A Digital Sexual Health Intervention for Urban Adolescent and Young Adult Male Emergency Department Patients: User-Centered Design Approach
Notice bibliographique
Résumé
BACKGROUND: Adolescents and young adults frequently present to the emergency department (ED) for medical care and continue to have many unmet sexual health needs. Digital interventions show promise to improve adolescent and young adult sexual health; yet, few interventions focus on male ED patients, despite their infrequent use of contraceptives and rising rates of sexually transmitted infections. OBJECTIVE: This paper describes the design and development of Dr. Eric (Emergency Room Interventions to Improve Care), a digital app focused on promoting condom use among sexually active adolescent and young adult male ED patients. METHODS: This study followed 4 phases of app development, which were based on user-centered design and the software development lifecycle. In phase 1, define, we explored our target population and target health problem (infrequent condom use among male ED patients) by collecting key stakeholder input and conducting in-depth interviews with male patients and urban ED medical providers. In phase 2, discover, we partnered with a digital product agency to explore user experience and digital strategy. In phase 3, design, we refined Dr. Eric's content, a 5-part sexual health educational module and a 10-week SMS text messaging program that focuses on condom use and partner communication about effective contraceptives. We conducted semistructured interviews with male adolescent and young adults to gather feedback on the app and perform usability testing, editing the app after each interview. We also interviewed informatics experts to assess the usability of a high-fidelity prototype. Interviews were recorded and analyzed via descriptive thematic analysis; informatic expert feedback was categorized by Nielsen's heuristic principles. In phase 4, develop, we created the technical architecture and built a responsive web app. These findings were gathered leading to the final version of the digital Dr. Eric program. RESULTS: Using data and key stakeholder input from phases 1 and 2, we iteratively created the Dr. Eric prototype for implementation in the ED setting. Interviews with 8 adolescent and young adult male ED patients suggested that users preferred (1) straightforward information, (2) a clear vision of the purpose of Dr. Eric, (3) open-ended opportunities to explore family planning goals, (4) detailed birth control method information, and (5) games presenting novel information with rewards. Five usability experts provided heuristic feedback aiming to improve the ease of use of the app. These findings led to the final version of Dr. Eric. CONCLUSIONS: Following these mobile health development phases, we created a digital sexual health mobile health intervention incorporating the principles of user experience and interface design. Dr. Eric needs further evaluation to assess its efficacy in increasing condom use among adolescent and young adult male ED patients. Researchers can use this framework to form future digital health ED-based digital interventions.
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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 | 0,013 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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) | 0,005 | 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 ».