| Literature DB >> 31406943 |
Steve Balian1, Shaun K McGovern1, Benjamin S Abella1, Audrey L Blewer2, Marion Leary1,3.
Abstract
AIM OF THE STUDY: Augmented reality (AR) has the potential to offer a novel approach to CPR training that supplements conventional training methods with gamification and a more interactive learning experience. This is done through computer-generated imagery superimposed on users' view of the real environment to simulate interactive training scenarios. We sought to test the feasibility of an AR CPR training system (CPReality) for health care providers (HCPs).Entities:
Keywords: Augmented reality; Cardiac arrest; Cardiology; Cardiopulmonary resuscitation; Computer science; Emergency medicine; Health profession; Simulation
Year: 2019 PMID: 31406943 PMCID: PMC6684477 DOI: 10.1016/j.heliyon.2019.e02205
Source DB: PubMed Journal: Heliyon ISSN: 2405-8440
Fig. 1The CPReality AR CPR training application: Microsoft HoloLens device integrated with a Laerdal CPR feedback manikin displaying the holographic circulatory system.
Baseline participant characteristics.
| Age (years), median (IQR) | 31 (27–41) |
| Gender (female) | 36 (70.6%) |
| Race (white) | 36 (70.6%) |
| Healthcare experience (years), median (IQR) | 5 (3–15) |
| Healthcare position | |
| Registered nurse | 34 (66.7%) |
| Physician | 8 (15.7%) |
| Advanced practice registered nurse | 2 (3.9%) |
| Technician | 2 (3.9%) |
| Pharmacy | 2 (3.9%) |
| Other | 3 (5.9%) |
| Resuscitations per month | |
| 0 | 20 (39.2%) |
| 1 | 18 (35.3%) |
| 2-3 | 10 (19.6%) |
| >4 | 3 (5.9%) |
| BLS last certified | |
| ≤1 month | 4 (7.8%) |
| 2–6 months | 14 (27.5%) |
| 7–12 months | 8 (15.7%) |
| 13–24 months | 20 (39.2%) |
| >24 months | 5 (9.8%) |
| ACLS last certified | |
| 2–6 months | 6 (11.8%) |
| 7–12 months | 3 (5.9%) |
| 13–24 months | 21 (41.2%) |
| >24 months | 4 (7.8%) |
| No Response | 5 (9.8%) |
| Not certified | 12 (23.5%) |
| Used AR device in the past (yes) | 2 (3.9%) |
IQR, interquartile range; BLS, basic life support; ACLS, advanced cardiac life support; AR, augmented reality.
Fig. 2Frequency histograms of participants' mean CC rate (cpm), mean CC depth (mm), and percent of CC with complete chest recoil (%) after 2 min of CPR.
Hands-only CPR performance parameters during AR resuscitation.
| CC rate (cpm), mean ± SD | 126 ± 12.9 |
| CC depth (mm), median (IQR) | 53 (46–58) |
| CC with complete recoil (%), median (IQR) | 80 (12–100) |
CC, chest compression; cpm, compressions per minute; SD, standard deviation; mm, millimeters, IQR, interquartile range. When the normality assumption was violated, variables were reported as medians (interquartile range) instead of means ±standard deviation.
Fig. 3Likert scale participant evaluation of the CPReality system.
Open-ended participant feedback about the CPReality application categorized thematically, N = 51.
| Number of responses | ||
|---|---|---|
| Please tell us what you liked about the augmented reality CPR training application? | Real-time audiovisual feedback: Allowed appropriate adjustment of CC in real-time Helped deliver effective CC | 35 |
| Blood flow visualization: Made experience more realistic Helped visualize the goal of CC Helped understand the effect of compressions on perfusing vital organs | 24 | |
| Use of technology | 3 | |
| Performance scorecard at the end | 1 | |
| Nothing | 1 | |
| No response | 7 | |
| What would you change about the augmented reality CPR training application? | Position the blood circulation hologram on top of instead of next to the manikin | 12 |
| Provide users with real-time stats during CC | 9 | |
| Sound effect quality or volume | 4 | |
| Headset weight and stability | 2 | |
| Subject reported dizziness | 1 | |
| Nothing | 8 | |
| No response | 15 |
CPR, cardiopulmonary resuscitation; CC, chest compression.
Some comments fall under more than one category.