Smartphone-Based Simulation Training Improves ACLS Algorithmic Decision-Making and ECG Recognition in Emergency Medicine Residents: A Pilot Randomized Controlled Trial

Background/Objective: Advanced Cardiac Life Support (ACLS) training is a cornerstone of emergency medicine residency programs. Timely and effective application of ACLS protocols can be lifesaving. This study aims to evaluate the effectiveness of simulation-based smartphone applications in enhancing ACLS practice compared to traditional educational methods. Methods: This single-center, pilot randomized controlled trial enrolled emergency medicine residents from a tertiary teaching hospital who had previously completed standard ACLS training. Participants were randomized to receive either traditional manikin-based training (STD group, n = 17) or smartphone-based simulation training using the “ECG Rhythms and ACLS Cases” application (SIM group, n = 17). All participants completed 12 standardized ACLS scenarios and 20 electrocardiogram (ECG) rhythm recognition tests immediately following training. Performance was assessed using standardized checklists based on current American Heart Association (AHA) guidelines (scored automatically by the application in the SIM group and by a single unblinded observer in the STD group). Since each scenario had different maximum achievable scores (ranging from 1200 to 4200 points), performance was standardized by calculating an overall success rate as the percentage of total earned points to total possible points across all scenarios. Results: All 34 enrolled residents (17 per group) completed the study. The SIM group achieved significantly higher overall ACLS success rates compared to the STD group (89.95 ± 5.26% vs. 79.23 ± 7.63%, p < 0.001). Similarly, ECG rhythm recognition accuracy was superior in the SIM group (82.06 ± 6.14% vs. 72.06 ± 7.08%). Descriptively, the SIM group showed numerically smaller standard deviations across scenarios compared to the STD group. Conclusions: Smartphone-based simulation training was associated with higher ACLS algorithmic decision-making scores and ECG rhythm recognition accuracy than traditional manikin-based training, likely through provision of standardized algorithmic guidance, immediate feedback, and consistent educational content. Because the outcome captured cognitive and decision-making components rather than psychomotor skills, and because the assessment method differed between groups, these findings support the application as an adjunct to, rather than a replacement for, hands-on manikin training.