Cognitive and motor effects of Kinect‐based games training in people with and without Parkinson disease: A preliminary study
Bibliographic record
Abstract
Abstract Objective Purpose of this study is to evaluate the effects of training with six commercial Xbox KinectTM games on cognitive and motor aspects in Parkinson's disease (PD) patients and to compare the effects with a group of paired healthy subjects. Methods This study was a quasi‐experimental, controlled trial. Eight individuals with PD (mean age 68.9 ± 7.9) and eight older adults without PD, matched by age (mean age 67.6 ± 7.3) were enrolled in the study. Ten sessions of six Xbox 360 KinectTM commercial games were performed for 5 weeks. Subjects were evaluated before and 7 and 30 days after intervention. They were assessed using Montreal Cognitive Assessment, Frontal Assessment Battery (FAB), Timed Up and Go test, Ten Meters Walking test, and Balance Berg Scale. The Freezing of Gait Questionnaire, the Movement Disorder Society Unified Parkinson Disease Rating Scale, and the Parkinson's disease Questionnaire were also applied to PD group. Results Significant improvement was found for cognitive aspects measured by Montreal Cognitive Assessment and FAB in both groups but without retention on FAB in PD group. No significant improvements were found for motor aspects in none group. Conclusion Motor–cognitive training using Xbox KinectTM games is a feasible resource to improve executive functions in PD patients and in older healthy people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".