The Potential Application of Commercially Available Active Video Games to Cardiac Rehabilitation: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Commercially available active video games (AVGs) have recently been used for rehabilitation in some specific patient populations but rarely in those with cardiovascular disease (CVD). Commercially available AVGs are designed to increase motivation for continuous play, which could be applicable to the long-term cardiac rehabilitation process. OBJECTIVE: The objective of this scoping review was to assess the effectiveness of AVG-induced physical exercise, safety management, and patient adherence by applying commercially available AVGs to cardiac rehabilitation. METHODS: Four databases (CINAHL, MEDLINE, PubMed, and SPORTDiscus) were searched for all years up to August 12, 2020. Articles were retained if they were written in English, included patients with CVD who were aged 18 years or older, and used AVGs as part of a physical exercise program. The included studies were then evaluated from the viewpoints of effectiveness as physical exercise, safety, and adherence management. RESULTS: Among 120 nonduplicate articles reviewed, 5 (4.2%) were eligible for inclusion, of which 3 (2.5%) were reported by the same research group. The AVG consoles used were Xbox Kinect and Nintendo Wii, and sports-related programs were adopted for the intervention. No adverse cardiac events occurred in the identified studies, and dropout rates tended to be low. CONCLUSIONS: AVGs appear to be safe and feasible for promoting an active lifestyle in patients with CVD. However, the effectiveness of AVGs alone as a therapeutic exercise to improve physical function may be limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".