eHealth Literacy and Its Association With Demographic Factors, Disease-Specific Factors, and Well-Being Among Adults With Type 1 Diabetes: Cross-Sectional Survey Study
Notice bibliographique
Résumé
Background: The use of digital health technology in diabetes self-care is increasing, making eHealth literacy an important factor to consider among people with type 1 diabetes. There are very few studies investigating eHealth literacy among adults with type 1 diabetes, highlighting the need to explore this area further. Objective: The aim of this study was to explore associations between eHealth literacy and demographic factors, disease-specific factors, and well-being among adults with type 1 diabetes. Methods: The study used data from a larger cross-sectional survey conducted among adults with type 1 diabetes in Sweden (N=301). Participants were recruited using a convenience sampling method primarily through advertisements on social media. Data were collected between September and November 2022 primarily through a web-based survey, although participants could opt to answer a paper-based survey. Screening questions at the beginning of the survey determined eligibility to participate. In this study, eHealth literacy was assessed using the Swedish version of the eHealth Literacy Scale (Sw-eHEALS). The predictor variables, well-being was assessed using the World Health Organization-5 Well-Being Index and psychosocial self-efficacy using the Swedish version of the Diabetes Empowerment Scale. The survey also included research group-developed questions on demographic and disease-specific variables as well as digital health technology use. Data were analyzed using multiple linear regression presented as nested models. A sample size of 270 participants was required in order to detect an association between the dependent and predictor variables using a regression model based on an F test. The final sample size included in the nested regression model was 285. Results: The mean Sw-eHEALS score was 33.42 (SD 5.32; range 8-40). The model involving both demographic and disease-specific variables explained 31.5% of the total variation in eHealth literacy and was deemed the best-fitting model. Younger age (P=.01; B=-0.07, SE=0.03;95% CI -0.12 to -0.02), lower self-reported glycated hemoglobin levels (P=.04; B=-0.06, SE=0.03; 95% CI -0.12 to 0.00), and higher psychosocial self-efficacy (P<.001; B=3.72, SE=0.53; 95% CI 2.68-4.75) were found associated with higher Sw-eHEALS scores when adjusted for demographic and disease-specific variables in this model. Well-being was not associated with eHealth literacy in this study. Conclusions: The demographic and disease-specific factors explained the variation in eHealth literacy in this sample. Further studies in this area using newer eHealth literacy tools are important to validate our findings. The study highlights the importance of development and testing of interventions to improve eHealth literacy in this population for better glucose control. These eHealth literacy interventions should be tailored to meet the needs of people in varying age groups and with differing levels of psychosocial self-efficacy.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».