Active Video Games as a Training Tool for Individuals With Chronic Respiratory Diseases
Bibliographic record
Abstract
PURPOSE: Exercise is an effective treatment for reducing symptom severity and improving quality of life for patients with chronic respiratory diseases. Active video games offer a new and enjoyable way to exercise and have gained popularity in a rehabilitation setting. However, it is unclear whether they achieve comparable physiological and clinical effects as traditional exercise training. METHODS: A systematic literature search was performed to identify studies that included an active video game component as a form of exercise training and a comparator group in chronic respiratory disease. Two assessors independently reviewed study quality using the Cochrane risk of bias tool and extracted data for exercise capacity, quality of life, and preference of exercise model. RESULTS: Six studies were included in this review. Because of the heterogeneity of the populations, study designs, length of intervention, and outcome measures, meta-analysis could not be performed. Active video game training resulted in comparable training maximal heart rate and dyspnea levels to those achieved when exercising using a treadmill or cycle (n = 5). There was insufficient evidence (n = 3) to determine whether active video game training improved exercise capacity as measured by 6-min walk test or treadmill endurance walking. CONCLUSIONS: Although the quality of evidence was low, in a small number of studies active video games induced peak heart rates and dyspnea levels comparable with traditional exercise training. Larger and longer-term randomized controlled trials are needed to establish the impact of video game training for individuals with chronic respiratory diseases.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".