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Record W1964888200 · doi:10.1002/mds.22886

A positive influence of vision on motor symptoms during sensory attention focused exercise for Parkinson's disease

2009· article· en· W1964888200 on OpenAlexaff
Michael Sage, Quincy J. Almeida

Bibliographic record

VenueMovement Disorders · 2009
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPhysical medicine and rehabilitationSensory systemParkinson's diseasePsychologyRating scaleGaitPhysical therapyDiseaseMedicineNeuroscienceDevelopmental psychologyInternal medicine

Abstract

fetched live from OpenAlex

This study evaluated the effect of increased attention to sensory feedback during exercise. Two 12-week exercise programs that differed only in the presence (PD SAFEx) or absence (non-SAFE control group) of increased attention focused on sensory feedback were compared. Participants were assessed symptomatically using the Unified Parkinson's Disease Rating Scale (UPDRS) before the start of the exercise program, immediately following the 12-week program and after a 6-week nonexercise washout period. Secondary outcome measures included the Timed-Up-and-Go (TUG), Grooved Pegboard (GP) and velocity and step length of self-paced gait. Both groups significantly improved on the TUG, GP, velocity, and step length, and this was maintained after a 6-week washout period. Of additional interest, only the PD SAFEx program significantly improved motor symptoms (UPDRS). These gains were maintained in the PD SAFEx group 6 weeks after the exercise was stopped, while motor symptoms significantly worsened in the non-SAFE group. These results suggest that increasing awareness of sensory feedback may be a critical factor that specifically impacts motor symptoms. Future work should strive to uncover the underlying neurophysiological mechanism behind this effect.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.914

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 head0.006
GPT teacher head0.250
Teacher spread0.243 · 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

Citations49
Published2009
Admission routes1
Has abstractyes

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