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Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, April 2018
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Citations

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228 Mendeley
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Title
Advanced Robotic Therapy Integrated Centers (ARTIC): an international collaboration facilitating the application of rehabilitation technologies
Published in
Journal of NeuroEngineering and Rehabilitation, April 2018
DOI 10.1186/s12984-018-0366-y
Pubmed ID
Authors

Hubertus J. A. van Hedel, Giacomo Severini, Alessandra Scarton, Anne O’Brien, Tamsin Reed, Deborah Gaebler-Spira, Tara Egan, Andreas Meyer-Heim, Judith Graser, Karen Chua, Daniel Zutter, Raoul Schweinfurther, J. Carsten Möller, Liliana P. Paredes, Alberto Esquenazi, Steffen Berweck, Sebastian Schroeder, Birgit Warken, Anne Chan, Amber Devers, Jakub Petioky, Nam-Jong Paik, Won-Seok Kim, Paolo Bonato, Michael Boninger, for the ARTIC network

Abstract

The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 228 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 228 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 31 14%
Researcher 26 11%
Student > Master 26 11%
Student > Ph. D. Student 20 9%
Other 12 5%
Other 35 15%
Unknown 78 34%
Readers by discipline Count As %
Nursing and Health Professions 49 21%
Medicine and Dentistry 36 16%
Engineering 15 7%
Neuroscience 12 5%
Psychology 8 4%
Other 23 10%
Unknown 85 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 May 2018.
All research outputs
#7,216,349
of 23,041,514 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#453
of 1,293 outputs
Outputs of similar age
#125,909
of 329,529 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#11
of 31 outputs
Altmetric has tracked 23,041,514 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,293 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. 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 329,529 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 61% of its contemporaries.
We're also able to compare this research output to 31 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 64% of its contemporaries.