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Lookup NU author(s): Dr Sarah CharmanORCiD, Dr Renae StefanettiORCiD, Dr Cecilia Jimenez MorenoORCiD, Professor Grainne Gorman
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 Weinans et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. There is a growing interest to analyze physiological data from a complex systems perspective. Accelerometer data is one type of data that is easy to obtain but often difficult to analyze for insights beyond basic levels of description. Previous work hypothesizes that an individual’s activity pattern can be seen as a complex dynamical system. Here, we explore this hypothesis further by investigating whether complexity-based measures quantifying repetitiveness and fragmentation of activity captured via accelerometer can detect health differences beyond traditional measures. Our results demonstrate that healthy individuals have a higher regularity (indicated by a lower correlation dimension), a higher probability of activity after a period of rest, and a lower probability of a period of rest after a period of activity compared with patients living with Myotonic Dystrophy type I (DM1), a chronic, progressive, complex, multisystem disease. For the correlation dimension, this difference was independent of the average, coefficient of variation and autocorrelation of the activity signals. This suggests that the correlation dimension can extract clinically relevant information from accelerometer data. Therefore, our results corroborate the idea that a complexity perspective may help to reveal the emergent characteristics of health and disease.
Author(s): Weinans E, Rector JL, Charman S, Stefanetti RJ, Jimenez-Moreno C, Gorman GS, van de Leemput I, van As D, Melis R, van Engelen B
Publication type: Article
Publication status: Published
Journal: PLoS ONE
Year: 2025
Volume: 20
Issue: 7
Online publication date: 09/07/2025
Acceptance date: 30/05/2025
Date deposited: 21/07/2025
ISSN (electronic): 1932-6203
Publisher: Public Library of Science
URL: https://doi.org/10.1371/journal.pone.0326522
DOI: 10.1371/journal.pone.0326522
Data Access Statement: The raw accelerometer data and the code to reproduce the figures is available from https://github.com/elsweinans/optimistic_data_code.
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