Efficacy of a Mobile Serious Game (SwaziYolo) for Increasing HIV Risk Perception: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Eswatini has one of the highest HIV prevalence rates worldwide (24.8% among people aged ≥15 years), with unprotected heterosexual transmission accounting for more than 90% of new HIV infections in the country. Low HIV risk perception is known to influence risk behavior. Mobile phone technology is growing rapidly, offering opportunities for technology-driven interventions to improve HIV risk perception and prevention. OBJECTIVE: We aimed to design and test a serious game to increase HIV risk perception and intention to engage in protective HIV behaviors among young people in Eswatini. METHODS: Our team developed SwaziYolo, a smartphone-based, interactive, educational story game that places the player in the role of a young adult looking for love in Eswatini's capital city. We conducted the Serious Games HIV Prevention Trial (SGPrev-Trial), a 4-week, 2-arm, unblinded, 1:1 randomized controlled trial of SwaziYolo among people aged between 18 and 29 years in Eswatini. The primary outcome was HIV risk perception using a 10-item and subset 8-item Perceived Risk of HIV Infection Scale (PRHS). We used modified intention-to-treat and per-protocol difference-in-difference (DID) estimation to compare the change between groups in the mean PHRS scores before and after intervention. RESULTS: Of the 380 participants in the study, 122 (64.2%) in the control group and 119 (62.6%) in the intervention group completed the follow-up, and 95 (79.8%) played the game. In the modified intention-to-treat analyses, no significant differences between groups were observed for the 8-item PRHS (DID: mean 1.1, SD 0.72; P=.13) and the 10-item PRHS (DID: mean 1.3, SD 0.80; P=.12) scores. In the per-protocol analyses, HIV risk perception increased significantly among participants who played the game (8-item DID: mean 1.6, SD 0.74; P=.04 and 10-item DID: mean 1.8, SD 0.83; P=.03). Nearly all (94/95, 98.9%) participants strongly agreed or agreed that they would recommend SwaziYolo to their peers. CONCLUSIONS: SwaziYolo was acceptable and increased HIV risk perception among young people in Eswatini who self-selected to play the game. More research is needed to improve and evaluate the SwaziYolo intervention. TRIAL REGISTRATION: UMIN Clinical Trial Registry UMIN000021781; https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000025103.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".