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Use It and Improve It or Lose It: Interactions between Arm Function and Use in Humans Post-stroke

Overview of attention for article published in PLoS Computational Biology, February 2012
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Title
Use It and Improve It or Lose It: Interactions between Arm Function and Use in Humans Post-stroke
Published in
PLoS Computational Biology, February 2012
DOI 10.1371/journal.pcbi.1002343
Pubmed ID
Authors

Yukikazu Hidaka, Cheol E. Han, Steven L. Wolf, Carolee J. Winstein, Nicolas Schweighofer

Abstract

"Use it and improve it, or lose it" is one of the axioms of motor therapy after stroke. There is, however, little understanding of the interactions between arm function and use in humans post-stroke. Here, we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy (CIMT) in the EXCITE clinical trial. Using a Bayesian regression framework, we systematically compared this model with competitive models that included, or not, interactions between function and use. Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model. Furthermore, by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization, we found that therapy increased the parameter that controls the effect of arm function on arm use. Increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, lead to increase in spontaneous use after therapy compared to before therapy.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 205 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
Japan 1 <1%
United Kingdom 1 <1%
India 1 <1%
Unknown 198 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 31 15%
Student > Ph. D. Student 30 15%
Student > Bachelor 27 13%
Researcher 21 10%
Professor 12 6%
Other 41 20%
Unknown 43 21%
Readers by discipline Count As %
Medicine and Dentistry 34 17%
Neuroscience 27 13%
Engineering 27 13%
Nursing and Health Professions 25 12%
Agricultural and Biological Sciences 12 6%
Other 28 14%
Unknown 52 25%