The right treatment for the right patient: utility of exergaming and medical yoga for heart failure patients
Bibliographic record
Abstract
This invited commentary refers to ‘Effects of exergaming and yoga on exercise capacity, physical and mental health in heart failure patients: a randomized sub-study’ by L. Klompstra et al., https://doi.org/10.1093/eurjcn/zvae155. Regular physical activity and exercise are well-established management and treatment strategies for adults with heart failure,1 but there are several barriers to being active experienced by these patients.2 Identifying engaging and motivating strategies to promote these vulnerable populations to become active is necessary. To address this problem, Klompstra et al. 3 conducted a 3 month randomized controlled trial comparing the effects of exergaming (Nintendo Wii-Sport 5 days/week for 30 min), yoga (2 days/week for 1 h at a yoga centre), and an active control (received physical activity advice and support from a nurse or physiotherapist) among people with heart failure.3 Follow-ups were conducted 3, 6, and 12 month time points. While there were no between-group effects in any of their outcome measures, within-group effects demonstrated that exergaming and yoga improved exercise capacity (6 min walk test), fatigue, and shortness of breath. Exergaming also improved physical health-related quality of life, whereas yoga improved emotional health-related quality of life. The active control group improved their overall well-being at 3 months. This study demonstrates the potential for using exergaming and yoga as alternatives to conventional exercise prescriptions to improve symptoms of patients with heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".