Virtual Health Assistants in Preventive Cancer Care Communication: Systematic Review
Notice bibliographique
Résumé
Background: Virtual health assistants (VHAs), interactive digital programs that emulate human communication, are being increasingly used in health care to improve patient education and care and to reduce the burden on health care providers. VHAs have the potential to promote cancer equity through facilitating patient engagement, providing round-the-clock access to information, and reducing language barriers. However, it is unclear to what extent audience-centeredness is being considered in the development of cancer-related applications. Objective: This systematic review identifies and synthesizes strategies used to make VHA-based cancer prevention and screening interventions audience-centered. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we searched 4 databases (PubMed, Embase, Web of Science, and EBSCOhost) for peer-reviewed studies on VHA interventions promoting cancer screening (January 2022). Included studies focused on adult populations in primary care settings, with interventions emphasizing interactivity and immediacy (key VHA features). Excluded studies were on cancer treatment, noninteractive decision aids, or technical VHA development. Screening, data extraction, and quality assessment (Mixed Methods Appraisal Tool) were performed independently by multiple reviewers. Thematic synthesis was used to analyze audience-centered strategies. Results: Of 1055 records screened, 17 studies met inclusion criteria. Most (n=11) targeted colorectal cancer, with others addressing prostate, breast, cervical, or lung cancer. A total of 16 studies were US-based; 1 study focused on Uganda. Key strategies for audience-centered design included: (1) Demographic Concordance: Race or gender alignment between VHA and users (eg, African American participants interacting with Black-coded avatars); (2) User Feedback: Iterative testing via interviews, think-aloud protocols, or pilot studies to refine interventions; (3) Preintervention Needs Assessment: Identifying cultural, linguistic, or literacy barriers (eg, myths about screening in Ugandan communities); (4) Theoretical Frameworks: The Health Belief Model (most common), the Modality, Agency, Interactivity, and Navigability (MAIN) model, or tailored messaging theories guided design; (5) Information Customization: Culturally adapted content (eg, Spanish-language interfaces, narratives addressing racial disparities); and (6) Feature Customization: Adjusting VHA appearance (eg, animations and fonts) based on user preferences. Notably, 7/17 studies focused on racially minoritized groups (eg, African Americans, Hispanic farmworkers), addressing systemic barriers like mistrust in health care. However, gaps persisted in intersectional tailoring (eg, rurality and income) and non-English languages (only 2/17 studies). Recruitment methods influenced diversity; community-based strategies yielded more representative samples than solely internet-based recruitment approaches. Conclusions: The systematic review identified the audience-centered development practices currently being used for VHA-based interventions in preventive cancer care. The majority of the studies included processes to diversify and segment the intended audience, focused on medically underrepresented population groups, and implemented strategies to be culturally sensitive to the population of interest. However, opportunities remain to address multidimensional inequities (eg, rural access and low literacy). Future interventions should integrate intersectional frameworks, expand language diversity, and measure social presence to enhance engagement. This review provides a roadmap for developing equity-focused eHealth tools in cancer prevention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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) | 0,004 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 ».