The Adapted Resistance Training with Instability Randomized Controlled Trial for Gait Automaticity
Bibliographic record
Abstract
BACKGROUND: Deficits in the cerebellar locomotor region (CLR) have been associated with loss of gait automaticity in individuals with freezing of gait in Parkinson's disease (freezers); however, exercise interventions that restore gait automaticity in freezers are lacking. We evaluated the effects of the adapted resistance training with instability ([ARTI] complex exercises) compared with traditional motor rehabilitation (without complex exercises) on gait automaticity and attentional set-shifting. We also verified associations between gait automaticity change and CLR activation change previously published. METHODS: Freezers were randomized either to the experimental group (ARTI, n = 17) or to the active control group (traditional motor rehabilitation, n = 15). Both training groups performed exercises 3 times a week for 12 weeks. Gait automaticity (dual-task and dual-task cost [DTC] on gait speed and stride length), single-task gait speed and stride length, attentional set-shifting (time between Trail Making Test parts B and A), and CLR activation during a functional magnetic resonance imaging protocol of simulated step initiation task were evaluated before and after interventions. RESULTS: Both training groups improved gait parameters in single task (P < 0.05), but ARTI was more effective than traditional motor rehabilitation in improving DTC on gait speed, DTC on stride length, dual-task stride length, and CLR activation (P < 0.05). Changes in CLR activation were associated with changes in DTC on stride length (r = 0.68, P = 0.002) following ARTI. Only ARTI improved attentional set-shifting at posttraining (P < 0.05). CONCLUSIONS: ARTI restores gait automaticity and improves attentional set-shifting in freezers attributed to the usage of exercises with high motor complexity. © 2020 International Parkinson and Movement Disorder Society.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".