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Introducing Actigraphic Data Analyzer (ADA), an open source software for sleep/wake scoring and circadian rhythm

Piotr Biegański1, Monika Tutaj2, Anna Duszyk-Bogorodzka3

  • 1Faculty of Physics, Biomedical Physics Division, University of Warsaw, 5 Pasteura st., 02-093, Warsaw, Poland. pbieganski@fuw.edu.pl.

Scientific Reports
|July 1, 2026
PubMed
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We developed Actigraphic Data Analyser (ADA), an open-source Python package for analyzing sleep and circadian rhythms from actigraphy devices. ADA reveals distinct groups of circadian rhythm descriptors related to period length and rhythm strength.

Area of Science:

  • Chronobiology
  • Biomedical Data Analysis
  • Software Development

Background:

  • Actigraphy is crucial for objective sleep and circadian rhythm assessment.
  • Existing software for actigraphy data analysis can be limited in scope or accessibility.
  • Standardized analysis of circadian rhythm descriptors is needed.

Purpose of the Study:

  • Introduce Actigraphic Data Analyser (ADA), a user-friendly, open-source Python package.
  • Provide comprehensive tools for sleep and circadian rhythm analysis from raw actigraphy data.
  • Facilitate the exploration of correlations between various circadian rhythm descriptors.

Main Methods:

  • Developed ADA with a graphical user interface (GUI) and Python library functionality.
  • Integrated data import from GENEActiv, ActiGraph devices, and the MESA dataset.

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  • Implemented multiple epoch collapsing methods, sleep/wake scoring algorithms (including Universal Filter Approach), and circadian rhythm analysis tools.
  • Utilized a Python script to analyze correlations between circadian rhythm descriptors (DFA, cosinor, AR model spectrum, IS, IV, M10, L5) on a freely available dataset.

    • Main Results:

    • ADA processes raw actigraphy data, offering versatile sleep/wake scoring and circadian rhythm analysis.
    • The analysis of 87 weekly recordings revealed two distinct groups of circadian rhythm descriptors.
    • One group correlates with circadian period length, while the other relates to the strength of the 24-hour rhythm.

    Conclusions:

    • Actigraphic Data Analyser (ADA) provides a powerful, accessible platform for sleep and circadian rhythm research.
    • The study highlights a novel grouping of circadian rhythm descriptors based on their relationship to period length and rhythm strength.
    • Both ADA and the associated dataset are freely available, promoting open science in chronobiology.