Loneliness and insomnia in a representative sample of United States adults: investigating the effects of age, sex, and depression
Bibliographic record
Abstract
This study examined the association between loneliness and insomnia symptoms in a large, nationally representative sample of U.S. adults. We also tested age and sex as moderators and depression as a potential mediator. Participants (N = 1,594) were recruited via Prime Panels and completed measures of insomnia, loneliness, and depression. Linear regression models assessed the relationship between loneliness and insomnia severity, adjusting for age, sex, race/ethnicity, educational attainment, and household income. We also tested moderation by age and sex, and mediation by depression symptoms. Loneliness was significantly and positively associated with insomnia symptom severity. Neither age nor sex moderated this association. Depression symptoms partially mediated the relationship between loneliness and insomnia. These findings suggest that the relationship between loneliness and insomnia symptoms is robust across age and sex and may be partially explained by elevated depression symptoms. Future research should investigate the directionality and causal mechanisms of these links to inform integrated interventions targeting social connection, mental health, and sleep quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".