Preferred Characteristics for mHealth Interventions Among Young Sexual Minoritized Men to Support HIV Testing and PrEP Decision-Making: Focus Group Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».