Telerehabilitation for Individuals with Parkinson’s Disease and a History of Falls: A Pilot Study
Bibliographic record
Abstract
Purpose: Falls among persons with Parkinson’s disease (PD) decrease health-related quality of life (HRQOL) and are a risk factor for hospitalization. Although physiotherapy can decrease falls and improve functional capacity, people living in remote areas have limited access to such services. This pilot study aimed to document the feasibility of a physiotherapy telerehabilitation intervention for patients with PD and to estimate the change over time in functional capacity, HRQOL, and the rate of falls. Methods: Eleven persons with PD participated in an 8-week physiotherapy telerehabilitation intervention. We assessed feasibility by computing retention rate and assiduity, number of undesirable health events, and technical problems. We assessed functional capacity, HRQOL, and falls at baseline, after the intervention, and at the 3-month follow-up. Results: Retention rate and assiduity were 91% and 100%. We resolved all technical problems (21.9% of sessions). No undesirable health events occurred. Point estimates suggest an improvement in functional capacity (Mini-BESTest) and HRQOL. Forty percent of participants fell during the intervention phase. Conclusion: Physiotherapy telerehabilitation is feasible and safe for persons with PD. Improvements in functional capacity and HRQOL must be confirmed with an appropriate design.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".