Prevention of HIV and Other Sexually Transmitted Infections by Geofencing and Contextualized Messages With a Gamified App, UBESAFE: Design and Creation Study
Notice bibliographique
Résumé
BACKGROUND: Advances in the development of information and communication technologies have facilitated social and sexual interrelationships, thanks to the websites and apps created to this end. However, these resources can also encourage sexual contacts without appropriate preventive measures in relation to HIV and other sexually transmitted infections (STIs). How can users be helped to benefit from the advantages of these apps while keeping in mind those preventive measures? OBJECTIVE: This study aimed to prevent STIs by helping users to remember preventive measures in the risky situations. METHODS: We have used the design and creation methodology and have developed a software system. This system has two parts: an Android operating system app with emphasis on ubiquitous computing and gamification as well as a server with a webpage. First, a functional test with 5 men who have sex with men (MSM) allowed us to test the app with end users. In addition, a feasibility test with 4 MSM for a month allowed us to try the UBESAFE system with all its functionalities. RESULTS: The main output is a system called UBESAFE that is addressed to MSM. The system has two main parts: (1) an app that sends preventive contextualized messages to users when they use a contact app or when they are near a point where sexual contacts are likely and (2) a server part that was managed by the public health agency of Barcelona (ASPB), which preserves the quality and pertinence of messages and places and offers instant help to users. To increase users' adherence, UBESAFE uses a gamified system to engage users in the creation of preventive messages. Users increased the initial pool of messages by more than 100% (34/30) and created more than 56% (9/16) of places (named hot zones). CONCLUSIONS: The system helped MSM who used it to become conscious about HIV and other STIs. The system also helped the ASPB to stay in contact with MSM and to detect behaviors that could benefit from preventive measures. All functions were performed in a nonintrusive manner because users used the app privately. Furthermore, the system has shown how important it is to make users a part of the creation process as well as to develop apps that work by themselves and thus become useful to the users.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».