Evaluating a Telemedicine Video Game–Linked High-Intensity Interval Training Exercise Programme in Paediatric Heart Transplant Recipients
Bibliographic record
Abstract
Paediatric heart transplant recipients (HTRs) have reduced exercise capacity, physical activity (PA), health-related quality of life (HRQoL), and self-efficacy towards PA. Exercise interventions have demonstrated improvements in exercise capacity and functional status in adult HTRs, with a specific emerging interest in the role of high-intensity interval training (HIIT). Studies of exercise interventions in paediatric HTRs have been limited and nonrandomized to date. HIIT has not yet been evaluated in paediatric HTRs. We thus seek to evaluate the safety and feasibility of a randomized crossover trial of a 12-week, home-based, video game-linked HIIT intervention using a cycle ergometer with telemedicine and remote physiological monitoring capabilities (MedBIKE) in paediatric HTRs. The secondary objective is to evaluate the impact of the intervention on (1) exercise capacity, (2) PA, (3) HRQoL and self-efficacy towards PA, and (4) sustained changes in secondary outcomes at 6 and 12 months after intervention. After a baseline assessment of the secondary outcomes, participants will be randomized to receive the MedBIKE intervention (12 weeks, 36 sessions) or usual care. After the intervention and a repeated assessment, all participants will cross over. Follow-up assessments will be administered at 6 and 12 months after the MedBIKE intervention. We anticipate that the MedBIKE intervention will be feasible and safely yield sustained improvements in exercise capacity, PA, HRQoL, and self-efficacy towards PA in paediatric HTRs. This study will serve as the foundation for a larger, multicentre randomized crossover trial and will help inform exercise rehabilitation programmes for paediatric HTRs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".