Effects of Exergame with Biofeedback Training on Functional Status, Cognition, and Quality of Life in Outpatients with Polyneuropathies: A Longitudinal Pilot Study
Bibliographic record
Abstract
Background: Polyneuropathies impair sensory, motor, and autonomic functions, affecting functional status, cognition, and quality of life. This pilot study investigated the effects of exergame with biofeedback training (Riablo system) versus standard rehabilitation on these outcomes in outpatients with mixed-etiology polyneuropathies. Methods: Seventeen outpatients were assigned to standard rehabilitation (Group 1, n = 9) or combined standard plus Riablo training (Group 2, n = 8) over three weeks. Functional status, pain, cognition, quality of life, and psychological well-being were assessed pre- and post-intervention, with a 6-month follow-up. Outcome measures included the Morse Fall Scale, Visual Analogue Scales for pain and autonomy, Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), Stroop Test, Frontal Assessment Battery (FAB), Verbal fluency test, the Short-Form Health Survey-12 (SF-12), and the Patient Health Questionnaire-4 (PHQ-4). Longitudinal changes and between-group differences were analyzed using nonparametric statistics. Results: Both groups showed significant improvements in functional status and global cognition at post-intervention. Group 2 demonstrated greater improvements in executive functions and attention, with significant reductions in pain and fall risk. At 6-month follow-up, Group 2 maintained post-intervention gains in QoL and psychological outcomes, while Group 1 showed a significant decline. Technology evaluation revealed high usability and positive psychosocial impact in Group 2, with strong correlations between executive function improvements and device usability. Conclusions: Integrating exergames with biofeedback into standard rehabilitation may provide broader and longer-lasting benefits for polyneuropathy patients. These findings support further large-scale trials to confirm efficacy and optimize technology-assisted rehabilitation protocols.
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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".