Cycling-Based Telerehabilitation: Acceptability and Feasibility Study
Bibliographic record
Abstract
Background: Telerehabilitation is a promising solution to provide continuity of care. Most existing telerehabilitation platforms focus on rehabilitating upper limbs, balance, and cognitive training, but exercises improving cardiovascular fitness are often neglected. Objective: The objective of this study is to evaluate the acceptability and feasibility of a telerehabilitation intervention combining cognitive and aerobic exercises. Methods: A virtual reality-based dual-task exercise exploiting a cycle ergometer was designed, developed, and integrated with a commercially available telerehabilitation platform. Patients with different conditions were enrolled and administered subjective questionnaires investigating attitudes toward technology, usability, technology acceptance, and subjective workload. Their therapists were interviewed, and adherence and performance data were analyzed. Results: In total, 26 patients with neurological or post-COVID symptoms were included. Their attitude toward technology (range: 0-5) did not change after the training period (pre: 3.44 [IQR 0.63]; post: 3.50 [IQR 0.48]); the platform was rated usable and acceptable. Frustration and physical and mental workload were present, especially among younger participants. The adherence was moderate, but individual differences were present (0.59 [IQR 0.54]). The therapists highlighted the potential of remote rehabilitation programs but also identified some limitations. Conclusions: This study proved the feasibility and acceptability of a customized virtual reality-based telerehabilitation program allowing for the safe implementation of aerobic cycling-based dual-task training. The solution was judged meaningful for dehospitalized patients, although some environmental and technical barriers should be overcome to implement telerehabilitation more effectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".