Sleep and mental health independently affect cognitive performance in university students
Bibliographic record
Abstract
Abstract Objectives Young adults experience the highest rates of mental health disorders of any age group. Given that mental health disorders are associated with sleep disturbances and cognitive impairments, we investigated whether sleep moderates the effects on cognition. Methods University students (N=89; aged 18-30 years) remotely monitored their sleep for seven consecutive days using wrist actigraphy and sleep diaries. On day seven, participants completed cognitive testing and mental health questionnaires. Cognitive tests included the Psychomotor Vigilance Task (PVT), Cambridge Neuropsychological Test Automated Battery (CANTAB), and the Mnemonic Similarity Task (MST). CANTAB’s Delayed Matching to Sample (DMS) and MST are designed to tax pattern separation, a computational mechanism supporting encoding of similar experiences as distinct representations. Beck’s Depression Inventory and Beck’s Anxiety Inventory assessed mental health. Results Eighty participants (mean age: 20.13±2.00) were included in the analyses. Most participants reported mild to severe depressive and anxiety symptoms. Depressive symptoms were correlated with wake-up time (ρ =. 35, p=. 002) as well as PVT (ρ =. 26, p=. 02) and DMS (ρ =. 24, p= .04) performance. Bedtime was correlated with performance on MST ( r=-. 29, r=. 02) and DMS (ρ =. 25, p=. 03), while wake-up time was correlated with performance on MST ( r=- . 31, p=. 01) and DMS (ρ =. 28, p=. 01). Sleep did not moderate the effects of mental health on cognitive performance. Conclusion Cognitive tests taxing pattern separation are sensitive to depressive symptoms and sleep timing. While students face a disproportionate burden of mental health disorders compromising cognitive functioning, improving sleep quality may offer a partial, though not moderating, pathway to alleviating these cognitive impairments.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".