Effects of visual and auditory cues on gait initiation in people with Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of auditory and visual cues on gait initiation in people with Parkinson's disease. SUBJECTS: Fourteen subjects with Parkinson's disease were recruited from community support groups, seven of whom reported having experienced freezing when walking. DESIGN AND SETTING: This study was a repeated measures analysis of gait initiation performance during a single visit to a university-based motion laboratory. Following baseline trials, auditory and visual cue conditions were presented in random order. The auditory cues were rhythmic sounds with an interval matching the subject's average step time. The visual cues were high-contrast transverse lines on the floor adjusted for the subject's first step length and overall height. MAIN MEASURES: Kinematic recordings enabled calculation of the timing and length of steps as well as overall velocity. The timing and magnitude of weight shift and push-off force were obtained from a force platform. RESULTS: The magnitudes of first and second step lengths, of push-off force and of overall gait velocity were significantly greater in the visual cue condition than in the baseline condition, whereas there was no significant effect of auditory cue on these measures. Neither cue had any significant effect on the timing of key events in gait initiation. CONCLUSIONS: Transverse line visual cues enable people with Parkinson's disease to begin walking with longer steps, greater push-off force and higher velocity. Auditory cues that others have shown to improve aspects of gait in people with Parkinson's disease do not appear to have any systematic effect on the first two steps of gait initiation.
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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 | 0.006 |
| 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".