ArticleGait & posture2018
Biomechanical Evaluation of Virtual Reality-based Turning on a Self-Paced Linear Treadmill.
Article in Gait & posture, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Alterations in Physical Demands During Virtual/Augmented Reality-Based Tasks: A Systematic Review.Annals of biomedical engineering · 2023Pooled it
- Improved cortical activity and reduced gait asymmetry during poststroke self-paced walking rehabilitation.Journal of neuroengineering and rehabilitation · 2021Article
- Make a Left Turn: Cortico-Striatal Circuitry Mediating the Attentional Control of Complex Movements.Movement disorders : official journal of the Movement Disorder Society · 2021Review
- Complex Movement Control in a Rat Model of Parkinsonian Falls: Bidirectional Control by Striatal Cholinergic Interneurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Background: Patients with brain injuries such as Parkinson's disease or stroke exhibit abnormal gait characteristics especially during gait transitions such as step initiation and turning. Since such transitions could precipitate falls and resultant injuries, evaluation and rehabilitation of non-steady state gait in those patients are important. Whereas body weight supported treadmill training (BWSTT) provides a safe and controlled environment for gait training, it is unable to adequately train for gait transitions since the typical linear treadmill does not allow for changes in walking direction and natural fluctuations in speed. Research question: This paper verifies if the suggested virtual reality (VR) based walking interface combined with the unidirectional treadmill can stimulate the user to initiate turning gait. Methods: To validate whether initiation of turning was successfully achieved with the proposed walking system, we developed the VR-based walking interface combined with the self-paced treadmill and compared kinematics, kinetics, and muscle activation levels during the VR-based turning and over ground (OG) turning as well as between straight walking and turning within conditions. Results: Despite walking on a linear treadmill, subjects showed significant increases in head rotation, pelvic rotation, right hip abduction, left hip adduction, foot progression, medial-lateral ground reaction forces, right medial hamstring activation level, and changes in step width during the VR turn compared to straight walking. Significance: The developed VR-based turning interface can provide a safe and controlled environment for assessment of turning in healthy controls and may have a potential for assessment and training in patients with neurological disorders.
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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.