Challenges of and Solutions for Developing Tailored Video Interventions That Integrate Multiple Digital Assets to Promote Engagement and Improve Health Outcomes: Tutorial
Notice bibliographique
Résumé
BACKGROUND: Video is a versatile and popular medium for digital health interventions. As mobile device and app technology advances, it is likely that video-based interventions will become increasingly common. Although clinic waiting rooms are complex and busy environments, they offer the opportunity to facilitate engagement with video-based digital interventions as patients wait to see their providers. However, to increase efficiency in public health, leverage the scalability and low cost of implementing digital interventions, and keep up with rapidly advancing technology and user needs, more design and development guidance is needed for video-based tailored interventions. OBJECTIVE: We provide a tutorial for digital intervention researchers and developers to efficiently design and develop video-based tailored digital health interventions. We describe the challenges and solutions encountered with Positive Health Check (PHC), a hybrid app used to deliver a brief, interactive, individually tailored video-based HIV behavioral counseling intervention. PHC uses video clips and multimedia digital assets to deliver intervention content, including interactive tailored messages and graphics, a repurposed animated video, and patient and provider handouts generated in real time by PHC. METHODS: We chronicle multiple challenges and solutions for the following: (1) using video as a medium to enhance user engagement, (2) navigating the complexity of linking a database of video clips with other digital assets, and (3) identifying the main steps involved in building an app that will seamlessly deliver to users individually tailored messages, graphics, and handouts. RESULTS: We leveraged video to enhance user engagement by featuring "video doctors," full-screen video, storyboards, and streamlined scripts. We developed an approach to link the database of video clips with other digital assets through script coding and flow diagrams of algorithms to deliver a tailored user experience. We identified the steps to app development by using keyframes to design the integration of video and digital assets, using agile development methods to gather iterative feedback from multidisciplinary teams, and creating an intelligent data-driven back-end solution to tailor message delivery to individual users. CONCLUSIONS: Video-based digital health interventions will continue to play an important role in the future of HIV prevention and treatment, as well as other clinical health practices. However, facilitating the adoption of an HIV video intervention in HIV clinical settings is a work in progress. Our experience in designing and developing PHC presented unique challenges due to the extensive use of a large database of videos tailored individually to each user. Although PHC focuses on promoting the health and well-being of persons with HIV, the challenges and solutions presented in this tutorial are transferable to the design and development of video-based digital health interventions focused on other areas of health.
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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 | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
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 ».