Toby B Cumming1, Ai Beng Yeo2, Jodie Marquez2, Leonid Churilov3, Jean-Marie Annoni4, Umaru Badaru5, Nastaran Ghotbi6, Joe Harbison7, Gert Kwakkel8, Anners Lerdal9, Roger Mills10, Halvor Naess11, Harald Nyland12, Arlene Schmid13, Wai Kwong Tang14, Benjamin Tseng15, Ingrid van de Port16, Gillian Mead17, Coralie English18. 1. Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia; Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery, Australia. Electronic address: toby.cumming@florey.edu.au. 2. School of Health Sciences and Priority Research Centre for Stroke and Brain Injury, University of Newcastle, Callaghan, Australia. 3. Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia. 4. Department of Medicine, University of Fribourg, Switzerland. 5. Department of Physiotherapy, Bayero University, Kano, Nigeria. 6. Department of Physiotherapy, Tehran University of Medical Sciences, Iran. 7. Department of Medical Gerontology, Trinity College Dublin, Ireland. 8. Department of Rehabilitation Medicine, VU University, Amsterdam, the Netherlands. 9. Department of Nursing Science, University of Oslo, Norway. 10. Department of Neurology, Royal Preston Hospital, UK. 11. Department of Neurology, Haukeland University Hospital, Norway. 12. Institute of Clinical Medicine, University of Bergen, Norway. 13. Department of Occupational Therapy, Colorado State University, Fort Collins, USA. 14. Department of Psychiatry, Chinese University of, Hong Kong. 15. Department of Health & Kinesiology, University of Texas, Tyler, USA. 16. Revant Rehabilitation Centres, Breda, the Netherlands. 17. Centre for Clinical Brain Sciences, University of Edinburgh, UK. 18. Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery, Australia; School of Health Sciences and Priority Research Centre for Stroke and Brain Injury, University of Newcastle, Callaghan, Australia.
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.
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.
Authors: Kristine M Ulrichsen; Knut K Kolskår; Geneviève Richard; Mads Lund Pedersen; Dag Alnaes; Erlend S Dørum; Anne-Marthe Sanders; Sveinung Tornås; Luigi A Maglanoc; Andreas Engvig; Hege Ihle-Hansen; Jan E Nordvik; Lars T Westlye Journal: Brain Behav Date: 2022-06-06 Impact factor: 3.405