Fine Motor Serious Game Training Improves Gait in Parkinson’s Disease: A Pilot Study
Bibliographic record
Abstract
Abstract Crucial functions for human behavior such as gross motor skills (e.g., walking), cognitive, rhythmic, and fine motor processes are mostly considered unrelated. However, these functions may interact, but their relations are still poorly understood. Moreover, evidence of causal links between them is scarce. Neurological disorders such as Parkinson’s Disease affect all these functions and thus provide a model to study the interplay between them. We tested the effect of a training delivered using serious games on tablet, involving upper limb rhythmic and fine motor functions, on walking capacities in patients with Parkinson’s Disease (PwPD). PwPD gathered into an Intervention group played either a rhythm game, or an adaptation of Tetris , four times a week for six weeks. Before and after the training, gait was evaluated in spontaneous walking and a dual task (counting backward while walking). A Control group of participants did not receive any training. Gait speed, stride length and cadence improved in the Intervention group after the training in comparison with the Control group. The improvement was observed in both Intervention groups, in the spontaneous and in the dual-task conditions. These findings support the hypothesis that gross (axial) motor functions can be trained by fine (lateralized) motor training administered via serious games, possibly by stimulating the rhythmic, perceptual, and cognitive resources sensory systems. This opening promising perspectives for telerehabilitation. Patients with movement disorders could benefit from this promising low-cost and engaging training method, fostering inclusivity and autonomy for people who have reduced access to the clinic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".