Ability to Maintain a 0.22 m/sec Gait Speed as Directed by an Auditory Metronome in Adults
Bibliographic record
Abstract
Purpose: To determine if healthy adults can maintain a slow gait speed after a seven-day training period of metronome guidance.\nSubjects: Twenty students age 18-45 years.\nMaterials/Methods: The cadence of each participant was determined while walking on a treadmill at 0.22m/sec and individual metronomes were set accordingly. Participants walked along a pre-determined path and GaitRite mat (measuring cadence and velocity) with and without metronome guidance. This was repeated after a seven-day training period that consisted of walking with the metronome 10 minutes/day over five of the seven days.\nResults: No significant visit effect for cadence (P=0.41) or velocity (P=0.47). Both cadence and velocity were significantly higher in the metronome vs. non-metronome condition (P=0.004, P=0.001). An interaction effect showed that cadence did not significantly change between visits with the metronome, however significantly decreased without the metronome (P=0.02). Velocity was not significantly different than the desired speed of 0.22 m/sec at either visit without the metronome (P=0.095, P=0.56), however was significantly faster with the metronome at visit two (P=0.001).\nConclusion: Training slow cadence using a metronome is effective in achieving a slow velocity only once this cue is removed. Constant cueing helps maintain consistent cadence and velocity, however metronome guidance alone cannot promote a specific gait speed.\nRelevance: Potential application to parent-led interventions for early walking in children with Down syndrome.
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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 | 0.001 |
| 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) | 0.002 | 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".