Assessing the Ability to Use eHealth Resources Among Older Adults: Cross-Sectional Survey Study
Notice bibliographique
Résumé
Background: Increasing reliance on digital health resources can create disparities among older patients. Understanding health-related, mobility, and socioeconomic factors associated with the use of eHealth technologies is important for addressing inequitable access to health care. Objective: We sought to assess digital health literacy among patients aged ≥65 years and identify factors associated with their ability to access, understand, and use digital health resources. Methods: We developed a survey instrument grounded in the Technology Acceptance Model and conducted a cross-sectional, mixed-mode survey of patients aged ≥65 years from an integrated, multispecialty medical center. Digital health literacy was measured using the eHeals health literacy scale, and responses were analyzed across self-rated health, self-reported mobility, and socioeconomic deprivation assessed with the Area Deprivation Index (ADI). Counts (n) and frequencies (%) are reported across response groups, and analyses for differences are performed using the χ2 test for independence or the Fisher exact test. Results: Analyses included 878 responses (response rate=878/2847; 30.8%). There was a significant difference in the distribution of race between responders and nonresponders (P<.001) but no significant differences were observed by age (P=.053) or gender (P=.73). Respondents with lower self-rated health had lower levels of digital health literacy; only 54.2% (n=13/25) participants with poor self-rated health were able to send a message to their doctor compared to 89.5% (n=68/77) of patients with excellent self-rated health. All comparisons across the digital health literacy domains revealed significant differences across self-rated health groups (P<.05). Respondents with mobility restrictions had lower levels of digital health literacy, including lower frequencies of reporting knowledge of what health resources are available on the internet (mobility restricted, n=92/182; 52.0% vs no mobility restriction, n=433/688; 64.7%), knowledge of how to find health resources on the internet (mobility restricted, n=120/182; 67.4% vs no mobility restriction, n=513/688; 76.8%), and ability to use a camera or video with a doctor easily (mobility restricted, n=58/182; 32.6% vs no mobility restriction, n=321/688; 48.0%). Older adults experiencing increased socioeconomic deprivation, as measured by the ADI, reported lower rates of digital health literacy across most categories, including knowledge of how to find health resources on the internet (high ADI, n=28/49; 59.6% vs low ADI, n=551/751; 75.5%) and the ability to send an electronic message to their doctor easily (high ADI, n=27/49; 57.4% vs low ADI, n=584/751; 80.2%). Conclusions: Our findings highlight the need for targeted interventions to improve engagement with eHealth among patients aged ≥65 years, who are impacted by poor health, limited mobility, and socioeconomic deprivation. Enhancing digital health literacy can help bridge the gap in access to digital health resources and improve overall health outcomes for this population.
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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 | 0,003 | 0,005 |
| 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».