| Title: |
Rest-Activity Rhythms During Clinical Episodes of Bipolar Disorder: Disruptions in Mean Levels, Temporal Variability, and Multivariate Structure |
| Authors: |
Konicarová, Carmen-Anna; Schneider, Jakub; Španiel, Filip; Alda, Martin; Bakštein, Eduard |
| Publisher Information: |
Zenodo |
| Publication Year: |
2025 |
| Collection: |
Zenodo |
| Subject Terms: |
Bipolar Disorder; Machine Learning; long-term study; rest-activity rhythms; actigraphy; mean; variability; correlation |
| Description: |
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, ... |
| Document Type: |
report |
| Language: |
unknown |
| Relation: |
https://zenodo.org/communities/brady/; https://zenodo.org/records/15879387; oai:zenodo.org:15879387; https://doi.org/10.5281/zenodo.15879387 |
| DOI: |
10.5281/zenodo.15879387 |
| Availability: |
https://doi.org/10.5281/zenodo.15879387; https://zenodo.org/records/15879387 |
| Rights: |
Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Accession Number: |
edsbas.706410F6 |
| Database: |
BASE |