Utility of mobile learning in Electrocardiography

Abstract

Aims: Mobile learning is attributed to the acquisition of knowledge derived from accessing information on a mobile device. Although increasingly implemented in medical education, research on its utility in Electrocardiography remains sparse. In this study, we explored the effect of mobile learning on the accuracy of electrocardiogram (ECG) analysis and interpretation.

Methods and results: The study comprised 181 participants (77 fourth- and 69 sixth-year medical students, and 35 residents). Participants were randomized to analyse ECGs with a mobile learning strategy [either searching the Internet or using an ECG reference application (app)] or not. For each ECG, they provided their initial diagnosis, key supporting features, and final diagnosis consecutively. Two weeks later, they analysed the same ECGs, without access to any mobile device. ECG interpretation was more accurate when participants used the ECG app (56%), as compared to searching the Internet (50.3%) or neither (43.5%, P = 0.001). Importantly, mobile learning supported participants in revising their initial incorrect ECG diagnosis (ECG app 18.7%, Internet search 13.6%, no mobile device 8.4%, P < 0.001). However, whilst this was true for students, there was no significant difference amongst residents. Internet searches were only useful if participants identified the correct ECG features. The app was beneficial when participants searched by ECG features, but not by diagnosis. Using the ECG reference app required less time than searching the Internet (7:44 ± 4:13 vs. 9:14 ± 4:34, P < 0.001). Mobile learning gains were not sustained after 2 weeks.

Conclusion: Whilst mobile learning contributes to increased ECG diagnostic accuracy, the benefits were not sustained over time.

Keywords: App; ECG; Electrocardiography; Internet; Medical education; Mobile learning.

Graphical Abstract

Figure 1

Study flow.

Figure 2

The content in this app is organized by features and by diagnoses. The section on Rhythm Analysis (A) provides approaches and differential diagnoses for different rate and rhythm abnormalities, whereas the section on Waveform Analysis (B) provides the normal parameters, as well as the differential diagnoses for abnormal waveforms. The section on ECG interpretation (C) provides a list of ECG diagnoses, where the key ECG features are given for each diagnosis.

Figure 3

Participants were asked to provide an initial diagnosis (spot diagnosis) for each ECG. Once the answer was submitted, participants were asked to provide the key features on the ECG that supported the diagnosis. They could then proceed to providing their final diagnosis. Analysis and interpretation of the first three ECGs occurred without access to any mobile device (A). For the subsequent sets of three ECGs, participants could search the Internet freely (B) or access the ECG reference app (C) respectively from their mobile devices, during ECG analysis and interpretation.

Figure 4

Accuracy of final ECG interpretation, after having had access to no mobile device, searching the Internet or accessing the ECG reference app. The results of Test 1 are shown for junior and senior medical students, medical residents and all participants. Two weeks later, during Test 2, the same ECGs were analysed without access to a mobile device. The results shown for junior and senior medical students, medical residents and all participants, and are categorized according to whether the ECGs were analysed 2 weeks prior without access to a mobile device, or having access to a mobile device and able to search the Internet or access the ECG reference app. Data were expressed as proportions and compared using the Chi-square test. Significant differences between subgroups are indicated as follows: ***P < 0.001, **P < 0.01, and *P < 0.05.

Figure 5

The proportion of ECGs for which the participants gave a correct or incorrect initial diagnosis and subsequently changed to a correct or incorrect final diagnosis, or not. The ECGs are categorized as to whether the participants had no mobile device, could search the Internet, or access the ECG app. Whilst junior (A) and senior medical students (B) were influenced by searching the Internet or accessing the ECG app when changing an incorrect initial diagnosis to a correct final diagnosis, this was not true for medical residents (C). Overall (D), the greatest gains were present with ECGs for which participants had access to the ECG reference app.

Figure 6

There was an association between searching the correct ECG features or diagnosis on the web and correcting an initial incorrect ECG diagnosis. When using the ECG reference app, searching by ECG diagnosis was not associated with changing an incorrect initial diagnosis, whereas searching by features was.