Evidence map›Paper›PMID 36468121›Full record

ArticleHeliyon2022

The effect of gamified robot-enhanced training on motor performance in chronic stroke survivors.

Arzu Guneysu Ozgur, Maximilian J Wessel, Jennifer K Olsen, Andéol Geoffroy Cadic-Melchior, Valérie Zufferey, Wafa Johal, Giulia Dominijanni, Jean-Luc Turlan, Andreas Mühl, Barbara Bruno and 3 more

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 4 countries.

Arzu Guneysu OzgurComputer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Maximilian J WesselDefitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Jennifer K OlsenUniversity of San Diego, San Diego, CA, USA.
Andéol Geoffroy Cadic-MelchiorDefitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Valérie ZuffereyDefitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Wafa JohalSchool of Computing and Information Systems, University of Melbourne, Victoria, Australia.
Giulia DominijanniBertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and School of Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland.
Jean-Luc TurlanNeurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland.
Andreas MühlNeurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland.
Barbara BrunoComputer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Philippe VuadensNeurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland.
Pierre DillenbourgComputer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Friedhelm C HummelDefitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
Clinique Romande de Réadaptation · CHHuman Computer Interaction (Switzerland) · CHÉcole Polytechnique Fédérale de Lausanne · CHThe University of Melbourne · AUUniversity of Geneva · CHUniversity of San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Task-specific training constitutes a core element for evidence-based rehabilitation strategies targeted at improving upper extremity activity after stroke. Its combination with additional treatment strategies and neurotechnology-based solutions could further improve patients' outcomes. Here, we studied the effect of gamified robot-assisted upper limb motor training on motor performance, skill learning, and transfer with respect to a non-gamified control condition with a group of chronic stroke survivors. The results suggest that a gamified training strategy results in more controlled motor performance during the training phase, which is characterized by a higher accuracy (lower deviance), higher smoothness (lower jerk), but slower speed. The responder analyses indicated that mildly impaired patients benefited most from the gamification approach. In conclusion, gamified robot-assisted motor training, which is personalized to the individual capabilities of a patient, constitutes a promising investigational strategy for further improving motor performance after a stroke.

Indexed as

GamificationMotor rehabilitationPersonalizationRobot-assisted trainingRoboticsStroke

Identifiers

PMID36468121
PMCPMC9708787
OpenAlexW4309535309

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.