Evidence map›Paper›PMID 41225520›Full record

ArticleJournal of neuroengineering and rehabilitation2025

The interplay between haptic guidance and personality traits in robotic-assisted motor learning.

Alberto Garzás-Villar, Caspar Boersma, Alexis Derumigny, J Micah Prendergast, Arkady Zgonnikov, Jane Murray Cramm, Laura Marchal-Crespo

Abstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Alberto Garzás-VillarDepartment of Cognitive Robotics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands. A.Garzasvillar@tudelft.nl.
Caspar BoersmaDepartment of Cognitive Robotics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands.
Alexis DerumignyDepartment of Applied Mathematics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands.
J Micah PrendergastDepartment of Cognitive Robotics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands.
Arkady ZgonnikovDepartment of Cognitive Robotics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands.
Jane Murray CrammDepartment of Socio-Medical Sciences, Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Burgemeester Oudlaan, Rotterdam, 3062 PA, Zuid Holland, Netherlands.
Laura Marchal-CrespoDepartment of Cognitive Robotics, Delft University of Technology, Mekelweg, Delft, 2628 CD, Zuid Holland, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRobotic devices have shown promise in supporting motor (re)learning. However, there is a limited understanding of how personality traits influence the effectiveness of robot-aided training strategies.

methodsWe conducted a motor learning experiment with 40 unimpaired participants who trained to control a virtual pendulum using a robotic haptic device. Before the experiment, we assessed personality traits including the perceived control over life events (Locus of Control), the tendency to turn challenges into engaging activities (Transform of Challenge), and other subscales from Autotelic and Hexad gaming style questionnaires. Participants were divided into two groups, one receiving haptic guidance during training and a second one without assistance. Short- and long-term retention was assessed, and relationships between personality traits, performance metrics, and human-robot interaction metrics were analyzed.

resultsParticipants with high Transform of Challenge or external Locus of Control characteristics who received physical guidance during training reduced the human-robot interaction forces to a lesser extent compared to the ones who did not receive guidance. Additionally, participants with a high Free Spirit gaming style showed greater sensitivity to how their perception of the guidance affected their performance during the retention phases.

conclusionOur findings suggest that autotelic personality, Locus of Control, and gaming style modulate motor learning outcomes during robotic-assisted training, affecting both performance and human-robot interaction metrics. This highlights the potential of integrating personality-based adaptations in robot-aided rehabilitation protocols to enhance performance and motor (re)learning. Future works should explore the relationship between personality traits and psychological states (e.g., perceived difficulty, attention) across diverse tasks and guidance methods in clinical populations.

Indexed as

LearningPersonalityRoboticsAdultFemaleHumansInternal-External ControlMaleYoung AdultHaptic guidanceMotor learningPersonality traitsPersonalizationRobotic-assisted rehabilitation

Identifiers

PMID41225520
PMCPMC12613454

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.