Brainstorming Session I‐Actigraphy in bipolar disorders: Which parameters and which analyses? Chair: Benicio Frey
Bibliographic record
Abstract
Through the use of actigraphy, sleep-wake cycle and biological rhythm patterns have been increasingly investigated in mood disorders, such as bipolar disorders (BD).Increasingly, attention has been directed toward studying actigraphy-derived variables beyond sleep, particularly in BD and other mood disorders.Emerging methods of analyzing actigraphy data include investigating probabilities of transitioning between rest and active states, and investigating variables quantifying light exposure, which is measured by some actigraph models.To highlight the relevance of these variables to mood disorders, we will present results from two studies, which looked at a broad range of actigraphy measures, including light exposure and transition probabilities.The first study investigated differences in actigraphy and questionnaire-derived sleep and biological rhythms variables between diagnoses, and their influence on quality of life and functioning in individuals with BD, major depressive disorder and healthy controls(n = 111).Findings revealed that increased probability of transitioning from activity to rest during the day was related to better quality of life, and lower functional impairment in linear regression models.Individuals with mood disorders also had later timing of light exposure over 1000 lux.The second study investigated the impact of actigraphy-derived variables and clinical variables during pregnancy on postpartum depression in a longitudinal cohort of women during the perinatal period(n = 79).Severity of depressive symptoms at 6-12 weeks postpartum was linked to timing of light exposure over 100 and 500 lux during pregnancy, among other actigraphy-derived variables in a linear regression model.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.046 |
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".