Literature DB >> 20159136

Monitoring of physical activity after stroke: a systematic review of accelerometry-based measures.

Nick Gebruers1, Christel Vanroy, Steven Truijen, Sebastiaan Engelborghs, Peter P De Deyn.   

Abstract

OBJECTIVE: To assess the clinimetric properties and clinical applicability of different accelerometry-based measurement techniques in persons with stroke. DATA SOURCES: A systematic search of literature was performed using a specific search strategy by means of different electronic databases until October 2008 (PubMed, EMBASE, CINAHL, Cochrane Library of Clinical Trials). STUDY SELECTION: A first selection was made by means of title and abstract. A second selection was performed by means of predefined inclusion criteria: (1) accelerometry in stroke population, (2) application of accelerometry in patients with stroke including clinimetric properties. The exclusion criteria were (1) dysphagia, (2) new engineering techniques or software alterations, (3) secondary sources, and (4) Case studies. DATA EXTRACTION: The clinimetric properties and applicability of accelerometry were described based on the included publications. DATA SYNTHESIS: Twenty-five articles (4 randomized controlled trials) were included. The information of the publications was divided into (1) gait, cadence, and ambulatory activity; (2) upper-extremity activity; and (3) topics related to stroke other than upper or lower extremity. Accelerometry was shown to be valid and had good test-retest reliability in a large number of settings. Numerous studies demonstrated correlations between accelerometry and common stroke scales. Trunk movements were measured as an outcome of disturbed gait. The vertical asymmetry index especially was able to differentiate between persons with stroke and healthy controls. Persons with stroke showed less ambulatory activity, measured as steps per day, than sedentary controls. Triaxial accelerometry was able to distinguish between varying activity levels. Upper-extremity use was lesser in persons with stroke. It was impossible to calculate a minimal clinical difference for arm use by a uniaxial accelerometer. Evidence was presented that finger-tapping and sit-to-stand measured by accelerometers could be used to define recovery from stroke.
CONCLUSIONS: The literature concerning accelerometry incorporated into stroke research is young, limiting the ability to draw consistent conclusions. Nonetheless, the available evidence suggests that accelerometers yield valid and reliable data about the physical activity of patients with stroke. Future research is necessary to investigate clinimetric properties like predictive value and responsiveness further before implementing accelerometry in clinical trials as an outcome for change. Copyright 2010 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20159136     DOI: 10.1016/j.apmr.2009.10.025

Source DB:  PubMed          Journal:  Arch Phys Med Rehabil        ISSN: 0003-9993            Impact factor:   3.966


  77 in total

1.  Identifying activity levels and steps of people with stroke using a novel shoe-based sensor.

Authors:  George D Fulk; S Ryan Edgar; Rebecca Bierwirth; Phil Hart; Paulo Lopez-Meyer; Edward Sazonov
Journal:  J Neurol Phys Ther       Date:  2012-06       Impact factor: 3.649

2.  Sampling frequency impacts measurement of walking activity after stroke.

Authors:  Brian Knarr; Margaret A Roos; Darcy S Reisman
Journal:  J Rehabil Res Dev       Date:  2013

3.  Efficacy of computer-assisted, 3D motion-capture toothbrushing instruction.

Authors:  Kee-Deog Kim; Jin-Sun Jeong; Hae Na Lee; Yu Gu; Kyeong-Seop Kim; Jeong-Whan Lee; Wonse Park
Journal:  Clin Oral Investig       Date:  2014-11-14       Impact factor: 3.573

4.  Dynamic bimanual force control in chronic stroke: contribution of non-paretic and paretic hands.

Authors:  Prakruti Patel; Neha Lodha
Journal:  Exp Brain Res       Date:  2019-06-13       Impact factor: 1.972

5.  Using sensors to measure activity in people with stroke.

Authors:  George D Fulk; Edward Sazonov
Journal:  Top Stroke Rehabil       Date:  2011 Nov-Dec       Impact factor: 2.119

6.  Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation.

Authors:  Yoon-Hee Choi; Nam-Jong Paik
Journal:  J Vis Exp       Date:  2018-03-08       Impact factor: 1.355

Review 7.  Advancing measurement of locomotor rehabilitation outcomes to optimize interventions and differentiate between recovery versus compensation.

Authors:  Mark G Bowden; Andrea L Behrman; Michelle Woodbury; Chris M Gregory; Craig A Velozo; Steven A Kautz
Journal:  J Neurol Phys Ther       Date:  2012-03       Impact factor: 3.649

8.  Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice.

Authors:  Jenna Blumenthal; Andrea Wilkinson; Mark Chignell
Journal:  Physiother Can       Date:  2018       Impact factor: 1.037

9.  Changes in spontaneous activity assessed by accelerometry correlate with extent of cerebral ischemia-reperfusion injury in the nonhuman primate.

Authors:  Henryk F Urbanski; Steven G Kohama; G Alexander West; Christine Glynn; Rebecca L Williams-Karnesky; Eric Earl; Martha N Neuringer; Lauren Renner; Alison Weiss; Mary Stenzel-Poore; Frances Rena Bahjat
Journal:  Transl Stroke Res       Date:  2012-12       Impact factor: 6.829

10.  Relating wrist accelerometry measures to disability in older adults.

Authors:  Megan J Huisingh-Scheetz; Masha Kocherginsky; Elizabeth Magett; Patricia Rush; William Dale; Linda Waite
Journal:  Arch Gerontol Geriatr       Date:  2015-09-16       Impact factor: 3.250

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