Sleep and Depressive Symptoms in Sedentary Community-Dwelling Older Adults With Sleep Complaints: Findings From Ambulatory Sleep EEG
Bibliographic record
Abstract
BackgroundThere is limited and inconsistent evidence on the association between electroencephalography (EEG) measured sleep and depressive symptoms among community-dwelling older adults. This study aimed to investigate the cross-sectional association between EEG-measured sleep and depressive symptoms.MethodsUsing baseline data from a randomized clinical trial, we included 66 sedentary community-dwelling older adults with sleep complaints (≥ 1 self-reported insomnia symptom). Sleep was measured using an in-home sleep EEG (Sleep Profiler™) for 2 nights and the Geriatric Depression Scale (GDS-15) was used to measure depressive symptoms. Multiple linear regression analyses were conducted with each sleep parameter as the primary predictor and GDS score as the outcome, adjusting for age, sex, race, education, marital status, chronic conditions, and Montreal Cognitive Assessment (MoCA) score.ResultsSeveral sleep variables were associated with depressive symptoms (GDS score), including a higher percentage of sleep stage N1 (B = 0.11, 95% confidence interval [CI]: 0.02 - 0.20) and N2 (B = 0.04, 95% CI: 0.00 - 0.08), a lower percentage of N3 sleep (B = -0.04, 95% CI: -0.08 to -0.01), greater wake after sleep onset (B = 0.01, 95% CI: 0.00 - 0.02), and a greater number of awakenings ≥90s/hour (B = 0.87, 95% CI: 0.21-1.53).ConclusionsOur study reveals that among sedentary community-dwelling older adults with sleep complaints, more lighter sleep (stage N1, N2), less deep (N3) sleep, and increased awakenings are associated with more depressive symptoms. Sleep interventions aimed at enhancing sleep architecture may also help alleviate depressive symptoms in this population.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".