Evidence map›Paper›PMID 38566157›Full record

ArticleBioPsychoSocial medicine2024

Time-dependent complexity characterisation of activity patterns in patients with Chronic Fatigue Syndrome.

Paloma Rabaey, Peter Decat, Stefan Heytens, Dirk Vogelaers, An Mariman, Thomas Demeester

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Article in BioPsychoSocial medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Paloma RabaeyIDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium. paloma.rabaey@ugent.be.ORCID http://orcid.org/0000-0001-6064-0788
Peter DecatDepartment of Public Health and Primary Care, Ghent University, Ghent, Belgium.
Stefan HeytensDepartment of Public Health and Primary Care, Ghent University, Ghent, Belgium.
Dirk VogelaersCenter of Integrative Medicine, Department of Physical Medicine and Rehabilitation, University Hospital Ghent, Ghent, Belgium.
An MarimanCenter of Integrative Medicine, Department of Physical Medicine and Rehabilitation, University Hospital Ghent, Ghent, Belgium.
Thomas DemeesterIDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium.

Funding

Fonds Wetenschappelijk Onderzoek 170122N
6 · The paper itself

Abstract

backgroundChronic Fatigue Syndrome patients suffer from symptoms that cannot be explained by a single underlying biological cause. It is sometimes claimed that these symptoms are a manifestation of a disrupted autonomic nervous system. Prior works studying this claim from the complex adaptive systems perspective, have observed a lower average complexity of physical activity patterns in chronic fatigue syndrome patients compared to healthy controls. To further study the robustness of such methods, we investigate the within-patient changes in complexity of activity over time. Furthermore, we explore how these changes might be related to changes in patient functioning.

methodsWe propose an extension of the allometric aggregation method, which characterises the complexity of a physiological signal by quantifying the evolution of its fractal dimension. We use it to investigate the temporal variations in within-patient complexity. To this end, physical activity patterns of 7 patients diagnosed with chronic fatigue syndrome were recorded over a period of 3 weeks. These recordings are accompanied by physicians' judgements in terms of the patients' weekly functioning.

resultsWe report significant within-patient variations in complexity over time. The obtained metrics are shown to depend on the range of timescales for which these are evaluated. We were unable to establish a consistent link between complexity and functioning on a week-by-week basis for the majority of the patients.

conclusionsThe considerable within-patient variations of the fractal dimension across scales and time force us to question the utility of previous studies that characterise long-term activity signals using a single static complexity metric. The complexity of a Chronic Fatigue Syndrome patient's physical activity signal does not suffice to characterise their high-level functioning over time and has limited potential as an objective monitoring metric by itself.

Indexed as

Activity patternsChronic Fatigue SyndromeComplex adaptive systemsFractal dimensionPersonalised monitoringTime-dependent complexity

Identifiers

PMID38566157
PMCPMC10986074

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