Digital Literacy and Its Association With Subjective Health Status and Healthy Lifestyle Behaviors Among Korean Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: With an aging population driven by advances in medical technology, digital literacy has become essential for improving the quality of life of older adults, enhancing access to health information, and promoting healthy lifestyles. Furthermore, the COVID-19 pandemic may have influenced the subjective health perceptions and healthy lifestyle behaviors of older adults. However, there is limited research exploring the relationship between digital literacy, subjective health perceptions, and healthy lifestyle behaviors in Korea. Objective: This study aimed to investigate digital literacy's impact on Korean older adults' subjective health status and healthy lifestyle behaviors. Methods: Data of 8664 respondents (aged 65 years and older) from the 2020 National Survey of the Older Koreans were analyzed. Digital literacy was measured based on the use of IT devices (ITDs), difficulty using online information, and inconvenience of ITDs. Statistical analyses, such as the Rao-Scott chi-square test, Wilcoxon rank sum test, and multiple regression analysis, were conducted. Results: Respondents with above-average ITD use (adjusted odds ratio [aOR] 1.73, 95% CI 1.50-1.99) and less difficulty using online information (aOR 1.41, 95% CI 1.24-1.61) had higher odds of perceiving themselves as healthy. Conversely, high difficulty using ITDs was associated with lower odds of respondents perceiving themselves as healthy (aOR 0.84, 95% CI 0.82-0.87). Furthermore, high ITD use predicted engagement in healthy lifestyle behaviors (aOR 1.51, 95% CI 1.33-1.72), whereas high difficulty using ITDs predicted lower odds of engagement (aOR 0.94, 95% CI 0.92-0.97). In contrast, there was no difference in the odds of engaging in healthy lifestyle behaviors regardless of difficulty using online information (aOR 1.03, 95% CI 0.92-1.15). Conclusions: This study underscores the significant association between digital literacy and improved health outcomes among older adults. Promotion of digital literacy and relevant policies is essential to help older adults effectively obtain health information online, thereby improving their quality of life and overall health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".