Preferred Characteristics for mHealth Interventions Among Young Sexual Minoritized Men to Support HIV Testing and PrEP Decision-Making: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Epidemiological trends in the United States have shown an increase in HIV cases among young sexual minoritized men. Using mobile health (mHealth), which refers to health services and information delivered or enhanced through the internet and related technologies, is a crucial strategy to address HIV disparities. However, despite its potential, the practical implementation of mHealth remains limited. Additionally, it is important to consider that young individuals may become accustomed to, distracted from, or lose interest in these apps, highlighting the need for regular updates and monitoring of relevant content. OBJECTIVE: In this study, we sought to highlight the voices of young sexual minoritized men aged 17-24 years and explored preferred mHealth intervention characteristics and willingness to adopt these technologies among a diverse, nationwide sample of young sexual minoritized men. METHODS: From April to September 2020, we recruited participants through web-based platforms such as social media and geosocial networking apps for men. These individuals were invited to participate in synchronous web-based focus group discussions centered around topics pertaining to HIV testing and prevention and their preferences for mHealth technologies. RESULTS: A total of 41 young sexual minoritized men, aged between 17 and 24 years, participated in 9 focus group discussions spanning April to September 2020, with 3-7 participants in each group. The findings shed light on three key insights regarding young sexual minoritized men's preferences: (1) the need for personalized and representative content, (2) a preference for mobile and web-based simulation of prevention scenarios, and (3) a preference for digital software with individually tailored content. As expected, preference for mHealth apps was high, which supports the potential and need to develop or adapt interventions that use smartphones as a platform for engaging young sexual minoritized men in HIV prevention. This study expands on previous research in multiple meaningful ways, delving into the use and perceptions of mHealth information amid the COVID-19 pandemic. This study also highlighted the importance of streamlined access to health care providers, especially in light of the barriers faced by young people during the COVID-19 pandemic. In terms of presentation and navigation, participants favored a user-friendly design that was easy to use and appropriate for their age, which was effectively addressed through the implementation of web-based simulations. CONCLUSIONS: Ultimately, this study provides valuable insight into the preferences of young sexual minoritized men when it comes to mHealth interventions and highlights the need for further research in order to develop effective and tailored HIV prevention tools. A future direction for researchers is to evaluate how best to address participants' desire for personalized content within mHealth apps. Additionally, as technology rapidly evolves, there is a need to re-assess the effectiveness of web-based simulations, particularly those that are used in HIV prevention.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".