Barriers to and Facilitators of Digital Health Technology Adoption Among Older Adults With Chronic Diseases: Updated Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Older adults with chronic diseases are key beneficiaries of digital health technologies, yet adoption remains inconsistent, particularly in rural areas and among certain demographic groups, such as older women. OBJECTIVE: This systematic review aimed to identify barriers to and facilitators of digital health adoption among older adults with chronic diseases, with particular attention to rural-urban differences, co-design, and equity-relevant factors. METHODS: This updated review built on a previously published review by extending the search to include PsycArticles, Scopus, Web of Science, and PubMed databases for studies published between April 2022 and September 2024. Gray literature from August 2021 onward was also included. Studies were eligible if they reported barriers to or facilitators of digital health adoption among adults aged ≥60 years with chronic diseases. Findings were mapped to the capability, opportunity, and motivation-behavior model and analyzed using the PROGRESS-Plus (place of residence; race, ethnicity, culture, and language; occupation; gender and sex; religion; education; socioeconomic status; and social capital-plus) equity framework. Quality was assessed using the Mixed Methods Appraisal Tool, and all results are reported in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: In total, 12 studies from the original review were retained, with 17 new peer-reviewed studies added, yielding a total of 29 studies in addition to 30 documents identified in the gray literature search. Barriers included limited digital literacy and physical and cognitive challenges (capability); infrastructural deficits and usability challenges (opportunity); and privacy concerns, mistrust, and high satisfaction with existing care (motivation). Facilitators included tailored training and accessible design (capability), health care provider endorsement and hybrid care models (opportunity), and recognition of digital health benefits (motivation). Health care providers emerged as both facilitators and barriers, positively influencing adoption when engaged and trained but hindering it when lacking confidence or involvement. Comparative analysis of rural and urban contexts was limited by inconsistent reporting of equity-relevant variables. However, gray literature suggested that rural users face additional infrastructural challenges but express higher satisfaction with local care, potentially reducing motivation for digital uptake. Gender differences were observed in 5% (3/59) of the peer-reviewed studies and gray literature sources, with older women showing lower adoption and differing outcome priorities. Co-design enhanced adoption, especially when involving not just older adults but also health care providers and community stakeholders. CONCLUSIONS: Digital health adoption among older adults is shaped by capability, opportunity, and motivation factors. Effective and equitable digital health strategies must address infrastructural and literacy barriers, engage health care providers through training and co-design, and ensure multistakeholder involvement. This review highlights that greater attention to standardized reporting of demographic variables, especially gender and rurality, is essential in digital health research to support inclusive implementation. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42024586893; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024586893. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-https://doi.org/10.3399/bjgp25X742161.
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 | 0,015 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,010 |
| Bibliométrie | 0,013 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».