Elevated Hair Cortisol Concentrations Are Associated With Poor Sleep Quality Evaluated Using the Pittsburgh Sleep Quality Index but Not With Actigraphy
Bibliographic record
Abstract
There is growing interest in studying how habitual sleep disturbance affects biological risk factors that may underscore adverse health outcomes. This study examined associations between hair cortisol concentrations and self-reported sleep quality and objectively measured sleep metrics derived using actigraphy. Data were collected from 306 female and 177 male adults, aged 18-79 years. Hair cortisol was analysed from 3-cm proximal hair segments from the head to represent cortisol accumulation over approximately 90 days. Sleep quality measures included Pittsburgh Sleep Quality Index (PSQI) scores and five actigraphy-derived metrics: sleep latency, total sleep time, wake after sleep onset, sleep efficiency and awakening bouts. In the fully adjusted multiple regression model, higher hair cortisol concentrations were associated with poor self-reported sleep quality (i.e., PSQI > 5; p = 0.020), and higher mean PSQI scores (p = 0.027). No significant relationships were observed with actigraphy-derived sleep measures. The findings support hair cortisol as a promising biomarker for evaluating chronic stress that often coincides with self-reported sleep disturbance. The results suggest the importance of aligning time reference periods for biomarker and self-reported outcomes and highlight the need for further research to reconcile discrepancies between subjective and objective sleep measures.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".