Background

Gamification is the strategic integration of game design principles into non-game environments, a term first coined by Brett Terrill in 2008 and gaining widespread recognition around 2010. While extensively embraced in marketing, economics, research, and healthcare, its implementation in education has been relatively gradual (1).

Nonetheless, with the advancements in technology-enhanced learning (TEL) and the use of digital platforms, such as social networks, digital media, and the Internet, a diverse range of electronic educational games, virtual patient simulations, and online gamified quizzes and puzzles have increased, with varying degrees of success in recent years (2).

The electrocardiogram (ECG) is a pivotal diagnostic tool in contemporary healthcare practices (3). Proficiency in ECG interpretation is a paramount requirement for medical professionals, serving as a foundational element in the training framework for medical students during their clinical rotations. Recent literature reviews have highlighted significant deficiencies in electrocardiogram (ECG) interpretation skills among interns, residents, and attending physicians across various medical specialties, including internal medicine, family practice, and emergency medicine. These inadequacies could adversely affect patient care outcomes. Additionally, there is a pressing need for further research to explore strategies aimed at enhancing ECG interpretation skills (4, 5).

The integration of gamification into medical education has attracted considerable interest from educators, resulting in increased research within this domain due to its potential to enhance motivation and engagement among medical students (6, 7). Furthermore, a recent landscape review study authored by McCoy, Lewis, and Dalton delved into gamified learning platforms in medical education, unearthing that game-based learning integrated into medical curricula has amplified student engagement, promoted collaborative learning, and improved decision-making (8).

This evidence has prompted the application of gamification principles in the context of ECG education. However, the implementation of gamification in ECG training remains nascent in the medical field, likely due to the limited availability of platforms in this discipline. Consequently, there is a compelling need for meticulously designed research to evaluate the effects of gamification on educational outcomes in ECG training.

Objectives

The primary aim of this study was to assess the educational outcomes derived from ECG-focused game sessions compared to traditional teaching methods among a cohort of medical students.

 Methods

A comparative study was undertaken to evaluate the effectiveness of game-based ECG learning (G-ECG) when compared to conventional ECG learning (C-ECG) among second-year medical students during the academic year 2023-2024 at the Faculty of Allied Health Sciences (AHS), Dr. M.G.R Educational and Research Institute, located in Chennai, Tamil Nadu, India. The participants were selected utilizing the census method from the second-year medical students (n = 120) enrolled at the above-mentioned private medical college. A total of 120 students participated in the study, with 60 students allocated to the experimental group (game-based ECG learning) and the remaining 60 assigned to the control group (conventional ECG learning) through a stratified random assignment process.

The allocation of learning methods (G-ECG versus C-ECG) to students with varying levels of academic proficiency was determined through a random process, such as a coin toss. Both groups underwent a 30-minute lecture on fundamental ECG concepts provided by certified ECG professionals.

The Six-Second ECG (SS-ECG) is a web-based simulation tool designed to facilitate the learning of twenty-seven familiar cardiac rhythms. Through its "explore-review" feature, users can access detailed information on each rhythm by simply clicking on its name. The "explore-review-play" function also enables users to diagnose ECG patterns by customizing the game settings, including the time period, dynamic or static rhythm display, sound volume, and grid display (9).

The experimental cohort engaged in learning via the Six-Second ECG (SS-ECG) web-based game offered by SKILLSTAT. The G-ECG group engaged with a game application for one hour to gain proficiency in recognizing 27 ECG rhythms. In contrast, the C-ECG group received a one-hour training session from certified ECG professionals to develop competency in identifying 27 ECG rhythms. The 27 different types of ECG rhythms were categorized into 5 sections: Sinus Arrhythmias, Atrial Arrhythmias, Heart Blocks, Junctional Arrhythmias, and Ventricular Arrhythmias. Sinus Arrhythmias include 6 types of ECG patterns: Sinus Rhythm, Sinus Bradycardia, Sinus Tachycardia, Sinus Arrhythmia, Sinus Exit Block, and Sinus Arrest. Atrial Arrhythmias consist of 5 types of ECG patterns: NSR with PAC, SVT, Atrial Fibrillation, Atrial Flutter, and Paced Atrial.

Heart Blocks comprise 5 types of ECG patterns: NSR with 1° AVB, 2° AVB Type I, 2° AVB Type II, 2° AVB 2:1, and 3° AV Block. Junctional Arrhythmias include 5 types of ECG patterns: NSR with PJC, Junctional Rhythm, Accelerated Junctional, Junctional Tachycardia, and Wandering Pacemaker. Ventricular Arrhythmias consist of 6 types of ECG patterns: NSR with PVC, Idioventricular, Accelerated IVR, Ventricular Tachycardia, Ventricular Fibrillation, and Paced Ventricular.

In the game-based learning group, platforms were secured with password protection and administered to prevent contamination bias in the study's outcome. Participants were explicitly instructed not to use alternative platforms during the experimental period. Both the G-ECG and C-ECG groups utilized identical learning materials. The audio-visual department verified that all ECG images used in the study were of high quality. The effectiveness of both groups was evaluated through an online test, with scoring and response times for each question compared using statistical analysis. The Institutional Ethical Committee (IEC/ACSMCH) approved the study under reference No. 957/IEC/ACSMCH.

Statistical analysis: The research data were obtained using a pre-tested form and subsequently entered into SPSS 20 (SPSS Inc., Chicago, USA). Descriptive statistics for quantitative variables were expressed as the mean and standard deviation. The chi-square test was used to analyze the association between the knowledge and the two groups. The Independent t-test was employed to assess time consumption differences between groups for continuous variables. In this study, a significance level of 0.05 was considered for statistical tests.

Results

In a study involving 120 second-year students, 41 (34.1%) were male, and 79 (65.8%) were female. Among them, 60 participated in conventional learning, and 60 were part of the game-based learning group. Of the participants, 24 (40%) and 17 (28.3%) were male, while 36 (60%) and 43 (71.7%) were female in the conventional and game-based learning groups, respectively.

The mean (SD) age was 19.23 ± 0.50 years in the conventional learning group and 19.15 ± 0.60 years in the game-based learning group. No statistically significant differences in age (P=0.412), gender (P=0.241), academic year (P=0.312), or distribution were observed between the two groups. All participants from both groups reported no prior experience in gamified learning.

The efficacy of Game-Based ECG Learning (G-ECG) vs conventional ECG Learning (T-ECG) based on score attainment and time consumption: The comparison of the Game-Based ECG learning method with the conventional ECG learning approach revealed no significant difference in the time consumed by both groups (P>0.05) across various aspects, including Sinus Arrhythmias, Atrial Arrhythmias, Heart Blocks, Junctional Arrhythmias, and Ventricular Arrhythmias, within the two cohorts (Table 1).

Table 1. The efficacy of Game-Based ECG Learning (G-ECG) vs traditional ECG Learning (T-ECG) based on time consumption

Out of 27 ECG rhythms evaluated, 7 ECG rhythms exhibited a significant association between the two groups, evidenced by a p-value <0.001 (Table 2). Participants utilizing the game-based ECG learning method achieved a sufficient knowledge attainment level of 61.7% (33 cases) compared to 33.3%. (20 cases) in the conventional ECG learning method (P<0.001). This finding suggests that the G-ECG Method resulted in a markedly higher level of knowledge attainment than the conventional ECG learning approach.

Table 2. The efficacy of Game-Based ECG Learning (G-ECG) vs traditional ECG Learning (T-ECG)

The efficacy of conventional ECG Learning (T-ECG) based on Gender: In the context of our analysis of conventional-based ECG learning, we assessed the knowledge levels of male and female participants. Out of 27 ECG rhythms evaluated, 4 ECG rhythms exhibited a statistically significant association between the two gender groups, as indicated by a p-value<0.001 (Table 3).

Table 3. The efficacy of Traditional ECG Learning (T-ECG) based on Gender

Female participants who engaged in the conventional-based ECG learning method attained a knowledge level of 58.3% (21 cases), unlike male participants, who achieved only 16.7% (4 cases), with a p-value of <0.001. These findings suggest that female participants demonstrated a considerably higher level of knowledge attainment than their male counterparts.

The female participants exhibited a longer duration of time consumption compared to the male participants with statistically significant p-values (P<0.001 for all) observed across various parameters, including Sinus Arrhythmias, Atrial Arrhythmias, Heart Blocks, Junctional Arrhythmias, and Ventricular Arrhythmias (Table 4).

Table 4. The efficacy of traditional ECG Learning (T-ECG) based on time consumption

The efficacy of Game-Based ECG Learning (G-ECG) based on Gender: In the context of our analysis of game-based ECG learning, we evaluated the knowledge levels of male and female participants. Among the 27 ECG rhythms assessed, 3 ECG rhythms demonstrated a statistically significant association between the two gender groups (P<0.001) (Table 5). 58.1% of female participants who engaged in the game-based ECG learning method (25 cases) achieved a sufficient knowledge level in contrast to 47.1% of male participants (8 cases), with a p-value of <0.001. These findings indicate that female participants exhibited a markedly higher level of knowledge acquisition than their male counterparts.

Table 5. The efficacy of Game-Based ECG Learning (G-ECG) based on Gender

The female participant exhibited a longer duration of time consumption compared to the male participant, with statistically significant p-values (<0.001) observed across various parameters, including Sinus Arrhythmias, Atrial Arrhythmias, Heart Blocks, Junctional Arrhythmias, and Ventricular Arrhythmias (Table 6).

Table 6. The efficacy of Game-Based ECG Learning (G-ECG) based on time consumption

Discussion

Prior research has highlighted the insufficient Skills in 12-lead ECG interpretation skills among medical students, emphasizing the need for innovative pedagogical approaches to enhance ECG competency (10-12).

In today's educational landscape, online ECG learning has gained traction as a supplementary instructional method, offering the flexibility often lacking in conventional classroom settings. However, the engagement and enthusiasm of students in online learning courses remain inadequate. In response to this educational challenge, we utilized a web-based gamified ECG learning platform (SS-ECG, SKILLSTAT) as a gamified online active learning approach to enhance student engagement through a competitive and interactive learning environment (10).

The present study demonstrated that Game-Based ECG Learning (G-ECG) significantly improved ECG diagnostic accuracy and interpretation skills. The results indicate that game-based learning is more effective than conventional ECG learning in enhancing factual knowledge among undergraduate medical students. This is likely due to the collaborative and competitive elements of gamified learning, which create a stimulating learning environment and actively engage students in the learning process.

According to a review article focused on computer programs designed to facilitate the teaching of ECG interpretation, there was no evidence in the literature concerning the existence of an application or webpage that incorporates gamification for this purpose before the completion of the data analysis presented in this study. Although no studies were identified that explicitly utilize gamification technologies, it is important to highlight several related studies that have explored the application of alternative technologies for teaching ECG interpretation (13, 14).

Determining the most effective method for teaching ECG interpretation remains inconclusive, as indicated by Fent et al. (15). They found that integrating multiple instructional techniques is essential for optimal learning outcomes.

In a comparative study conducted by Kopeć, which assessed two e-learning approaches for ECG interpretation among medical students, it was similarly concluded that collaborative e-learning proved more effective than self-directed e-learning (16).

Furthermore, Nilsson suggested that enhancing traditional ECG instruction with online learning modalities could significantly bolster ECG comprehension among medical students (17). Montassier et al. (18) conducted a research study to assess the efficacy of an e-learning strategy for interpreting ECG through a randomized controlled trial.

The findings demonstrated that this approach significantly enhanced the acquisition of ECG interpretation skills among medical students. This study distinguishes itself from similar investigations by developing and evaluating an application augmented by artificial intelligence to foster a personalized pedagogical approach. Furthermore, the application incorporates gamification elements to bolster student engagement, thereby offering valuable contributions to the existing body of literature.

The incorporation of gamification elements can encompass reward strategies designed to enhance student motivation in the learning of ECG interpretation. Further research is essential to assess students' acceptance of and progress in ECG learning through gamification.

The incorporation of gamification This study aims to propose gamified strategies designed to enhance student engagement and to compare these gamified methods with alternative educational approaches. Moreover, the present study's evidence suggests that game-based learning is comparable to traditional methods in improving students' abilities to interpret ECGs. It is anticipated that this academic inquiry will contribute to future research initiatives and ultimately support the development of innovative tools for ECG instruction.

Conclusion

We have demonstrated that using games as an instructional method is equally effective as conventional ECG teaching, offering a more interactive and learner-centered experience. This approach is promising for students who thrive in relaxed, learner-focused environments. Furthermore, the level of knowledge attained through the game-based approach surpassed that of conventional ECG teaching, with both methods requiring a similar amount of time.