Evidence map›Paper›PMID 39627867›Full record

ArticleJournal of neuroengineering and rehabilitation2024

On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.

Francesca Patarini, Federica Tamburella, Floriana Pichiorri, Shiva Mohebban, Alessandra Bigioni, Andrea Ranieri, Francesco Di Tommaso, Nevio Luigi Tagliamonte, Giada Serratore, Matteo Lorusso and 5 more

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In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. Review
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Francesca PatariniDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185, Rome, Italy.
Federica TamburellaDepartment of Life Sciences, Health and Health Professions, Link Campus University Rome, Rome, Italy.
Floriana PichiorriNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.
Shiva MohebbanNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.
Alessandra BigioniLaboratory of Robotic Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy.
Andrea RanieriDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185, Rome, Italy.
Francesco Di TommasoUniversità Campus Bio-Medico di Roma, Rome, Italy.
Nevio Luigi TagliamonteLaboratory of Robotic Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy.
Giada SerratoreLaboratory of Robotic Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy.
Matteo LorussoLaboratory of Robotic Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy.
Angela CiaramidaroDepartment of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Reggio Emilia, Italy.
Febo CincottiDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185, Rome, Italy.
Giorgio ScivolettoLaboratory of Robotic Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy.
Donatella MattiaNeuroelectrical Imaging and Brain Computer Interface Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.
Jlenia ToppiDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, 00185, Rome, Italy. jlenia.toppi@uniroma1.it.

Funding

Ministero della Salute GR2019-12369207Sapienza Università di Roma RM123188F229EC72
6 · The paper itself

Abstract

backgroundTreadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a third agent to the traditional one-to-one physiotherapist-patient (Pht-Pt) relationship, the therapist might not be fully aware of the patient's motor performance. This gap has been bridged by the integration in rehabilitation robots of a visual FeedBack (FB) that informs about patient's performance. Despite the recognized importance of FB in t-RAGT, the optimal role of the therapist in the complex patient-robot interaction is still unclear. This study aimed to describe whether the type of FB combined with different modalities of Pht's interaction toward Pt would affect the patients' visual attention and emotional engagement during t-RAGT.

methodsTen individuals with incomplete Spinal Cord Injury (C or D ASIA Impairment Scale level) were assessed using eye-tracking (ET) and high-density EEG during seven t-RAGT sessions with Lokomat where (i) three types of visual FB (chart, emoticon and game) and (ii) three levels of Pht-Pt interaction (low, medium and high) were randomly combined. ET metrics (fixations and saccades) were extracted for each of the three defined areas of interest (AoI) (monitor, Pht and surrounding) and compared among the different experimental conditions (FB, Pht-Pt interaction level). The EEG spectral activations in theta and alpha bands were reconstructed for each FB type applying Welch periodogram to data localised in the whole grey matter volume using sLORETA.

resultsWe found an effect of FB type factor on all the ET metrics computed in the three AoIs while the factor Pht-Pt interaction level also combined with FB type showed an effect only on the ET metrics calculated in Pht and surrounding AoIs. Neural activation in brain regions crucial for social cognition resulted for high Pht-Pt interaction level, while activation of the insula was found during low interaction, independently on the FB used.

conclusionsThe type of FB and the way in which Pht supports the patients both have a strong impact on patients' engagement and should be considered in the design of a t-RAGT-based rehabilitation session.

Indexed as

ElectroencephalographyEye-Tracking TechnologyFeedback, SensoryRoboticsAdultExercise TherapyFemaleGaitGait Disorders, NeurologicHumansMaleMiddle AgedPhysical TherapistsSpinal Cord InjuriesEEGEye-trackingRobot-assisted gait trainingSpinal cord injuryVisual feedback

Identifiers

PMID39627867
PMCPMC11616173

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.