Overground versus treadmill: A manipulation of visual feedback during gait training in Parkinson's disease
Bibliographic record
Abstract
Visual cues are known to improve gait in PD.This study explored two different visual feedback training interventions (6 weeks each) for Parkinson's disease. All PD subjects were matched for severity(UPDRS score) and height prior to the training and quasi assigned to one of three groups: treadmill walking, overground walking, or a non-training PD control group.Pre and post assessments included: the Unified Parkinson's Disease Rating Scale, 30-second chair stand, timed up and go (TUG), and gait analysis on a GAITRite carpet.Both training groups used transverse lines as external visual cues that were placed either on treadmills or 16-meter carpets.Both groups were required to walk at a predetermined speed calculated in the pre-assessment with instruction to take a step, with each heel hitting the line.To determine gait changes after the training intervention, a 3group x 2limb x 3trials repeated measures ANOVA was performed. The ANOVA showed a significant interaction for GroupxStepLength (F(2,39)=3.4820,p=.041), with a post hoc test confirming main effects in both treadmill and overground groups, with significantly increased step lengths (p=.012 and p=.018 respectively).Also, the TUG test revealed a GroupxTUG interaction (F(2,39)=4.048,p =.025);post hoc analysis indicated that the overground group had significantly better TUG times (p=.022) after the intervention.Results will be discussed in terms of how visual feedback may contribute to specific aspects of gait training in PD.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".