Digital Health Interventions to Support Chronic Disease Management: Systematic Scoping Review
Notice bibliographique
Résumé
Background: Health interventions delivered by digital platforms are gaining popularity and are evolving to address the needs of patients with chronic diseases. The heterogeneity of chronic diseases requires that digital health platforms vary in their approaches to chronic disease management. Objective: This review aimed to explore the characteristics of digital health platforms and the corresponding digital interventions developed to support patients with chronic diseases. This includes those platforms' design, development, and the metrics by which any incremental benefits they provide are assessed. Methods: We searched electronic databases including Scopus, Web of Science, PsycINFO, IEEE Xplore, MEDLINE, and Embase. Relevant articles published from January 2013 to November 2024 were extracted. Extracted data were then synthesized using qualitative content analysis and presented in narrative form with relevant tables. Results: In total, we identified 69 digital health platforms supporting the management of 20 chronic diseases. Most platforms were mobile apps (n=22) or a combination of web and mobile apps (n=15). Most of the platforms (n=44) were tailored to support self-management of chronic diseases. These platforms also provided a web-based portal where health care providers could review and manage the information recorded by patients. In 77% (53/69) of the studies, patients reported that the digital interventions delivered by the platform improved their quality of life, their health, and their ability to self-manage their chronic diseases. In addition, health care providers reported positive outcomes, including improved clinical utility and patient communication. While short-term health outcomes of the digital health interventions were largely positive, long-term health outcomes remain unknown. This was because most of the studies were short-term pilots and often formative in nature (n=42). Many had limited sample sizes, limited participant uptake of the digital platforms, and technical issues. In many cases, further personalization of platforms was required to meet patients' self-management needs. Conclusions: Digital health interventions can be beneficial in the management of chronic disease. The adoption of digital interventions in combination with regular clinical care can improve health outcomes, support self-management, and enhance communication between patients and health care providers. However, long-term user engagement is the major barrier to their long-term success. High dropout rates, often resulting from a lack of motivation or technical issues, testify to the need for adaptive, low-burden interventions that function seamlessly in users' daily lives. Adopting user-centered and co-design approaches that engage both clinicians and patients in designing digital health platforms may enhance the usability and uptake of such platforms.
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 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 | 0,016 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,014 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».