Importance of Maximal Strength and Muscle-Tendon Mechanics for Improving Force Steadiness in Persons with Parkinson’s Disease
Bibliographic record
Abstract
Although plantar flexion force steadiness (FS) is reduced in persons with Parkinson’s disease (PD), the underlying causes are unknown. The aim of this exploratory design study was to ascertain the influence of maximal voluntary contraction (MVC) force and gastrocnemius-Achilles muscle-tendon unit behaviour on FS in persons with PD. Nine persons with PD and nine age- and sex-matched non-PD controls (~70 years, 6 females per group) performed plantar flexion MVCs and sub-maximal tracking tasks at 5, 10, 25, 50 and 75% MVC. Achilles tendon elongation and medial gastrocnemius fascicle lengths were recorded via ultrasound during contraction. FS was quantified using the coefficient of variation (CV) of force. Contributions of MVC and tendon mechanics to FS were determined using multiple regression analyses. Persons with PD were 35% weaker during MVC (p = 0.04) and had 97% greater CV (p = 0.01) with 47% less fascicle shortening (p = 0.004) and 38% less tendon elongation (p = 0.002) than controls. Reduced strength was a direct contributor to lower FS in PD (ß = 0.631), and an indirect factor through limiting optimal muscle-tendon unit interaction. Interestingly, our findings indicate an uncoupling between fascicle shortening and tendon elongation in persons with PD. To better understand limitations in FS and muscle-tendon unit behavior, it is imperative to identify the origins of MVC decrements in persons with PD.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".