Examining Challenges to Co-Design Digital Health Interventions With End Users: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Digital health interventions (DHIs) are changing the dynamic of health care by providing personalized, private, and instantaneous solutions to end users. However, the explosion of digital health has been fraught with challenges. The approach to co-design with end users varies across a diverse domain of stakeholders, often resulting in siloed approaches with no clear consensus. The concept of validating user experiences contrasts greatly between digital stakeholders (ie, user experience and retention) and health stakeholders (ie, safety and efficacy). Several methodologies and frameworks are being implemented to address this challenge to varying degrees of success. OBJECTIVE: We aimed to broadly examine the advancements and challenges to co-design DHIs with end users over the last decade. This task was undertaken to identify the key problem areas at the domain level, with the ultimate goal of creating recommendations for better approaches to co-design DHIs with end users. METHODS: We conducted a systematic search of key databases for co-design studies involving end users in DHIs. Searches were divided into 3 relevant streams: health behavior, user experience, and digital methodologies and frameworks. The eligibility criteria were guided by the PerSPEcTiF framework and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. In line with this framework, studies were included in this review that (1) address research on DHIs; (2) focus on interaction and co-design with end users; (3) explain results such that uptake, effectiveness, satisfaction, and health outcomes are discernible, positively or negatively; and (4) describe actionable procedures for better DHI design. The search was conducted in a diverse group of 6 bibliographical databases from January 2015 to May 2024: PsycINFO, PubMed (MEDLINE), Web of Science, CINAHL, Institute of Electrical and Electronics Engineers Xplore, and Scopus. From the 13,961 studies initially screened for titles and abstracts, 489 (3.6%) were eligible for a full-text screening, of which 171 (1.2%) studies matched the inclusion criteria and were included in a qualitative synthesis. RESULTS: Of the 171 studies analyzed across 52 journals, we found 5 different research approaches, spanning 8 different digital health solution types and 5 different design methodologies. These studies identified several core themes when co-designing with end users: advancements, which included participatory co-design; challenges, which included participatory co-design, environment and context, testing, and cost and scale; and gaps, which included a pragmatic hybridized framework and industry implementability. CONCLUSIONS: This research supports a pragmatic shift toward using mixed methods approaches at scale, methods that are primed to take advantage of the emerging big data era of digital health co-design. This organic outlook should blend the vision of digital health co-designers with the pragmatism of Agile design methodology and the rigor of health care metrics. TRIAL REGISTRATION: PROSPERO CRD42021238164; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238164. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/28083.
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 | 0,025 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,005 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».