PEMOCS: Evaluating the effects of a concept-guided, PErsonalised, MOtor-Cognitive exergame training on cognitive functions and gait in chronic Stroke—study protocol for a randomised controlled trial
Bibliographic record
Abstract
Background: Many stroke survivors remain with residual cognitive and motor impairments despite receiving timely acute and sub-acute rehabilitation. This indicates that rehabilitation following stroke should be continuous to meet the needs of individual stroke patients. Both cognitive and motor functions are essential for mastering daily life and, therefore, should be aimed at with rehabilitation. Exergames, motor-cognitive exercises performed using video games, are an auspicious method to train both motor and cognitive functions and at the same time may foster the long-term motivation for training. This study aims to assess the effect of concept-guided, personalised, motor-cognitive exergame training on cognitive and motor functions in chronic stroke survivors. Methods: This study is a single-blinded, randomised controlled trial. Assessments are performed at baseline, after a 12-week intervention, and at a 24-weeks follow-up. Chronic stroke patients (≥ 18 years old, ≥ 6 months post-stroke) able to stand for 3 min, independently walk 10 m, follow a two-stage command, and without other neurological diseases apart from cognitive deficits or dementia are included. Participants in the intervention group perform the exergame training twice per week for 30 (beginning) up to 40 (end) minutes additionally to their usual care programme. Participants in the control group receive usual care without additional intervention(s). Global cognitive functioning (total Montreal Cognitive Assessment (MoCA) score) is the primary outcome. Secondary outcomes include health-related quality of life, specific cognitive functions, single- and dual-task mobility, and spatiotemporal gait parameters. The target sample size for this trial is 38 participants. Linear mixed models with the post-outcome scores as dependent variables and group and time as fixed effects will be performed for analysis. Discussion: Superior improvements in global cognitive functioning and in the abovementioned secondary outcomes in the intervention group compared to the control group are hypothesised. The results of this study may guide future design of long-term rehabilitation interventions after stroke.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".