Temporal relation between sleep and mood in patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Early recognition of the prodromal symptoms of bipolar disorder, combined with a patient action plan, may help to prevent relapses. Sleep disturbances are frequent warning signs of both mania and depression. This study used cross correlation analysis to characterize the relationship between mood, sleep and bedrest in longitudinal data. METHODS: Self-reported mood, sleep and bedrest (mean 169 +/- 59 days of data per patient) from 59 outpatients with bipolar disorder receiving standard treatment were analyzed. The cross correlation function was used to determine the latency between the changes in sleep and/or bedrest and mood for time shifts of between -7 and 7 days. RESULTS: For sleep and/or bedrest, a significant inverse correlation was found with the change in mood, most commonly with a time latency of one day. Sleep plus bedrest had the strongest relationship with a change in mood, with a significant correlation in 24 of 59 patients (41%) for the night before or night of a mood change. The patients with a significant cross-correlation between mood and sleep plus bedrest reported about two thirds of all large sleep changes of >3 h and three fourths of all large mood changes (>20 on 100-unit scale). Patients with a significant cross correlation were more likely to take benzodiazepines. CONCLUSION: In most patients with a significant cross correlation between sleep and/or bedrest and mood, the mood change occurred on the day following the change in sleep and/or bedrest. Sleep changes from a previous pattern, especially those of more than 3 h, may indicate that a large mood change is imminent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".