Exploring Barriers to and Enablers of the Adoption of Information and Communication Technology for the Care of Older Adults With Chronic Diseases: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Information and communication technology (ICT) offers considerable potential for supporting older adults in managing their health, including chronic diseases. However, there are mixed opinions about the benefits and effectiveness of ICT interventions for older adults with chronic diseases. OBJECTIVE: We aim to map the use of ICT interventions in health care and identified barriers to and enablers of its use among older adults with chronic disease. METHODS: A scoping review was conducted using 5 databases (Ovid MEDLINE, Embase, Scopus, PsycINFO, and ProQuest) to identify eligible articles from January 2000 to July 2020. Publications incorporating the use of ICT interventions, otherwise known as eHealth, such as mobile health, telehealth and telemedicine, decision support systems, electronic health records, and remote monitoring in people aged ≥55 years with chronic diseases were included. We conducted a strengths, weaknesses, opportunities, and threats framework analysis to explore the implied enablers of and barriers to the use of ICT interventions. RESULTS: Of the 1149 identified articles, 31 (2.7%; n=4185 participants) met the inclusion criteria. Of the 31 articles, 5 (16%) mentioned the use of various eHealth interventions. A range of technologies was reported, including mobile health (8/31, 26%), telehealth (7/31, 23%), electronic health record (2/31, 6%), and mixed ICT interventions (14/31, 45%). Various chronic diseases affecting older adults were identified, including congestive heart failure (9/31, 29%), diabetes (7/31, 23%), chronic respiratory disease (6/31, 19%), and mental health disorders (8/31, 26%). ICT interventions were all designed to help people self-manage chronic diseases and demonstrated positive effects. However, patient-related and health care provider-related challenges, in integrating ICT interventions in routine practice, were identified. Barriers to using ICT interventions in older adults included knowledge gaps, a lack of willingness to adopt new skills, and reluctance to use technologies. Implementation challenges related to ICT interventions such as slow internet connectivity and lack of an appropriate reimbursement policy were reported. Advantages of using ICT interventions include their nonpharmacological nature, provision of health education, encouragement for continued physical activity, and maintenance of a healthy diet. Participants reported that the use of ICT was a fun and effective way of increasing their motivation and supporting self-management tasks. It gave them reassurance and peace of mind by promoting a sense of security and reducing anxiety. CONCLUSIONS: ICT interventions have the potential to support the care of older adults with chronic diseases. However, they have not been effectively integrated with routine health care. There is a need to improve awareness and education about ICT interventions among those who could benefit from them, including older adults, caregivers, and health care providers. More sustainable funding is required to promote the adoption of ICT interventions. We recommend involving clinicians and caregivers at the time of designing ICT interventions.
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 | 0,051 | 0,183 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,021 | 0,021 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».