CROSS-SECTIONAL AND LONGITUDINAL ASSOCIATIONS BETWEEN SLEEP DISTURBANCES, DEPRESSIVE AND ANXIETY SYMPTOMS
Bibliographic record
Abstract
Abstract Introduction Older adults often experience elevated sleep disturbances, depressive and anxiety symptoms. However, the role of sleep disturbances in explaining individual variability in depressive and anxiety symptoms among older adults is poorly understood. Methods The sample was derived from the National Social Life, Health, and Aging Project, a nationally representative longitudinal study among American older adults. MCI was defined as Montreal Cognitive Assessment scored less than 23. Subjective insomnia symptoms and objective sleep measures (total sleep time, wake after sleep onset, percentage sleep) obtained from actigraphy were used. Validated measures on depressive and anxiety symptoms were collected both at round 2(N=645) and round 3(N=456). Multiple regressions were conducted to establish cross-sectional and longitudinal associations between sleep disturbances, depressive and anxiety symptoms. Results Cross-sectionally, compared to cognitively intact older adults, severe insomnia symptoms were associated with poorer depressive symptoms(B=0.40, p< 0.01) in older adults with MCI. Severe insomnia symptoms were associated with poorer anxiety symptoms(B=0.13, p< 0.01) in older adults, and no interaction effects were found by MCI groups. Longitudinally, insomnia symptoms at round 2 were associated with poorer depressive symptoms and poorer anxiety symptoms in older adults at round 3, but no interaction effects were found by MCI groups. No significant relationships were found between objective sleep disturbances and depressive/anxiety symptoms both cross-sectionally and longitudinally. Conclusions The findings provide further insight into insomnia symptoms that may be associated with increased risks for developmental depressive and anxiety symptoms. These data suggest that targeting insomnia treatment may confer long-term mental health benefits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".