Optimizing Gait Outcomes in Parkinson’s Disease: The Effects of Musical Groove and Familiarity
Bibliographic record
Abstract
BACKGROUND: ) target gait dysfunction in PD by using the regular beat of music or metronome clips to cue normalized walking patterns. Previous research has suggested that auditory cue properties (e.g., familiarity and groove) and individual factors (e.g., beat perception ability and susceptibility to dual-task interference) influence auditory cueing treatment efficacy in healthy young and older adults; however, optimization of rhythmic cueing across individuals with PD remains understudied. METHODS: To address this, we explored the effects of familiarity, groove, beat perception ability, and synchronization instructions on gait in patients with PD during accelerated auditory cues. Individuals with idiopathic PD were randomized to walk freely or synchronized to music and metronome cues played 10% faster than their baseline walking cadence. Musical stimuli varied in self-reported familiarity and perceived groove and beat perception ability was assessed to classify participants as good or poor beat perceivers. RESULTS: Overall, high-groove music and synchronized walking elicited faster gait patterns compared to low-groove music and free walking, respectively, as demonstrated by increased gait velocity and cadence. Familiarity and beat perception ability did not significantly affect gait in individuals with PD. DISCUSSION: Altogether, our results indicate that high-groove music and synchronized walking lead to the greatest gait improvements during cueing, regardless of beat perception ability. CONCLUSION: Future studies and clinical interventions should consider stimulus type and synchronization instructions when implementing cueing therapies for gait dysfunction in PD in order to optimize treatment responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".