Biological rhythms are independently associated with quality of life in bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that patients with bipolar disorder (BD) experience biological rhythm disturbances; however, no studies have examined the impact of this disruption on quality of life (QOL). The aim of this study is to investigate the influence of biological rhythm, depressive symptoms, sleep quality, and sleep medication use on QOL in BD. METHODS: Eighty BD subjects (44 depressed and 36 euthymic) completed questionnaires assessing QOL (WHOQOL-BREF), biological rhythm disruption (BRIAN), depressive symptoms (MADRS), and sleep quality (PSQI). The impact of biological rhythm disturbance, depressive symptoms severity, sleep quality, and sleep medication use on QOL was determined with multiple regression analyses. RESULTS: BRIAN (β = -0.31, t = -2.73, p < 0.01), MADRS (β = -0.30, t = -2.93, p < 0.01), and sleep medication use (β = -0.45, t = -2.55, p < 0.05) were significant predictors of QOL in this model (F 4, 75 = 20.28; p < 0.0001). The relationship of these factors with subdomains of QOL showed that poorer social QOL was associated with greater biological rhythm disturbance (β = -0.43, t = -3.66, p < 0.01) and sleep medication use (β = -0.49, t = -2.35, p < 0.01), providing support for the social rhythm theory of BD. Physical QOL was associated with depression (β = -0.30, t = -2.93, p < 0.01) and biological rhythm disruption (β = -0.31, t = -2.73, p < 0.01). Main limitations include the cross-sectional assessment and the lack of objective measures of biological rhythms in relation to QOL. CONCLUSIONS: Disruption in biological rhythm is associated with poor QOL in BD, independent of sleep disturbance, sleep medication use, and severity of depression. Treatment strategies targeting regulation of biological rhythms, such as sleep/wake cycles, eating patterns, activities, and social rhythms, are likely to improve QOL 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".