Exergames in exercise-based cardiac rehabilitation for patients with heart failure: a systematic review
Bibliographic record
Abstract
AIM: The aim of this study was to systematically review the current literature on the use of exergames as an exercise-based cardiac rehabilitation intervention for patients with heart failure. METHODS: PubMed, SCOPUS and CINAHL Plus databases were searched from January 2007 to August 2023. Studies considered eligible for inclusion had to report one or more of the following outcomes: functional capacity (e.g. VO 2 max), quality of life, mortality, hospital admissions, physical activity level, and engagement/satisfaction of the intervention. Only studies reported in English were included. Two reviewers independently assessed studies for their eligibility. RESULTS: Two studies (in four reports) were included. Included studies reported only data on functional capacity (6-min walking test) and on physical activity level (accelerometers). Due to the low number of included studies, no meta-analysis was performed, and results were discussed narratively. CONCLUSION: Exergames may potentially be a promising tool for exercise-based cardiac rehabilitation in patients with heart failure; however, the low number of included studies was insufficient to drawn proper conclusions. Benefits of exergames compared with traditional interventions could be the possibility of it being delivered at home, reducing some of the barriers that patients with heart failure must face. Further studies are required to assess the efficacy of exergame interventions in patients with heart failure, and to define proper guidelines to deliver exergame interventions in this population.This systematic review was registered on PROSPERO (CRD42023446948).
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".