Assessing the Ability to Use eHealth Resources Among Older Adults: Cross-Sectional Survey Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".