Evaluating Quality, Usability, Evidence-Based Content, and Gamification Features in Mobile Learning Apps Designed to Teach Children Basic Life Support: Systematic Search in App Stores and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Globally, 3.7 million people die of sudden cardiac death annually. Following the World Health Organization endorsement of the Kids Save Lives statements, initiatives to train school-age children in basic life support (BLS) have been widespread. Mobile phone apps, combined with gamification, represent an opportunity for including mobile learning (m-learning) in teaching schoolchildren BLS as an additional teaching method; however, the quality of these apps is questionable. OBJECTIVE: This study aims to systematically evaluate the quality, usability, evidence-based content, and gamification features (GFs) of commercially available m-learning apps for teaching guideline-directed BLS knowledge and skills to school-aged children. METHODS: We searched the Google Play Store and Apple iOS App Store using multiple terms (eg, cardiopulmonary resuscitation [CPR] or BLS). Apps meeting the inclusion criteria were evaluated by 15 emergency health care professionals using the user version of the Mobile Application Rating Scale and System Usability Scale. We modified a five-finger mnemonic for teaching schoolchildren BLS and reviewed the apps' BLS content using standardized criteria based on three CPR guidelines. GFs in the apps were evaluated using a gamification taxonomy. RESULTS: Of the 1207 potentially relevant apps, only 6 (0.49%) met the inclusion criteria. Most apps were excluded because the content was not related to teaching schoolchildren BLS. The mean total scores for the user version of the Mobile Application Rating Scale and System Usability Scale score were 3.2/5 points (95% CI 3.0-3.4) and 47.1/100 points (95% CI 42.1-52.1), respectively. Half of the apps taught hands-only CPR, whereas the other half also included ventilation. All the apps indicated when to start chest compressions, and only 1 app taught BLS using an automated external defibrillator. Gamification was well integrated into the m-learning apps for teaching schoolchildren BLS, whereas the personal and fictional, educational, and performance gamification groups represented most GFs. CONCLUSIONS: Improving the quality and usability of BLS content in apps and combining them with GFs can offer educators novel m-learning tools to teach schoolchildren BLS skills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".