Evidence map›Paper›PMID 36494668›Full record

ArticleJournal of neuroengineering and rehabilitation2022

Naturalistic visualization of reaching movements using head-mounted displays improves movement quality compared to conventional computer screens and proves high usability.

Nicolas Wenk, Karin A Buetler, Joaquin Penalver-Andres, René M Müri, Laura Marchal-Crespo

Open access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.6field-weighted citation impact, top 15% 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

8 citing papers in PubMed, 18 citations in OpenAlex.

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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

5 authors at 3 institutions in 2 countries.

Nicolas WenkMotor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland.
Karin A BuetlerMotor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland.
Joaquin Penalver-AndresMotor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland.
René M MüriGerontechnology and Rehabilitation, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Laura Marchal-CrespoMotor Learning and Neurorehabilitation Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland. laura.marchal@unibe.ch.
University of Bern · CHDelft University of Technology · NLUniversity Hospital of Bern · CH

Funding

Swiss National Science Foundation PP00P2 163800
6 · The paper itself

Abstract

backgroundThe relearning of movements after brain injury can be optimized by providing intensive, meaningful, and motivating training using virtual reality (VR). However, most current solutions use two-dimensional (2D) screens, where patients interact via symbolic representations of their limbs (e.g., a cursor). These 2D screens lack depth cues, potentially deteriorating movement quality and increasing cognitive load. Head-mounted displays (HMDs) have great potential to provide naturalistic movement visualization by incorporating improved depth cues, reduce visuospatial transformations by rendering movements in the space where they are performed, and preserve eye-hand coordination by showing an avatar-with immersive VR (IVR)-or the user's real body-with augmented reality (AR). However, elderly populations might not find these novel technologies usable, hampering potential motor and cognitive benefits.

methodsWe compared movement quality, cognitive load, motivation, and system usability in twenty elderly participants (>59 years old) while performing a dual motor-cognitive task with different visualization technologies: IVR HMD, AR HMD, and a 2D screen. We evaluated participants' self-reported cognitive load, motivation, and usability using questionnaires. We also conducted a pilot study with five brain-injured patients comparing the visualization technologies while using an assistive device.

resultsElderly participants performed straighter, shorter duration, and smoother movements when the task was visualized with the HMDs than screen. The IVR HMD led to shorter duration movements than AR. Movement onsets were shorter with IVR than AR, and shorter for both HMDs than the screen, potentially indicating facilitated reaction times due to reduced cognitive load. No differences were found in the questionnaires regarding cognitive load, motivation, or usability between technologies in elderly participants. Both HMDs proved high usability in our small sample of patients.

conclusionsHMDs are a promising technology to be incorporated into neurorehabilitation, as their more naturalistic movement visualization improves movement quality compared to conventional screens. HMDs demonstrate high usability, without decreasing participants' motivation, and might potentially lower cognitive load. Our preliminary clinical results suggest that brain-injured patients may especially benefit from more immersive technologies. However, larger patient samples are needed to draw stronger conclusions.*.

Indexed as

Augmented RealityVirtual RealityAgedComputersHumansMiddle AgedMovementPilot ProjectsAugmented realityCognitive loadHead-mounted displayMotivationMovement qualityNeurorehabilitationStrokeUsabilityVirtual reality

Identifiers

PMID36494668
PMCPMC9733395
OpenAlexW4311666190

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.