Can active proprioceptive training improve proprioception in freezing of gait
Bibliographic record
Abstract
Freezing of gait (FOG) is arguably the most debilitating symptom in Parkinson's disease (PD). Impairments specifically in proprioception have been argued to underlie FOG behaviour, therefore, proprioceptive training could be beneficial for FOG. Currently, only one study has demonstrated the potential for proprioception to be improved with training but not in patients with FOG. In the current study, individuals with PD and FOG (n=13) completed proprioceptive training involving a target-matching task utilizing active and self-defined movements with the upper and lower limbs. Training sessions were one hour long, occurring twice weekly for a period of four weeks. Proprioceptive accuracy was assessed pre- and post-intervention using a passive upper limb joint-angle matching task at three positions (10, 30, and 60 degrees away from the starting position). Absolute, constant, and variable error were calculated for the limb most affected by disease. No improvements in constant or absolute error were found, however, a significant improvement was found in variable error at the 60 degree position. A subsequent analysis was conducted to compare participants divided into LOW and HIGH proprioceptive error groups (using a median split), and found significant time x group interactions in constant error at the 30 and 60 degree positions, and in variable error at the 60 degree condition. These findings suggest that improvements in joint-position matching are possible with proprioceptive training in FOG. It appears that the greatest benefit is in larger angles. This may be a viable treatment option for FOG behaviour, although further investigation quantifying FOG is required.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".