Comparison of Spontaneous Motor Tempo during Finger Tapping, Toe Tapping and Stepping on the Spot in People with and without Parkinson’s Disease
Bibliographic record
Abstract
OBJECTIVE: Spontaneous motor tempo (SMT), observed in walking, tapping and clapping, tends to occur around 2 Hz. Initiating and controlling movement can be difficult for people with Parkinson's (PWP), but studies have not identified whether PWP differ from controls in SMT. For community-based interventions, e.g. dancing, it would be helpful to know a baseline SMT to optimize the tempi of cued activities. Therefore, this study compared finger tapping (FT), toe tapping (TT) and stepping 'on the spot' (SS) in PWP and two groups of healthy controls [age-matched controls (AMC) and young healthy controls (YHC)], as SMT is known to change with age. METHODS: Participants (PWP; n = 30, AMC; n = 23, YHC; n = 35) were asked to tap or step on the spot at a natural pace for two trials lasting 40 seconds. The central 30 seconds were averaged for analyses using mean inter-onset intervals (IOI) and coefficient of variation (CoV) to measure rate and variability respectively. RESULTS: PWP had faster SMT than both control groups, depending on the movement modality: FT, F(2, 87) = 7.92, p < 0.01 (PWP faster than YHC); TT, F(2, 87) = 4.89, p = 0.01 (PWP faster than AMC); and SS, F(2, 77) = 3.26, p = 0.04 (PWP faster than AMC). PWP had higher CoV (more variable tapping) than AMC in FT only, F(2, 87) = 4.10, p = 0.02. CONCLUSION: This study provides the first direct comparison of SMT between PWP and two control groups for different types of movements. RESULTS: suggest SMT is generally faster in PWP than control groups, and more variable when measured with finger tapping compared to stepping on the spot.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".