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Parameters and Measures in Assessment of Motor Learning in Neurorehabilitation; A Systematic Review of the Literature

Overview of attention for article published in Frontiers in Human Neuroscience, February 2017
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Title
Parameters and Measures in Assessment of Motor Learning in Neurorehabilitation; A Systematic Review of the Literature
Published in
Frontiers in Human Neuroscience, February 2017
DOI 10.3389/fnhum.2017.00082
Pubmed ID
Authors

Nataliya Shishov, Itshak Melzer, Simona Bar-Haim

Abstract

Upper limb function, essential for daily life, is often impaired in individuals after stroke and cerebral palsy (CP). For an improved upper limb function, learning should occur, and therefore training with motor learning principles is included in many rehabilitation interventions. Despite accurate measurement being an important aspect for examination and optimization of treatment outcomes, there are no standard algorithms for outcome measures selection. Moreover, the ability of the chosen measures to identify learning is not well established. We aimed to review and categorize the parameters and measures utilized for identification of motor learning in stroke and CP populations. PubMed, Pedro, and Web of Science databases were systematically searched between January 2000 and March 2016 for studies assessing a form of motor learning following upper extremity training using motor control measures. Thirty-two studies in persons after stroke and 10 studies in CP of any methodological quality were included. Identified outcome measures were sorted into two categories, "parameters," defined as identifying a form of learning, and "measures," as tools measuring the parameter. Review's results were organized as a narrative synthesis focusing on the outcome measures. The included studies were heterogeneous in their study designs, parameters and measures. Parameters included adaptation (n = 6), anticipatory control (n = 2), after-effects (n = 3), de-adaptation (n = 4), performance (n = 24), acquisition (n = 8), retention (n = 8), and transfer (n = 14). Despite motor learning theory's emphasis on long-lasting changes and generalization, the majority of studies did not assess the retention and transfer parameters. Underlying measures included kinematic analyses in terms of speed, geometry or both (n = 39), dynamic metrics, measures of accuracy, consistency, and coordination. There is no exclusivity of measures to a specific parameter. Many factors affect task performance and the ability to measure it-necessitating the use of several metrics to examine different features of movement and learning. Motor learning measures' applicability to clinical setting can benefit from a treatment-focused approach, currently lacking. The complexity of motor learning results in various metrics, utilized to assess its occurrence, making it difficult to synthesize findings across studies. Further research is desirable for development of an outcome measures selection algorithm, while considering the quality of such measurements.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 284 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hong Kong 1 <1%
India 1 <1%
Germany 1 <1%
Brazil 1 <1%
Unknown 280 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 49 17%
Student > Ph. D. Student 40 14%
Student > Bachelor 33 12%
Researcher 24 8%
Lecturer 16 6%
Other 61 21%
Unknown 61 21%
Readers by discipline Count As %
Nursing and Health Professions 46 16%
Neuroscience 42 15%
Medicine and Dentistry 36 13%
Engineering 25 9%
Sports and Recreations 16 6%
Other 47 17%
Unknown 72 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 April 2017.
All research outputs
#6,117,277
of 22,953,506 outputs
Outputs from Frontiers in Human Neuroscience
#2,490
of 7,179 outputs
Outputs of similar age
#99,816
of 311,628 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#74
of 197 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,179 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 311,628 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 197 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.