Evidence map›Paper›PMID 28105952›Full record

SynthesisBiomedical engineering online2016

Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review.

Luara Ferreira Dos Santos, Oliver Christ, Kedar Mate, Henning Schmidt, Jörg Krüger, Christian Dohle

Abstract readSystematic Review
In one paragraph

Synthesis in Biomedical engineering online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 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

29 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Review
  19. Article
  20. 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

6 authors.

Luara Ferreira Dos SantosRehabilitation Robotics Group (TU Berlin/ Fraunhofer IPK), Department of Industrial Automation Technology, Technische Universität Berlin, Pascalstr. 8-9, 10587, Berlin, Germany. luara.santos@iwf.tu-berlin.de.
Oliver ChristInstitute Humans in Complex Systems, School of Applied Psychology, University of Applied Sciences and Arts Nortwestern Switzerland, Riggenbachstrasse 16, Olten, Switzerland.
Kedar MateSchool of Physical and Occupational Therapy, McGill University, 3654 Promenade Sir William Osler Montreal, Quebec, H3G 1Y5, Canada.
Henning SchmidtRehabilitation Robotics Group (Fraunhofer IPK/ TU Berlin), Department of Automation Technology, Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstr. 8-9, 10587, Berlin, Germany.
Jörg KrügerRehabilitation Robotics Group (TU Berlin/ Fraunhofer IPK), Department of Industrial Automation Technology, Technische Universität Berlin, Pascalstr. 8-9, 10587, Berlin, Germany.
Christian DohleDepartment of Neurological Rehabilitation, MEDIAN Klinik Berlin-Kladow, Kladower Damm 223, 14089, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVirtual reality (VR) based applications play an increasing role in motor rehabilitation. They provide an interactive and individualized environment in addition to increased motivation during motor tasks as well as facilitating motor learning through multimodal sensory information. Several previous studies have shown positive effect of VR-based treatments for lower extremity motor rehabilitation in neurological conditions, but the characteristics of these VR applications have not been systematically investigated. The visual information on the user's movement in the virtual environment, also called movement visualisation (MV), is a key element of VR-based rehabilitation interventions. The present review proposes categorization of Movement Visualisations of VR-based rehabilitation therapy for neurological conditions and also summarises current research in lower limb application.

methodsA systematic search of literature on VR-based intervention for gait and balance rehabilitation in neurological conditions was performed in the databases namely; MEDLINE (Ovid), AMED, EMBASE, CINAHL, and PsycInfo. Studies using non-virtual environments or applications to improve cognitive function, activities of daily living, or psychotherapy were excluded. The VR interventions of the included studies were analysed on their MV.

resultsIn total 43 publications were selected based on the inclusion criteria. Seven distinct MV groups could be differentiated: indirect MV (N = 13), abstract MV (N = 11), augmented reality MV (N = 9), avatar MV (N = 5), tracking MV (N = 4), combined MV (N = 1), and no MV (N = 2). In two included articles the visualisation conditions included different MV groups within the same study. Additionally, differences in motor performance could not be analysed because of the differences in the study design. Three studies investigated different visualisations within the same MV group and hence limited information can be extracted from one study.

conclusionsThe review demonstrates that individuals' movements during VR-based motor training can be displayed in different ways. Future studies are necessary to fundamentally explore the nature of this VR information and its effect on motor outcome.

Indexed as

Activities of Daily LivingComputer SimulationGaitHumansLower ExtremityMotor SkillsMovementNervous System DiseasesPostural BalanceRecovery of FunctionStroke RehabilitationUser-Computer InterfaceVideo Games

Identifiers

PMID28105952
PMCPMC5249036

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.