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Related Concept Videos

Bipolar Disorder01:30

Bipolar Disorder

Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
Mania and Antimanic Drugs: Overview01:24

Mania and Antimanic Drugs: Overview

Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as a...

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Updated: May 24, 2026

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Is Temporal Variability a Standalone Predictor in Medical Data? An Actigraphy Study in Bipolar Disorder.

Carmen-Anna Konicarová1, Jakub Schneider1,2, Marian Kolenič2,3

  • 1Czech Technical University, Faculty of Electrical Engineering, Prague, Czech Republic.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Temporal variability in actigraphy data offers valuable insights into bipolar disorder (BD) clinical states. While mean activity levels can influence variability, actigraphy

Keywords:
Medical databiomarkersgeneralized linear mixed-effects modelmean-variance couplingpower transformationskewnessvariability

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Area of Science:

  • Biomedical Engineering
  • Chronobiology
  • Psychiatry

Background:

  • Measures of central tendency and temporal variability are common biomarkers in medical data.
  • Skewed distributions in physiological data can reduce the interpretability of standard metrics like mean and standard deviation (SD).
  • Actigraphy data is increasingly used to monitor disease dynamics, but the role of temporal variability requires further investigation.

Purpose of the Study:

  • To determine if temporal variability in actigraphy data contains outcome-relevant information for bipolar disorder (BD).
  • To assess whether the predictive capacity of temporal variability is confounded by mean activity levels in BD patients.

Main Methods:

  • Analysis of actigraphy recordings from 326 individuals with BD.
  • Statistical modeling using a subset of 34 participants with both manic and remission periods.
  • Power transformation (Box-Cox, Yeo-Johnson) of daily features, aggregated weekly into mean (μ7) and SD (σ7).
  • Mixed-effects logistic regression models to differentiate manic from remission weeks.

Main Results:

  • Power transformations decreased the correlation between mean (μ7) and SD (σ7) of activity levels.
  • Temporal variability (σ7) retained significant outcome-relevant predictive information for most actigraphy features.
  • For a subset of features (27%), the predictive impact of variability was partially explained by mean-variance coupling.

Conclusions:

  • Temporal variability in actigraphy data provides valuable predictive information for clinical states in bipolar disorder.
  • While mean activity levels can influence variability, temporal variability remains a significant independent predictor for most features.
  • Further research into mean-variance coupling is warranted to fully understand actigraphy-based biomarkers in BD.