Literature DB >> 12617381

Intensive, time-series measurement of upper limb recovery in the subacute phase following stroke.

Nicola Goodwin1, Alan Sunderland.   

Abstract

OBJECTIVES: To discover if intensive monitoring of wrist extension would produce consistent recovery curves during the subacute period, and whether any impact of additional physiotherapy could be detected. We also investigated improved approaches to statistical analysis in single-case experiments.
DESIGN: A randomized multiple-baseline experiment with very frequent assessment.
SETTING: Stroke rehabilitation unit.
SUBJECTS: Four patients with some active wrist movement less than seven weeks after stroke.
INTERVENTIONS: Wrist extension was measured twice daily with an electrogoniometer for 3-4 weeks. Additional upper limb physiotherapy 115 minutes, twice per day) commenced after a randomly determined period. MAIN OUTCOME MEASURES: Speed and range of wrist movement.
RESULTS: A logarithmic function was fitted to the data to produce recovery curves. In all cases, active range and maximum velocity of wrist extension rose gradually over time. Mean variability in range was <5%, but with occasional outliers. Range of passive movement decreased in two cases in association with pain and increased tone. There were no large improvements coinciding with additional physiotherapy but autoregression analysis indicated statistically significant changes in three cases. A randomization test confirmed an increase in active range associated with additional physiotherapy.
CONCLUSIONS: Intensive electrogoniometry provided a detailed recovery pattern for each of these patients. The data were surprisingly consistent over time, showing that it is feasible to use a time-series approach to investigate subacute recovery. Changes associated with additional physiotherapy were observed on some measures, demonstrating the potential of this approach for exploratory evaluation of interventions.

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Mesh:

Year:  2003        PMID: 12617381     DOI: 10.1191/0269215503cr571oa

Source DB:  PubMed          Journal:  Clin Rehabil        ISSN: 0269-2155            Impact factor:   3.477


  3 in total

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Review 2.  Position-sensing technologies for movement analysis in stroke rehabilitation.

Authors:  H Zheng; N D Black; N D Harris
Journal:  Med Biol Eng Comput       Date:  2005-07       Impact factor: 2.602

3.  Predicting recovery of cognitive function soon after stroke: differential modeling of logarithmic and linear regression.

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Journal:  PLoS One       Date:  2013-01-11       Impact factor: 3.240

  3 in total

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