Association Between Loneliness, eHealth Literacy and Quality of Life Among Chinese Older Adults: Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
Background: Loneliness is a growing public health challenge among older adults and is associated with a wide range of adverse health outcomes. The role of eHealth literacy in shaping the relationship between loneliness and quality of life remains unclear. Objective: This study aimed to examine the contributing role of eHealth literacy in moderating the associations between loneliness and quality of life among older adults. Methods: A community-based survey was conducted in 2025 among older adults aged 60 years or older living in northwestern China. A total of 2110 participants were included. Multiple linear regression and interaction models were used to assess associations and moderating effects. Sensitivity analyses were conducted by replacing the outcome variable with depressive symptoms and by performing stratified analyses to assess the robustness of the primary interaction findings. Results: Loneliness showed a consistent negative association with quality of life (β=-0.83, 95% CI -1.18 to -0.49; P<.001). Higher overall eHealth literacy was associated with better quality of life (β=0.41, 95% CI 0.35-0.47; P<.001). Interaction models indicated that higher eHealth literacy was associated with a steeper negative association between loneliness and quality of life (β=-0.04, 95% CI -0.08 to -0.002; P=.04). Sensitivity analyses produced similar results across alternative outcomes and subgroups. Conclusions: Higher loneliness was related to a poorer quality of life. Higher eHealth literacy was associated with a steeper negative association between loneliness and quality of life. These findings suggest that eHealth literacy may function as a double-edged sword in later life. Future research is needed to clarify the underlying mechanisms and to examine how digital health competencies interact with psychosocial vulnerability in shaping older adults' well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".