Effects of Distinct Dual-tasks on Gait and the Correlation between Gait speed and Clinical Features in Parkinson’s disease
Bibliographic record
Abstract
Abstract Background Gait impairment is a common and disabling motor symptom in Parkinson’s disease (PD), deteriorated gait parameters have showed in both single-task (ST) and dual-task (DT) conditions. The aim of this study was to investigate the effects of different motor-cognitive and motor-motor DTs on gait and the correlation between gait speed and clinical features in PD patients. Methods Fifty-six individuals with PD completed two motor-cognitive DTs (serial-7 subtractionand digit backward) and one motor-motor DT (button pressing). Spatiotemporal gait parameters were evaluated by wearable sensors. DT effects (DTEs) of gait parameters were calculated. Clinical variables recorded including Hoehn & Yahr (H-Y) staging, Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part I, II and III, New Freezing of Gait Questionnaire (NFOG-Q), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), 39-item Parkinson’s Disease Questionnaire (PDQ-39) and Nonmotor Symptom Scale (NMSS). Results Gait parameters including gait speed, cadence, stride length, gait cycle duration, double support phase deteriorated under the motor-cognitive DT conditions by Paired-sample t test and Wilcoxon signed-rank test (p＜0.01, p＜0.05). The motor-motor DT had no significant effect on gait performance except for gait speed (p＞0.05). The serial-7 subtraction DT paradigm had similar effect on gait with the digit backward DT. Gait speed was negatively correlated with MDS-UPDRS I, II, HAMA, HAMD, NMSS and PDQ-39 scores in PD patients under both ST and DT conditions (p＜0.01, p＜0.05). Conclusion Effects of DT conditions on gait deficits were independent of the types of cognitive tasks. Gait speed was influenced by clinical features of PD under both ST and DT conditions. Whatever the types, motor-cognitive DT training should be used to improve gait performance under DT conditions, which is required to provide more therapeutic support of PD patients in the future.
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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 | 0.002 |
| 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".