Introduction:
The AHA trains over 20 million people in CPR skills annually. Virtual Reality (VR) education may improve student engagement, but the efficacy of VR-CPR programs has not been adequately studied. We performed the largest study to-date evaluating the feasibility of VR-enhanced CPR training.
Hypothesis:
We hypothesized that an immersive, VR-enhanced CPR training program, digitally linked to a physical manikin, can teach middle/high-school students Hands-Only CPR skills resulting in strong learner engagement.
Methods:
We linked a VR-enhanced CPR training program to a standard manikin with an optical “time of flight” sensor and hand-tracking gloves. Based on AHA’s Hands-Only CPR protocol, training included compression rate (100-120/min), depth (2-2.4”), hand position, and full chest recoil, with 124 students from 3 middle/high-schools in Florida (n= 38) and California (n=42, n=44). Iterations to streamline the user experience were implemented for Pilots 2 and 3.
Results:
VR-enhanced CPR training significantly improved compression rate, depth, and hand position; chest recoil was not statistically changed. Post-training, students were tested on CPR knowledge: 78% (96/124) were correct on nature of cardiac arrest, 88% (109/124) on compression rate, and 83% (103/124) on compression depth. Of students with prior CPR training, 79% stated VR-CPR training was more effective than prior training. Iterations in Pilots 2 and 3 were associated with additional learning gains at those sites.
Conclusions:
In this first-of-its-kind study, middle/high school students learned Hands-Only CPR skills and knowledge with a VR-enhanced CPR training program digitally linked to a physical manikin. Our VR-CPR training uniquely separates each of 4 Hands-Only CPR skills into separate training modules in which students receive instant feedback. Future study and development of VR-CPR is warranted, especially as learning gains were associated with user experience iterations.
Establishment of 3D Virtual Reality Model for Underground Pipeline Combining Hand Position and Shape Tracking Algorithm
