Sleep and circadian disruption in bipolar disorders: From psychopathology to digital phenotyping in clinical practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sleep and biological rhythms are integral to mood regulation across the lifespan, particularly in bipolar disorder (BD), where alterations in sleep phase, structure, and duration occur in all mood states. These disruptions are linked to poorer quality of life, heightened suicide risk, impaired cognitive function, and increased relapse rates. This review highlights the pathophysiology of sleep disturbances in BD and aims to consolidate understanding and clinical applications of these phenomena. It also summarizes the evolution of sleep and biological rhythms assessment methods, including ecological momentary assessment (EMA) and digital phenotyping. It underscores the importance of recognizing circadian rhythm involvement in mood regulation, suggesting potential therapeutic targets. Future research directions include elucidating circadian clock gene mechanisms, understanding environmental impacts on circadian rhythms, and investigating the bidirectional relationship between sleep disturbances and mood regulation in BD. Standardizing assessment methods and addressing privacy concerns related to EMA technology and digital phenotyping are essential for advancing research. Collaborative efforts are crucial for enhancing clinical applicability and understanding the broader implications of biological rhythms in BD diagnosis and treatment. Overall, recognizing the significance of sleep and biological rhythms in BD offers promise for improved outcomes through targeted interventions and a deeper understanding of the disorder's underlying mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it