An Evidence-Based Serious Game App for Public Education on Antibiotic Use and Antimicrobial Resistance: Protocol of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The misuse and overuse of antibiotics contribute to the acceleration of antimicrobial resistance (AMR), but public knowledge on appropriate antibiotic use and AMR remained low despite ongoing health promotion efforts. App gamification has gained traction in recent years for health promotion and to affect change in health behaviors. Hence, we developed an evidence-based serious game app "SteWARdS Antibiotic Defence" to educate the public on appropriate antibiotic use and AMR and address knowledge gaps. OBJECTIVE: We aim to evaluate the effectiveness of the "SteWARdS Antibiotic Defence" app in improving the knowledge of, attitude toward, and perception (KAP) of appropriate antibiotic use and AMR among the public. The primary objective is to assess the changes in KAP of antibiotic use and AMR in our participants, while the secondary objectives are to assess the extent of user engagement with the app and the level of user satisfaction in using the app. METHODS: Our study is a parallel 2-armed randomized controlled trial with a 1:1 allocation. We plan to recruit 400 participants (patients or their caregivers) aged 18-65 years from government-funded primary care clinics in Singapore. Participants are randomized in blocks of 4 and into the intervention or control group. Participants in the intervention group are required to download the "SteWARdS Antibiotic Defence" app on their smartphones and complete a game quest within 2 weeks. Users will learn about appropriate antibiotic use and effective methods to recover from uncomplicated upper respiratory tract infections by interacting with the nonplayer characters and playing 3 minigames in the app. The control group will not receive any intervention. RESULTS: The primary study outcome is the change in participants' KAP toward antibiotic use and AMR 6-10 weeks post intervention or 6-10 weeks from baseline for the control group (web-based survey). We will also assess the knowledge level of participants immediately after the participant completes the game quest (in the app). The secondary study outcomes are the user engagement level (tracked by the app) and satisfaction level of playing the game (via the immediate postgame survey). The satisfaction survey will also collect participants' feedback on the game app. CONCLUSIONS: Our proposed study provides a unique opportunity to assess the effectiveness of a serious game app in public health education. We anticipate possible ceiling effects and selection bias in our study and have planned to perform subgroup analyses to adjust for confounding factors. The app intervention will benefit a larger population if it is proven to be effective and acceptable to users. TRIAL REGISTRATION: ClinicalTrials.gov NCT05445414; https://clinicaltrials.gov/ct2/show/NCT05445414. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45833.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".