employed for CCD effectiveness evaluation and improve the accuracy of the evaluation results. To this end, we propose a one-dimensional convolutional neural network (1D-CNN) classification method. First, we use the chest compression evaluation criterion to classify the pre-collected sensor signal data, from which the proposed 1D-CNN model learns classification features. After training, the model is used to classify and evaluate sensor signal data instead of distance measurements; this effectively avoids the influence of pressure occlusion and electromagnetic waves. We collect and label 937 valid CCD results from an emergency care simulator. In addition, the proposed 1D-CNN structure is experimentally evaluated and compared against other CNN models and support vector machines. The results show that after sufficient training, the proposed 1D-CNN model can recognize the CCD results with an accuracy rate of more than 95%. The execution time suggests that the model balances accuracy and hardware requirements and can be embedded in portable devices.

Guess it (SVUAL): An app designed to help nursing students acquire and retain knowledge about basic and advanced life support techniques

To design an app that helps nursing students to acquire and retain knowledge of Basic and Advanced Life Support techniques, as well as analyze the students' gamification experience. The study had two phases: 1) App design and development and 2) experimental study. A total of 184 students participated, with 92 in the experimental group and 92 in the control group. The instruments used were the Guess it (SVUAL) app, a test on knowledge and the Gameful Experience Scale. The app was deemed to have a suitable level of content and user-friendliness of 97%. The experimental group obtained a higher average score on the knowledge test than the control group (U = 2835.500; Z = -3.968; p < 0.05). On the re-test, the experimental group also obtained a higher average score than the control group. As for the experience within the game, all the dimensions scored higher than average, except the absence of negative effects dimension, which indicates that the app had very few negative consequences on the participants. The developed app has proven to have a good level of content and to be user-friendly, improving knowledge levels and retention of information in nursing students.

Smartwatch feedback device for high-quality chest compressions by a single rescuer during infant cardiac arrest: a randomized, controlled simulation study

OBJECTIVE

According to the guidelines, rescuers should provide chest compressions (CC) ~1.5 inches (40 mm) for infants. Feedback devices could help rescuers perform CC with adequate rates (CCR) and depths (CCD). However, there is no CC feedback device for infant cardiopulmonary resuscitation (CPR). We suggest a smartwatch-based CC feedback application for infant CPR.

PARTICIPANTS AND METHODS

We created a smartwatch-based CC feedback application. This application provides feedback on CCD and CCR by colour and text for infant CPR. To evaluate the application, 30 participants were divided randomly into two groups on the basis of whether CC was performed with or without the assistance of the smartwatch application. Both groups performed continuous CC-only CPR for 2 min on an infant mannequin placed on a firm table. We collected CC parameters from the mannequin, including the proportion of correct depth, CCR, CCD and the proportion of correct decompression depth.

RESULTS

Demographics between the two groups were not significantly different. The median (interquartile range) proportion of correct depth was 99 (97-100) with feedback compared with 83 (58-97) without feedback (P = 0.002). The CCR and proportion of correct decompression depth were not significantly different between the two groups (P = 0.482 and 0.089). The CCD of the feedback group was significantly deeper than that of the control group [feedback vs. control: 41.2 (39.8-41.7) mm vs. 38.6 (36.1-39.6) mm; P=0.004].

CONCLUSION

Rescuers who receive feedback of CC parameters from a smartwatch could perform adequate CC during infant CPR.

Effect of Watch-Type Haptic Metronome on the Quality of Cardiopulmonary Resuscitation: A Simulation Study

Objectives

The aim of this study was to test the applicability of haptic feedback using a smartwatch to the delivery of cardiac compression (CC) by professional healthcare providers.

Methods

A prospective, randomized, controlled, case-crossover, standardized simulation study of 20 medical professionals was conducted. The participants were randomly assigned into haptic-first and non-haptic-first groups. The primary outcome was an adequate rate of 100–120/min of CC. The secondary outcome was a comparison of CC rate and adequate duration between the good and bad performance groups.

Results

The mean interval between CCs and the number of haptic and non-haptic feedback-assisted CCs with an adequate duration were insignificant. In the subgroup analysis, both the good and bad performance groups showed a significant difference in the mean CC interval between the haptic and non-haptic feedback-assisted CC groups—good: haptic feedback-assisted (0.57–0.06) vs. non-haptic feedback-assisted (0.54–0.03), p < 0.001; bad: haptic feedback-assisted (0.57–0.07) vs. non-haptic feedback-assisted (0.58–0.18), p = 0.005—and the adequate chest compression number showed significant differences— good: haptic feedback-assisted (1,597/75.1%) vs. non-haptic feedback-assisted (1,951/92.2%), p < 0.001; bad: haptic feedbackassisted (1,341/63.5%) vs. non-haptic feedback-assisted (523/25.4%), p < 0.001.

Conclusions

A smartwatch cardiopulmonary resuscitation feedback system could not improve rescuers' CC rate. According to our subgroup analysis, participants might be aided by the device to increase the percentage of adequate compressions after one minute.

Using a smartphone application (PocketCPR) to determine CPR quality in a bystander CPR scenario - A manikin trial

PURPOSE OF THE STUDY

Feedback devices and dispatcher assistance increase CPR quality in bystander resuscitation. Yet, there is no data comparing both approaches with uninstructed CPR. The present prospective, randomized, controlled, manikin trial aims to determine the effects of the use of a smartphone application (PocketCPR) on CPR quality in a bystander CPR scenario compared to dispatcher-assisted telephone CPR and uninstructed CPR.

METHODS

100 laypersons were included to perform 8-min CPR on a manikin. Volunteers were randomly assigned to one of four groups: (1) uninstructed CPR (uninstructed group), (2) dispatcher-assisted telephone CPR (telephone-group), (3) guidance and feedback through a smartphone application (app-group) and (4) dispatcher-assisted telephone CPR combined with the smartphone-app (telephone + app-group).

RESULTS AND DISCUSSION

There was no significant difference in the time to first compression between the uninstructed and the app-group (p = 0.052), likewise between the telephone- and the telephone + app-group (p = 0.193). The no-flow-time of the uninstructed group was significantly longer compared to all other groups (p < 0.001). Median compression rate was significantly higher and within the recommended range in the app- and the telephone + app-group. There was no significant difference regarding correct compression depth between the four groups. Correct hand position and complete thorax release was found significantly more frequently in groups with smartphone-app support.

CONCLUSIONS

Feedback by a smartphone application can improve bystander CPR quality in terms of no-flow-time, compression rate, correct hand position, thorax release and does not delay CPR onset. However, the use of a smartphone application does not improve compression depth significantly.

The effectiveness of traditional Basic Life Support training and alternative technology-enhanced methods in high schools

Background:

Implementation of resuscitation training in school programs is a promising approach to improve rates of cardiopulmonary resuscitation use by trained bystanders. Unfortunately, theoretical cardiopulmonary resuscitation instruction alone is not sufficiently effective in developing practical skills.

Objectives:

This study aimed to investigate the effectiveness of traditional Basic Life Support training and alternative instructional methods to achieve learning objectives of Basic Life Support education.

Methods:

This quasi-experimental study was conducted in a secondary school in Ankara, Turkey. Eighty-three voluntary students were randomly allocated to theoretical (Group A), video-based (Group B), and mobile-assisted video-based instructions (Group C). All groups were led by the course teacher. Assessments were conducted in training and again 1 week later. Assessments were based on Basic Life Support knowledge and confidence performance scores.

Results:

Statistically significant difference was found for the groups’ Confidence Scale scores (F(2, 73) = 3.513, p = 0.035, ηp2 = 0.088); Group C (6.76 ± 1.70) scored higher than Group A. The groups’ Basic Life Support checklist scores were statistically significant (F(2, 73) = 28.050, p = 0.000, ηp2 = 0.435); Group C (32.32 ± 3.84) scored higher than the other groups. Statistically significant difference was found for the groups’ measurable Basic Life Support scores (F(2, 73) = 13.527, p = 0.000, ηp2 = 0.270); and Group C (23.76 ± 3.98) scored higher than the other groups.

Conclusion:

Our findings showed that all instruction methods led to increased Basic Life Support knowledge scores. The mobile-assisted program significantly increased knowledge scores. Same-group high-quality cardiopulmonary resuscitation parameters were more positive than the other instruction groups except for hand position. Group C students expressed higher confidence in their ability to act in an emergency when witnessing a victim collapse.

Real-Time Chest Compression Quality Measurements by Smartphone Camera

Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5 s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0 cpm), the calculated chest compression rate was within ±10 cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.

A novel depth estimation algorithm of chest compression for feedback of high-quality cardiopulmonary resuscitation based on a smartwatch

INTRODUCTION

High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR.

METHODS

For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin. We developed an algorithm based on the assumptions that (1) maximal acceleration measured by the smartwatch accelerometer and the chest compression depth (CCD) are positively correlated and (2) the magnitude of acceleration at a specific time point and interval is correlated with its neighboring points. We defined a statistic value M as a function of time and the magnitude of maximal acceleration. We labeled and processed collected data and determined the relationship between M value, compression rate and CCD. We built a model accordingly, and developed a smartwatch app capable of detecting CCD. For validation, researchers wore a smartwatch with the preinstalled app and performed chest compression-only CPR on the manikin at target sessions. We compared the CCD results given by the smartwatch and the reference using the Wilcoxon Signed Rank Test (WSRT), and used Bland-Altman (BA) analysis to assess the agreement between the two methods.

RESULTS

We analyzed a total of 3978 compressions that covered the target rate of 80-140/min and CCD of 4-7 cm. WSRT showed that there was no significant difference between the two methods (P = 0.084). By BA analysis the mean of differences was 0.003 and the bias between the two methods was not significant (95% CI: -0.079 to 0.085).

CONCLUSION

Our study indicates that the algorithm developed for estimating CCD based on a smartwatch with a built-in accelerometer is promising. Further studies will be conducted to evaluate its application for CPR training and clinical practice.

Effectiveness of feedback with a smartwatch for high-quality chest compressions during adult cardiac arrest: A randomized controlled simulation study

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.

Development of a diagnostic feedback device to assess neonatal cardiopulmonary resuscitation chest compression performance

Neonatal cardiopulmonary resuscitation (NCPR) is an important intervention to save the lives of newborns who suffer from cardiac and respiratory arrest. Despite its importance there is a dearth of NCPR research and no commercially available feedback device suitable for use during NCPR. The aim of this study is to develop a diagnostic feedback device in the form of a patch placed on the infant's chest. The diagnostic feedback patch measures both the compression depth and force during NCPR, while giving audio-visual feedback according to current NCPR guidelines. The patch was systematically evaluated by conducting a series of hardware validation tests to assess the depth, force and feedback performance. The average errors in the depth and force were found to be 10.8% and 12.4%, respectively, with maximal errors below 20.7% and 24.1%. These results along with positive outcome of the feedback test suggest that the device is reliable on a hardware level and is suitable for further evaluation in a clinical setting.

Evaluation of Smartphone Applications for Cardiopulmonary Resuscitation Training in South Korea

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.

Chest compression rate measurement from smartphone video

Background

Out-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any.

Methods

All calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison.

Results

Overall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders.

Conclusions

The presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device.

Using an inertial navigation algorithm and accelerometer to monitor chest compression depth during cardiopulmonary resuscitation

We present an original method using a low cost accelerometer and a Kalman-filter based algorithm to monitor cardiopulmonary resuscitation chest compressions (CC) depth. A three-axis accelerometer connected to a computer was used during CC. A Kalman filter was used to retrieve speed and position from acceleration data. We first tested the algorithm for its accuracy and stability on surrogate data. The device was implemented for CC performed on a manikin. Different accelerometer locations were tested. We used a classical inertial navigation algorithm to reconstruct CPR depth and frequency. The device was found accurate enough to monitor CPR depth and its stability was checked for half an hour without any drift. Average error on displacement was ±0.5mm. We showed that depth measurement was dependent on the device location on the patient or the rescuer. The accuracy and stability of this small low-cost accelerometer coupled to a Kalman-filter based algorithm to reconstruct CC depth and frequency, was found well adapted and could be easily implemented.

Smartwatches as chest compression feedback devices: A feasibility study

BACKGROUND

Recently, there have been attempts to use smartphones and smartwatches as the feedback devices to improve the quality of chest compressions. In this study, we compared chest compression depth feedback accuracy between a smartphone and a smartwatch in a hands-only cardiopulmonary resuscitation scenario, using a manikin with a displacement sensor system.

METHODS

Ten basic life support providers participated in this study. Guided by the chest compression depths displayed on the monitor of a laptop, which received data from the manikin, each participant performed 2min of chest compressions for each target depth (35mm and 55mm) on a manikin while gripping a smartphone and wearing a smartwatch. Participants had a rest of 1h between the instances, and the first target depth was set at random. Each chest compression depth data value from the smartphone and smartwatch and a corresponding reference value from the manikin with the displacement system were recorded. To compare the accuracy between the smartphone and smartwatch, the errors, expressed as the absolute of the differences between the reference and each device, were calculated.

RESULTS

At both target depths, the error of the smartwatch were significantly smaller than that of the smartphone (the errors of the smartphone vs. smartwatch at 35mm: 3.4 (1.3) vs. 2.1 (0.8) mm; p=0.008; at 55mm: 5.3 (2.8) vs. 2.3 (0.9) mm; p=0.023).

CONCLUSION

The smartwatch-based chest compression depth feedback was more accurate than smartphone-based feedback.

Effectiveness of chest compression feedback during cardiopulmonary resuscitation in lateral tilted and semirecumbent positions: a randomised controlled simulation study

Feedback devices have been shown to improve the quality of chest compression during cardiopulmonary resuscitation for patients in the supine position, but no studies have reported the effects of feedback devices on chest compression when the chest is tilted. Basic life support-trained providers were randomly assigned to administer chest compressions to a manikin in the supine, 30° left lateral tilt and 30° semirecumbent positions, with or without the aid of a feedback device incorporated into a smartphone. Thirty-six participants were studied. The feedback device did not affect the quality of chest compressions in the supine position, but improved aspects of performance in the tilted positions. In the lateral tilted position, the median (IQR [range]) chest compression rate was 99 (99-100 [96-117]) compressions.min(-1) with and 115 (95-128 [77-164]) compressions.min(-1) without feedback (p = 0.05), and the proportion of compressions of correct depth was 55 (0-96 [0-100])% with and 1 (0-30 [0-100])% without feedback (p = 0.03). In the semirecumbent position, the proportion of compressions of correct depth was 21 (0-87 [0-100])% with and 1 (0-26 [0-100])% without feedback (p = 0.05). Female participants applied chest compressions at a more accurate rate using the feedback device in the lateral tilted position but were unable to increase the chest compression depth, whereas male participants were able to increase the force of chest compression using the feedback device in the lateral tilted and semirecumbent positions. We conclude that a feedback device improves the application of chest compressions during simulated cardiopulmonary resuscitation when the chest is tilted.

Proper target depth of an accelerometer-based feedback device during CPR performed on a hospital bed: a randomized simulation study

PURPOSE

Feedback devices are used to improve chest compression (CC) quality related to survival rates in cardiac arrest. However, several studies have shown that feedback devices are not sufficiently reliable to ensure adequate CC depth on soft surfaces. Here, we determined the proper target depth of feedback (TDF) using an accelerometer during cardiopulmonary resuscitation in hospital beds.

METHODS

In prospective randomized crossover study, 19 emergency physicians performed CCs for 2 minutes continuously on a manikin in 2 different beds with 3 TDFs (5, 6, and 7 cm). We measured CC depth, the proportion of accurate compression depths, CC rate, the proportion of incomplete chest decompressions, the velocity of CC (CC velocity), the proportion of time spent in CC relative to compression plus decompression (duty cycle), and the time spent in CC (CC time).

RESULTS

Mean (SD) CC depths at TDF 5, 6, and 7 were 45.42 (5.79), 52.68 (4.18), and 58.47 (2.48) on one bed and 46.26 (4.49), 53.58 (3.15), and 58.74 (2.10) mm on the other bed (all P<.001), respectively. The proportions of accurate compression depths and CC velocity at TDF 5, 6, and 7 differed significantly according to TDF on both beds (all P<.001).The CC rate, CC time, and proportion of incomplete chest decompression did not differ on both beds (all P>.05). The duty cycle differed significantly on only B2.

CONCLUSIONS

The target depth of the real-time feedback device should be at least 6 cm but should not exceed 7 cm for optimal CC on patients on hospital beds.