Investigating post-stroke fatigue: An individual participant data meta-analysis

Toby B. Cumming*, Ai Beng Yeo, Jodie Marquez, Leonid Churilov, Jean Marie Annoni, Umaru Badaru, Nastaran Ghotbi, Joe Harbison, Gert Kwakkel, Anners Lerdal, Roger Mills, Halvor Naess, Harald Nyland, Arlene Schmid, Wai Kwong Tang, Benjamin Tseng, Ingrid van de Port, Gillian Mead, Coralie English

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review

    Abstract

    Objective: The prevalence of post-stroke fatigue differs widely across studies, and reasons for such divergence are unclear. We aimed to collate individual data on post-stroke fatigue from multiple studies to facilitate high-powered meta-analysis, thus increasing our understanding of this complex phenomenon. Methods: We conducted an Individual Participant Data (IPD) meta-analysis on post-stroke fatigue and its associated factors. The starting point was our 2016 systematic review and meta-analysis of post-stroke fatigue prevalence, which included 24 studies that used the Fatigue Severity Scale (FSS). Study authors were asked to provide anonymised raw data on the following pre-identified variables: (i) FSS score, (ii) age, (iii) sex, (iv) time post-stroke, (v) depressive symptoms, (vi) stroke severity, (vii) disability, and (viii) stroke type. Linear regression analyses with FSS total score as the dependent variable, clustered by study, were conducted. Results: We obtained data from 14 of the 24 studies, and 12 datasets were suitable for IPD meta-analysis (total n = 2102). Higher levels of fatigue were independently associated with female sex (coeff. = 2.13, 95% CI 0.44–3.82, p = 0.023), depressive symptoms (coeff. = 7.90, 95% CI 1.76–14.04, p = 0.021), longer time since stroke (coeff. = 10.38, 95% CI 4.35–16.41, p = 0.007) and greater disability (coeff. = 4.16, 95% CI 1.52–6.81, p = 0.010). While there was no linear association between fatigue and age, a cubic relationship was identified (p < 0.001), with fatigue peaks in mid-life and the oldest old. Conclusion: Use of IPD meta-analysis gave us the power to identify novel factors associated with fatigue, such as longer time since stroke, as well as a non-linear relationship with age.

    Original languageEnglish
    Pages (from-to)107-112
    Number of pages6
    JournalJournal of Psychosomatic Research
    Volume113
    DOIs
    Publication statusPublished - 1 Oct 2018

    Keywords

    • Depression
    • Fatigue
    • Fatigue Severity Scale
    • Individual data
    • Meta-analysis
    • Stroke

    Fingerprint Dive into the research topics of 'Investigating post-stroke fatigue: An individual participant data meta-analysis'. Together they form a unique fingerprint.

    Cite this