DIGItal health literacy after COVID-19 outbreak among frail and non-frail cardiology patients: the DIGI-COVID study
Notice bibliographique
Résumé
Abstract Funding Acknowledgements Type of funding sources: None. Background The COVID-19 pandemic has highlighted the role of telemedicine in reducing face-to-face visits. Telemedicine requires either the use of digital support methods and a minimum technological knowledge of the patients. Digital health literacy, defined as the use of digital literacy skills to find and use health information and services, may influence the use of telemedicine in most patients, particularly in specific groups such as those with frailty. Aim To explore the association between frailty status, patients' use of digital tools and digital health literacy to determine whether it would be possible to implement control visits in patients followed in a cardiac arrhythmias outpatient clinic. Methods We prospectively enrolled consecutive patients referring to arrhythmias outpatient clinics of our department from March to September 2022. Patients were divided according to frailty status as defined by the Edmonton Frail Scale (EFS) into three subgroups: robust, pre-frail, and frail. The degree of health digital literacy was assessed through the Digital Health Literacy Instrument (DHLI) Scale. The DHLI explores 7 digital skill categories measured by 21 self-report questions. The self-report questions require participants to rate on a 4-point scale how difficult different tasks are and how frequently they encounter certain difficulties on the Internet. The total DHLI and each skill category score were calculated by summing the received scores in every single domain (3 questions per each skill category) and reported as mean and median. A multivariable logistic regression analysis was also use to evaluate the association between the non-use of the Internet and frailty status. Results A total of 300 patients were enrolled (36.3% females, median age 75 [66-84]) and stratified according to frailty status as: (i) Robust (EFS ≤ 5; n = 212, 70.7%), (ii) Pre-Frail (EFS 6-7; n = 47, 15.7%), and (iii) Frail (EFS ≥ 8; n = 41, 13.7%). Frail patients used less frequently smartphones, PC and emails and had less availability of Wi-Fi at home compared to robust patients (Table 1). At the multivariable logistic regression analysis, frailty was significantly associated with the non-use of the Internet (adjusted odds ratio, 2.58 95% confidence interval 1.92-5.61). Digital health literacy score decreased as the level of frailty increased in all the domains explored (operational skills, navigation skills, information searching, evaluating the reliability of the information, determining the relevance of online information, adding self-generated content and protecting privacy while using the internet, all p<0.001, Table 2). Conclusions Frail patients are characterized by a lower use of digital tools and access to the Internet even though these patients would benefit the most from telemedicine. Digital skills are strongly influenced by frail status highlighting the need to implement digital health literacy with specific interventions in this population.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 |
| 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 ».