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Record W2096896007 · doi:10.3233/nre-2009-0528

Short-term effects of vibration therapy on motor impairments in Parkinson's disease

2009· article· en· W2096896007 on OpenAlexaff
Lauren King, Quincy J. Almeida, Heidi Ahonen

Bibliographic record

VenueNeurorehabilitation · 2009
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPhysical medicine and rehabilitationParkinson's diseaseRating scaleMedicineWhole body vibrationPhysical therapyVibrationRigidity (electromagnetism)GaitPsychologyDiseaseAcousticsPhysics

Abstract

fetched live from OpenAlex

Recent studies have suggested that vibration therapy may have a positive influence on motor symptoms in individuals with Parkinson's disease (PD). However, quantitative evidence of these benefits is scarce, and the concept of "whole-body" vibration in these studies is vague. The objectives of the current study were to evaluate the influence of vibration on motor symptoms and functional measures in PD by delivering sound waves to the entire body. We delivered whole body sound wave vibration to 40 individuals with PD using a Physioacoustic Chair, a piece of equipment with speakers spaced throughout the chair permitting a series of programmed low frequency sound waves through the body. Using a parallel cross-over design we utilized the Unified Parkinson's Disease Rating Scale (UPDRS), quantitative gait assessments, and a grooved pegboard for upper limb control. Improvements were seen in all symptom, motor control and functional outcome measures at the time of assessment. Specifically, a significant decrease in rigidity, and tremor were shown, as well as a significant increase in step length and improved speed on the grooved pegboard task. Results of this initial investigation provide support for vibration therapy as a non-pharmacological treatment alternative. Long-term benefits of vibration therapy will require further research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.276
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations90
Published2009
Admission routes1
Has abstractyes

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