Rest-Activity Rhythms During Clinical Episodes of Bipolar Disorder: Disruptions in Mean Levels, Temporal Variability, and Multivariate Structure
Bibliographic record
Abstract
Mood episodes in bipolar disorders (BD) are typically associated with changes in sleep and activity patterns. In this study, we present a novel correlation-based approach to examine the structure of relationships between actigraphy-derived variables and clinical status in individuals with BD. Using a large-scale longitudinal study spanning over 2 years of actigraphic recordings from 115 patients with bipolar disorder (BD), we compared three aggregation approaches: mean values, temporal variability, and inter-feature correlation structure, to classify mood states in a strict validation scenario and predict future episodes. Two binary classification subsets (mania–remission and depression–remission) were constructed based on automatically classified labels using clinician-rated scales (Montgomery-Åsberg Depression Rating Scale and Young Mania Rating Scale) and weekly self-assessments. Support Vector Machine models with Radial Basis Function kernels were trained using 7-day windows and forward feature selection in a nested 5-fold cross-validation setup. The classification model achieved statistically significant balanced accuracy: 58% (p < 0.05) for mania–remission and 59% (p < 0.001) for depression–remission. For mania, the most predictive aggregates were shorter mean sleep duration and deviations in 10-hour peak activity across different weeks, while for depression, increased inter-daily variability and intra-daily activity fluctuations emerged as key indicators. The models differentiated future episodes from remission solely from actigraphy data with above-chance accuracy, revealing distinct behavioral signatures for mania and depression. While structural changes in correlation patterns between features differed across mood states, they did not outperform mean or variability-based metrics in classification performance. However, modest classification performance and high inter-individual variability suggest that personalized modeling approaches may be essential for clinically meaningful prediction. Notably, inter- and intra-weekly feature changes provided the strongest predictive signals, suggesting that straightforward alterations in rest-activity rhythms may better reflect clinical episodes than more complex structural metrics.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".