Creating a Theoretically Grounded Gaming App to Increase Adherence to Pre-Exposure Prophylaxis: Lessons From the Development of the Viral Combat Mobile Phone Game
Bibliographic record
Abstract
BACKGROUND: In the United States, young minority men who have sex with men (MSM) are most likely to become infected with HIV. The use of antiretroviral medications to reduce the risk of acquiring HIV infection (pre-exposure prophylaxis, PrEP) is an efficacious and promising prevention strategy. There have been significant advances regarding PrEP, including the definitive demonstration that PrEP reduces HIV acquisition and the development of clinical prescribing guidelines. Despite these promising events, the practical implementation of PrEP can be challenging. Data show that PrEP's safety and effectiveness could be greatly compromised by suboptimal adherence to treatment, and there is concern about the potential for an increase in HIV risk behavior among PrEP users. Due to these challenges, the prescribing of PrEP should be accompanied by behavioral interventions to promote adherence. OBJECTIVE: This study aimed to develop an immersive, action-oriented iPhone gaming intervention to improve motivation for adherence to PrEP. METHODS: Game development was guided by social learning theory, taking into consideration the perspectives of young adult MSM who are taking PrEP. A total of 20 young men who have sex with men (YMSM; aged 18-35 years) were recruited from a sexually transmitted infection (STI), HIV testing, and PrEP care clinic in Jackson, Mississippi, between October 2016 and June 2017. They participated in qualitative interviews guided by the information-motivation-behavioral skills (IMB) model of behavior change. The mean age of participants was 26 years, and all the participants identified as male. Acceptability of the game was assessed with the Client Service Questionnaire and session evaluation form. RESULTS: A number of themes emerged that informed game development. YMSM taking PrEP desired informational game content that included new and comprehensive details about the effectiveness of PrEP, details about PrEP as it relates to doctors' visits, and general information about STIs other than HIV. Motivational themes that emerged were the desire for enhancement of future orientation; reinforcement of positive influences from partners, parents, and friends; collaboration with health care providers; decreasing stigma; and a focus on personal relevance of PrEP-related medical care. Behavioral skills themes centered around self-efficacy and strategies for adherence to PrEP and self-care. CONCLUSIONS: We utilized youth feedback, IMB, and agile software development to create a multilevel, immersive, action-oriented iPhone gaming intervention to improve motivation for adherence to PrEP. There is a dearth of gaming interventions for persons on PrEP. This study is a significant step in working toward the development and testing of an iPhone gaming intervention to decrease HIV risk and promote adherence to PrEP for YMSM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".