mHealth Strategies Related to HIV Postexposure Prophylaxis Knowledge and Access: Systematic Literature Review, Technology Prospecting of Patent Databases, and Systematic Search on App Stores
Notice bibliographique
Résumé
BACKGROUND: Globally, the number of HIV cases continue to increase, despite the development of multiple prevention strategies. New cases of HIV have been reported disproportionately more in men who have sex with men and other vulnerable populations. Issues such as internalized and structural homophobia prevent these men from accessing prevention strategies such as postexposure prophylaxis (PEP). Mobile health (mHealth) interventions are known to be one of the newest and preferred options to enhance PEP knowledge and access. OBJECTIVE: The aim of this study was to identify and analyze the mobile apps addressing PEP for HIV infections. METHODS: We conducted a descriptive exploratory study in 3 sequential phases: systematic literature review, patent analysis, and systematic search of app stores. For the systematic review, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines adapted for an integrative review in the databases of PubMed, Web of Knowledge, Scopus, Cochrane, Embase, Science Direct, Eric, Treasure, and CINAHL. The patent analysis was performed by exploring the databases of the Brazilian National Institute of Industrial Property, the United States Patent and Trademark Office, and the European Patent Office. For the systematic search, we analyzed mHealth apps related to HIV in 2 major app libraries, that is, Google Play Store and App Store. The apps were evaluated by name, characteristics, functions, and availability in iPhone operating system/Android phones. RESULTS: We analyzed 22 studies, of which 2 were selected for the final stage. Both studies present the use of apps as mHealth strategies aimed at improving the sexual health of men who have sex with men, and they were classified as decision support systems. The search in the patent databases showed only 1 result, which was not related to the topic since it was a drug intervention. In the app libraries, 25 apps were found and analyzed, with 15 (60%) apps available for Android systems but only 3 (12%) addressing PEP. In general, the apps inform about HIV and HIV prevention and treatment, with the focus users being health care providers, people with HIV, or the general population, but they have only limited features available, that is, mainly text, images, and videos. The 3 apps exclusively focusing on PEP were created by researchers from Brazilian universities. CONCLUSIONS: Our review found no connection between the scientific studies, registered patents, and the available apps related to PEP; this finding indicates that these available apps do not have a theoretical or a methodological background in their creation. Thus, since the scientific knowledge on HIV is not translated into technological products, preventing the emergence of new infections, especially in the more vulnerable groups, is difficult. In the future, researchers and the community must work in synergy to create more mHealth tools aimed at PEP.
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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 | 0,016 | 0,078 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,008 |
| Bibliométrie | 0,028 | 0,024 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».