Evidence map›Paper›PMID 35315785›Full record

ArticleJMIR serious games2022

Determining the Optimal Virtual Reality Exergame Approach for Balance Therapy in Persons With Neurological Disorders Using a Rasch Analysis: Longitudinal Observational Study.

Evelyne Wiskerke, Jan Kool, Roger Hilfiker, Karl Martin Sattelmayer, Geert Verheyden

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR serious games, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03993275 (Exergames Balance Program in Neurorehabilitation), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 25% 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.

NCT03993275 completednot on this map

Exergames Balance Program in Neurorehabilitation

TypeobservationalSponsorKlinik ValensRan2018 to 2020Enrolled84ConditionsStroke, Multiple Sclerosis, Balance DisordersArmsMindMotion GO
3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Digital therapeutics in the clinic.Bioengineering & translational medicine · 2023
    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

5 authors at 3 institutions in 2 countries.

Evelyne WiskerkeDepartment of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0001-6019-5944
Jan KoolRehazentrum Valens, Kliniken Valens, Valens, Switzerland.ORCID https://orcid.org/0000-0002-7074-8589
Roger HilfikerSchool of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland.ORCID https://orcid.org/0000-0001-8662-6116
Karl Martin SattelmayerSchool of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland.ORCID https://orcid.org/0000-0001-8001-4776
Geert VerheydenDepartment of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium.ORCID https://orcid.org/0000-0003-3095-8175
HES-SO Valais-Wallis · CHValens Clinics · CHKU Leuven · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVirtual reality (VR) exergames have gained popularity in the rehabilitation of persons with neurological disorders as an add-on therapy to increase intensity of training. Intensity is strongly dependent on the motivation of the patient. Motivation can be increased by delivering variation within training and challenging exercises. However, patients are often underchallenged, as exergame difficulty often does not match the patient's ability. A Rasch analysis can establish hierarchy of exergame items in order to assist the delivery of patient-centered therapy.

objectiveThe aim of this study was to apply the Rasch model to create a hierarchical order of existing VR balance exergames and to relate these exergames to the abilities of persons with neurological disorders, in order to deliver challenge and variation.

methodsA total of 30 persons with stroke and 51 persons with multiple sclerosis (MS) were included in the study. All participants performed a training program, lasting 3 weeks for persons with MS and 4 weeks for persons with stroke, in which they performed VR balance exergames with a movement recognition-based system (MindMotion GO; MindMaze SA). VR exercise scores, Berg Balance Scale scores, and clinical descriptive data were collected. Berg Balance Scale and device scores were analyzed with the Rasch model using a repeated-measures approach to examine whether the distribution of exercise scores fitted the Rasch model. Secondly, a person-item map was created to show the hierarchy of exercise difficulty and person ability.

resultsParticipants completed a selection of 56 balance exercises (ie, items), which consisted of a combination of various balance tasks and levels (ie, exercises). Using repeated measures, this resulted in a count of 785 observations. Analysis showed strong evidence for unidimensionality of the data. A total of 47 exercises (ie, items) had a sufficiently good fit to the Rasch model. Six items showed underfit, with outfit mean square values above 1.5. One item showed underfit but was kept in the analysis. Three items had negative point-biserial correlations. The final model consisted of 47 exercises, which were provided for persons with low to moderate balance ability.

conclusionsThe VR exercises sufficiently fitted the Rasch model and resulted in a hierarchical order of VR balance exercises for persons with stroke and MS with low to moderate balance ability. In combination with the Berg Balance Scale, the results can guide clinical decision-making in the selection of patient-focused VR balance exercises.

trial registrationClinicalTrials.gov NCT03993275; https://clinicaltrials.gov/ct2/show/NCT03993275.

Indexed as

balancedigital therapeuticsexergamingmultiple sclerosisneurorehabilitationRasch analysisstrokevirtual reality

Identifiers

PMID35315785
PMCPMC8984820
OpenAlexW4220731559

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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.