An Exploration of Sedentary Behavior Patterns in Community-Dwelling People With Stroke: A Cluster-Based Analysis.

J Neurol Phys Ther

Department of Rehabilitation, Physiotherapy Science & Sport, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (W.H., M.F.P.); School of Health Sciences and Priority Research Centre for Stroke and Brain Injury, Faculty of Health and Medicine, University of Newcastle, Callaghan, Australia (W.H., D.B.S., C.E.); Center for Physical Therapy Research and Innovation in Primary Care, Julius Health Care Centers, Utrecht, the Netherlands (W.H., M.F.P.); Bioinformatics, Hunter Medical Research Institute, and School of Medicine and Public Health, University of Newcastle, Newcastle, Australia (C.R.); Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU, Norwegian University of Science and Technology, Trondheim, Norway (T.A.); Department of Rehabilitation Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands (J.B.J.B.); Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia (M.L.C.); School of Health and Life Sciences, Glasgow Caledonian University, Glasgow, United Kingdom (S.F.M.C., L.P., Z.T.); Department of Movement and Sports Sciences, Faculty of Medicine and Health Science, Ghent University, Ghent, Belgium (S.F.M.C.); Department of Health Professions, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia (C.D., T.M.J.); Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Canada (V.E., P.J.M.); National Head, School of Allied Health, Faculty of Health Sciences, Australian Catholic University, Brisbane, Australia (S.S.K.); Discipline of Physiotherapy, Faculty of Health, University of Canberra, Canberra, Australia (N.M.); Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom (G.M.); Stroke Research Group, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom (S.A.M.); Department of Health Innovations and Technology, Fontys University of Applied Sciences, Eindhoven, the Netherlands (M.F.P.); Physical Activity for Health Research Centre, Institute for Sport, Physical Education and Health Sciences, University of Edinburgh, Edinburgh, United Kingdom (D.H.S.); Department of Geriatric Medicine, University of Edinburgh, United Kingdom (Z.T.); UMC Utrecht Brain Center, Center of Excellence for Rehabilitation Medicine, De Hoogstraat Rehabilitation, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (O.V.); and Centre for Research Excellence in Stroke Recovery and Rehabilitation, Florey Institute of Neuroscience and Hunter Medical Research Institute, Newcastle, Australia (C.E.).

Published: July 2021

Background And Purpose: Long periods of daily sedentary time, particularly accumulated in long uninterrupted bouts, are a risk factor for cardiovascular disease. People with stroke are at high risk of recurrent events and prolonged sedentary time may increase this risk. We aimed to explore how people with stroke distribute their periods of sedentary behavior, which factors influence this distribution, and whether sedentary behavior clusters can be distinguished?

Methods: This was a secondary analysis of original accelerometry data from adults with stroke living in the community. We conducted data-driven clustering analyses to identify unique accumulation patterns of sedentary time across participants, followed by multinomial logistical regression to determine the association between the clusters, and the total amount of sedentary time, age, gender, body mass index (BMI), walking speed, and wake time.

Results: Participants in the highest quartile of total sedentary time accumulated a significantly higher proportion of their sedentary time in prolonged bouts (P < 0.001). Six unique accumulation patterns were identified, all of which were characterized by high sedentary time. Total sedentary time, age, gender, BMI, and walking speed were significantly associated with the probability of a person being in a specific accumulation pattern cluster, P < 0.001 - P = 0.002.

Discussion And Conclusions: Although unique accumulation patterns were identified, there is not just one accumulation pattern for high sedentary time. This suggests that interventions to reduce sedentary time must be individually tailored.Video Abstract available for more insight from the authors (see the Video Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A343).

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Source
http://dx.doi.org/10.1097/NPT.0000000000000357DOI Listing

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