Exploring relationships between sleep quality and clinical depression in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Sleep plays an important role in cognitive function, such as emotional regulation, which is important for a higher quality of life. The study objective was to explore the association between baseline sleep quality and 6-year incidence of clinical depression. Using data from the Comprehensive cohort of the Canadian Longitudinal Study on Aging from baseline (2011-2015) to follow-up 2 (2018-2021), a total cohort of 13,997 middle aged and older Canadians free of self-reported diagnosis of clinical depression at baseline was obtained wherein the baseline mean age was 66 years (SD = 7.8), 50.3 % were female, 5.7 % were non-white, and 4.3 % had incident depression by follow-up 2. Sleep-related exposures included self-reported sleep quality (ranging from very dissatisfied to very satisfied), sleep duration, sleep frequencies (initiation difficulty, maintenance difficulty, daytime sleepiness), and symptoms of sleep disorders (sleep apnea, acting out dreams while asleep, restless leg syndrome). Multivariable regression models were used to assess the association between self-reported incident clinical depression and each sleep exposure, adjusting for several covariates (age, sex, sociodemographic, and health characteristics). Fully adjusted models demonstrated that individuals who were very dissatisfied with their sleep quality had 1.6 times the odds of reporting new depression while those with satisfied sleep had 1.2 times the odds of reporting new depression, compared to those who reported very satisfied sleep. Those with ≤4 h of sleep and those with 10+ hours of sleep had a higher predicted probability of having depression by 6-year follow-up, compared to those with 7 to 8 hours of sleep. This analysis suggests that poorer sleep quality may be associated with incident clinical depression, supporting the potential benefit of preventative measures and improvements to individuals' sleep in reducing the risk of depression.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".