Dataset Information

Real-Time Chest Compression Quality Measurements by Smartphone Camera.

ABSTRACT: 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.

SUBMITTER: Meinich-Bache O

PROVIDER: S-EPMC6277120 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Real-Time Chest Compression Quality Measurements by Smartphone Camera.

Meinich-Bache Øyvind Ø Engan Kjersti K Birkenes Tonje Søraas TS Myklebust Helge H

Journal of healthcare engineering 20181028

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 ...[more]

PMID: 30581549

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Project description:BACKGROUND:Current guidelines underline the importance of high-quality chest compression during cardiopulmonary resuscitation (CPR), to improve outcomes. Contrary to this many studies show that chest compression is often carried out poorly in clinical practice, and long interruptions in compression are observed. This prospective cohort study aimed to analyse whether chest compression quality changes when a real-time feedback system is used to provide simultaneous audiovisual feedback on chest compression quality. For this purpose, pauses in compression, compression frequency and compression depth were compared. METHODS:The study included 292 out-of-hospital cardiac arrests in three consecutive study groups: first group, conventional resuscitation (no-sensor CPR); second group, using a feedback sensor to collect compression depth data without real-time feedback (sensor-only CPR); and third group, with real-time feedback on compression quality (sensor-feedback CPR). Pauses and frequency were analysed using compression artefacts on electrocardiography, and compression depth was measured using the feedback sensor. With this data, various parameters were determined in order to be able to compare the chest compression quality between the three consecutive groups. RESULTS:The compression fraction increased with sensor-only CPR (group 2) in comparison with no-sensor CPR (group 1) (80.1% vs. 87.49%; P < 0.001), but there were no further differences belonging compression fraction after activation of sensor-feedback CPR (group 3) (P = 1.00). Compression frequency declined over the three study groups, reaching the guideline recommendations (127.81 comp/min vs. 122.96 comp/min, P = 0.02 vs. 119.15 comp/min, P = 0.008) after activation of sensor-feedback CPR (group 3). Mean compression depth only changed minimally with sensor-feedback (52.49 mm vs. 54.66 mm; P = 0.16), but the fraction of compressions with sufficient depth (at least 5 cm) and compressions within the recommended 5-6 cm increased significantly with sensor-feedback CPR (56.90% vs. 71.03%; P = 0.003 and 28.74% vs. 43.97%; P < 0.001). CONCLUSIONS:The real-time feedback system improved chest compression quality regarding pauses in compression and compression frequency and facilitated compliance with the guideline recommendations. Compression depth did not change significantly after activation of the real-time feedback. Even the sole use of a CPR-feedback-sensor ("sensor-only CPR") improved performance regarding pauses in compression and compression frequency, a phenomenon known as the 'Hawthorne effect'. Based on this data real-time feedback systems can be expected to raise the quality level in some parts of chest compression quality. TRIAL REGISTRATION:International Clinical Trials Registry Platform of the World Health Organisation and German Register of Clinical Trials (DRKS00009903), Registered 09 February 2016 (retrospectively registered).

Project description:BACKGROUND:With a survival rate of 6 to 11%, out-of-hospital cardiac arrest (OHCA) remains a healthcare challenge with room for improvement in morbidity and mortality. The guidelines emphasize the highest possible quality of cardiopulmonary resuscitation (CPR) and chest compressions (CC). It is essential to minimize CC interruptions, and therefore increase the chest compression fraction (CCF), as this is an independent factor for survival. Survival is significantly and positively correlated with the suitability of CCF targets, CC frequency, CC depth, and brief predefibrillation pause. CC guidance improves adherence to recommendations and allows closer alignment with the CC objectives. The possibility of improving CCF by lengthening the time between two CC relays and the effect of real-time feedback on the quality of the CC must be investigated. METHODS:Using a 2?×?2 factorial design in a multicenter randomized trial, two hypotheses will be tested simultaneously: (i) a 4-min relay rhythm improves the CCF (reducing the no-flow time) compared to the currently recommended 2-min relay rate, and (ii) a guiding tool improves the quality of CC. Primary outcomes (i) CCF and (ii) correct compression score will be recorded by a real-time feedback device. Five hundred adult nontraumatic OHCAs will be included over 2?years. Patients will be randomized in a 1:1:1:1 distribution receiving advanced CPR as follows: 2-min blind, 2?min with guidance, 4-min blind, or 4?min with guidance. Secondary outcomes are the depth, frequency, and release of CC; length (care, no-flow, and low-flow); rate of return of spontaneous circulation; characteristics of advanced CPR; survival at hospital admission; survival and neurological state on days 1 and 30 (or intensive care discharge); and dosage of neuron-specific enolase on days 1 and 3. DISCUSSION:This study will contribute to assessing the impact of real-time feedback on CC quality in practical conditions of OHCA resuscitation. It will also provide insight into the feasibility of extending the relay rhythm between two rescuers from the currently recommended 2 to 4?min. TRIAL REGISTRATION:ClinicalTrials.gov, NCT03817892 . Registered on 28 January 2019.

Project description:BACKGROUND:Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality. AIMS:The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep. METHODS:We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (? 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. RESULTS:Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency. CONCLUSIONS:These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.

Dataset Information

Real-Time Chest Compression Quality Measurements by Smartphone Camera.

Publications

Real-Time Chest Compression Quality Measurements by Smartphone Camera.

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