An Evidence-Based Serious Game App for Public Education on Antibiotic Use and Resistance: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The misuse and overuse of antibiotics accelerate the development of antimicrobial resistance (AMR). Serious games, any form of games that serve a greater purpose other than entertainment, could augment public education above ongoing health promotion efforts. Hence, we developed an evidence-based educational serious game app-SteWARdS Antibiotic Defence-to educate players on good antibiotic use practices and AMR through a game quest comprising 3 minigames and interaction with the nonplayer characters. OBJECTIVE: We aimed to evaluate the effectiveness of the SteWARdS Antibiotic Defence app in improving the knowledge of, attitude toward, and perceptions (KAP) of appropriate antibiotic use and AMR among the public in Singapore. METHODS: We conducted a 2-arm parallel randomized controlled trial, recruiting visitors aged 18-65 years from 2 polyclinics in Singapore. Intervention group participants had to download the SteWARdS Antibiotic Defence app (available only in English and on the Android platform) on their smartphones and complete the quest in the app. Participants took half a day to 2 weeks to complete the quest. The control group received no intervention. Knowledge questions on antibiotic use and AMR (11 binary questions) were self-administered at baseline, immediately after the intervention, and 6-10 weeks post intervention, while attitudes and perception questions (14 three-point Likert-scale questions) were self-administered at baseline and 6-10 weeks post intervention. We also collected participants' feedback on app usage. RESULTS: Participants (n=348; intervention: n=142, control: n=206) had a mean age of 36.9 years. Intervention group participants showed a statistically significant improvement in mean knowledge score (effect size: 0.58 [95% CI 0.28-0.87]) compared with controls after accounting for age, educational level, and exposure to advertisements on antibiotics and AMR. Intervention participants also showed a statistically significant improvement in mean attitude-perception scores (effect size: 0.98 (95% CI 0.44-1.52)) after adjusting for marital status and race. A majority of participants agreed that the "SteWARdS Antibiotic Defence" app improved their awareness on antibiotic use (135/142, 95.1%) and AMR (136/142, 95.8%). About 73.9% (105/142) of the participants agreed that the app is easy to use, 70.4% (100/142) agreed that the app was enjoyable, and 85.2% (121/142) would recommend the app to others. CONCLUSIONS: Our educational serious game app improves participants' KAP on appropriate antibiotic use and AMR. Public education apps should be engaging, educational, easy to use, and have an attractive user interface. Future research should assess the effectiveness of interventions in facilitating long-term knowledge retention and long-lasting behavioral change. TRIAL REGISTRATION: ClinicalTrials.gov NCT05445414; https://clinicaltrials.gov/ct2/show/NCT05445414. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/45833.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".