Rest-Activity Rhythm Variability Across Clinical Episodes of Bipolar Disorder: Standalone Biomarker or Statistical Artifact?

Background

Actigraphy-derived rest–activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data.

Objective

In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean–variance dependence.

Methods

We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box–Cox or Yeo–Johnson) were applied to the entire study cohort to reduce mean–variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC).

Results

Transformations reduced mean–absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%–4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50–0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (±1 hour), did not improve discrimination between mania and remission.

Conclusions

Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

Key strengths

• Large-scale longitudinal design: The analysis leverages 76,672 days of actigraphy data from 326 participants with BD, enabling robust estimation of within-participant dynamics and increasing statistical power. Analysis sample sizes were smaller for specific contrasts (mania–remission: 34 participants; depression–remission: 58 participants), reflecting the longitudinal nature of bipolar disorder (BD), sparseness of episodes, and the challenges of acquiring dense actigraphy data in clinical populations.

• Direct enumeration of mean–variance coupling: The study explicitly quantifies and mitigates mean–variance dependence using feature-wise variance-stabilizing transformations, addressing a critical but often neglected methodological issue, which has broader biomedical applications.

• Temporal variability is a standalone state marker in BD: After transformation, temporal variability was significantly associated with clinical state in the majority of features (mania: 11/22; depression: 16/22), indicating that these effects were not solely attributable to statistical coupling with the mean, and that variability of RAR is an inherent biomarker.