Digital Gaming to Improve Adherence Among Adolescents and Young Adults Living With HIV: Mixed-Methods Study to Test Feasibility and Acceptability
Bibliographic record
Abstract
BACKGROUND: An estimated 50% of adolescents and young adults (AYA) living with HIV are failing to adhere to prescribed antiretroviral treatment (ART). Digital games are effective in chronic disease management; however, research on gaming to improve ART adherence among AYA is limited. OBJECTIVE: We assessed the feasibility and acceptability of video gaming to improve AYA ART adherence. METHODS: Focus group discussions and surveys were administered to health care providers and AYA aged 13 to 24 years living with HIV at a pediatric HIV program in Washington, DC. During focus group discussions, AYA viewed demonstrations of 3 game prototypes linked to portable Wisepill medication dispensers. Content analysis strategies and thematic coding were used to identify adherence themes and gaming acceptance and feasibility. Likert scale and descriptive statistics were used to summarize response frequencies. RESULTS: Providers (n=10) identified common adherence barriers and strategies, including use of gaming analogies to improve AYA ART adherence. Providers supported exploration of digital gaming as an adherence intervention. In 6 focus group discussions, 12 AYA participants identified disclosure of HIV status and irregular daily schedules as major barriers to ART and use of alarms and pillboxes as reminders. Most AYA were very or somewhat likely to use the demonstrated game prototypes to help with ART adherence and desired challenging, individually tailored, user-friendly games with in-game incentives. Game prototypes were modified accordingly. CONCLUSIONS: AYA and their providers supported the use of digital games for ART adherence support. Individualization and in-game incentives were preferable and informed the design of an interactive technology-based adherence intervention among AYA living with HIV.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".