Frequency of Electronic Personal Health Record Use in US Older Adults: Cross-Sectional Study of a National Survey
Bibliographic record
Abstract
Background: Electronic personal health records (ePHRs) hold significant potential to improve health management for older adults by enhancing access to medical information and facilitating communication with health care providers. However, usage remains low among individuals aged 65 and older. While existing research has identified barriers such as low self-efficacy, limited digital literacy, and usability challenges, the specific factors influencing the use of ePHRs among older adults are not yet fully understood. Objective: This study integrates the Aging and Technology framework with the Patient Technology Acceptance Model to examine key predictors of ePHR use among older adults, including age, education, issue involvement, performance expectancy, effort expectancy, and self-efficacy, while controlling for demographic factors such as gender, race, and income. Methods: This study utilizes data from the Health Information National Trends Survey (HINTS 5 cycle 3), which includes 532 respondents representing 13,136,180 US adults aged 65 years and older, after applying survey weights. Structural equation modeling was used to analyze the factors influencing the frequency of ePHR access over the past 12 months. Results: The findings indicate that older adults with higher self-efficacy used ePHRs more frequently. Additionally, issue involvement, performance expectancy, and effort expectancy were positively associated with ePHR use. Notably, self-efficacy partially mediated the relationship between age and the frequency of ePHR use. Conclusions: These findings suggest that enhancing self-efficacy, improving usability, and increasing the perceived benefits of ePHRs are critical for boosting usage among older adults. The study underscores the need for targeted interventions to support older users, simplify digital interfaces, and provide accessible educational resources, ultimately contributing to better health outcomes and an improved quality of life for older adults.
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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 | 0.002 |
| 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 | 0.000 |
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".