Likelihood of Leveraging Augmented Reality Technology to Promote HIV Prevention and Treatment Among Adolescent Girls and Young Women in Cameroon: Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Adolescent girls and young women (AGYW) in Sub-Saharan Africa (SSA) represent four out of every five newly diagnosed HIV among AGYW globally. Leveraging augmented reality (AR) technology for HIV prevention and treatment holds significant potential among young people. However, there is a knowledge gap regarding the acceptance of AR by AGYW in SSA. OBJECTIVE: This study aimed to assess the likelihood of AGYW in Cameroon using AR for HIV testing, prevention, and treatment. The study findings will lay the groundwork for developing AR-based interventions to prevent and treat HIV in Cameroon and beyond. METHODS: This was a cross-sectional survey conducted in Yaoundé, Cameroon, in which 637 AGYW were recruited using a combination of multistage cluster and snowball sampling techniques. We used an online survey to collect data on participants' knowledge, prior use of AR technology, and likelihood of using AR technology for HIV prevention and treatment, and associated factors. Multivariate ordinal regressions were used to analyze the factors associated with AGYW's likelihood of using AR to prevent HIV. RESULTS: The study showed that 84% (536) of AGYW had never heard of AR before this study, and only 8% (49) had prior experience using AR. Participants' median age was 22 years (IQR: 21-24 years), with the majority (56.8%, 362) aged between 21 and 25 years. Despite the low usage rate of AR among AGYW, there was a high likelihood of using AR to promote HIV prevention and treatment. Specifically, 72% of AGYW reported that they were likely to use AR to visualize the HIV transmission process, while 73% and 74% reported the likelihood of using AR to learn about pre-exposure prophylaxis (PrEP) and how HIV medication lowers HIV viral load, respectively. More importantly, 54% (342) and 50% (319) of AGYW reported that they were extremely likely to use AR to learn the correct way of using condom and self-testing for HIV, respectively. The high likelihood of using AR to prevent and treat HIV was associated with a higher education level (P=0.012), having ever tested for HIV (P=0.031), and a history of previously using health apps or searching for health information on their phones (P<0.001). CONCLUSIONS: The likelihood of using AR technology to promote HIV prevention and treatment is high among AGYW in Cameroon. Future research should focus on exploring the preferred features of AR-based digital health interventions and consider methods of implementing them in the context of Cameroon or SSA. CLINICALTRIAL: N/a.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".