Association Between Visual Impairment and Daily Internet Use Among Older Japanese Individuals: Cross-Sectional Questionnaire Study
Bibliographic record
Abstract
Background: Older adults might not use computers due to psychological barriers, environmental barriers such as not owning a computer or lack of internet access, and health-related barriers such as difficulties with fine motor skills, low cognitive function, or low vision. Given the health benefits of internet use among older adults, inadequate use of the internet is an urgent public health issue in many countries. Objective: We aimed to determine whether visual impairment is associated with internet use in a population-based sample of older adults. Methods: This cross-sectional study sourced data for the year 2016 from the Japan Gerontological Evaluation Study. It included functionally independent community-dwelling individuals aged ≥65 years (N=19,452) in Japan. The respondents reported their visual status by answering the question, "Is your eyesight (without or with usual glasses or corrective lenses) excellent, very good, good, fair, or poor?" We defined "internet user" as a person who uses the internet "almost daily." We used multivariate logistic regression with multiple imputations to analyze visual status, daily internet use, and any correlations between them. Results: We observed that 23.6% (4599/19,452) of respondents used the internet almost daily. Respondents with good visual status notably tended to use the internet more frequently than those with poor visual status. Specifically, 13% and 31% of respondents with poor and excellent vision, respectively, used the internet almost daily. In contrast, 66% and 45% of respondents with poor and excellent vision, respectively, did not use the internet. Even after adjusting for several covariates (age, sex, equivalized income, years of education, marital status, depression, history of systemic comorbidities, frequency of meeting friends, and total social participation score), significant associations persisted between visual status and daily internet usage. The odds ratios (ORs) tended to increase as visual status improved (P for trend <.001). The adjusted ORs for individuals with excellent and very good visual status who used the internet almost daily were 1.38 (95% CI 1.22-1.56) and 1.25 (95% CI 1.15-1.36), respectively. Conversely, the adjusted OR for those with fair or poor visual status was 0.73 (95% CI 0.62-0.86). Conclusions: In this study, we reaffirmed what several previous studies have pointed out using a very large dataset; visual impairment negatively impacted daily internet use by older adults. This highlights the need to address visual impairments to promote web use as health care services become more easily accessed on the web.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".