Pilot project: Physiologic responses to a high‐intensity active video game with<scp>COPD</scp>patients–Tools for home rehabilitation
Bibliographic record
Abstract
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is a respiratory condition that causes a significant deterioration of the quality of life. However, exercise can improve the quality of life for COPD patients and it is for this reason previous study observed the effects of active video games to increase exercise. Using motion capture devices with short bursts of exercise never been tried with COPD patients. OBJECTIVES: The objective was to observe the feasibility of using this device safely and easily with COPD patients. METHODS: A total of 14 participants (8 men, 69 ± 6 years, 6 women, 74 ± 6 years), with a moderate to severe COPD diagnosis performed exercise games (Shape-Up, Ubisoft, Mtl) adapted under supervision. Gaming sessions of 10-15 min duration were composed of four games of about 1.5 min separated by rest. RESULTS: Average and peak minute ventilation, and METs peak were, respectively: Stunt Run game (lifting knees on spot) 25.3 ± 6.8, 33.5 ± 8.2 L/min and 4.2 ± 1.5 METs; Arctic Punch game (punching targets): 23.1 ± 5.6, 31.8 ± 9.8 L/min and 3.7 ± 1.2 METs; To the Core game (core twist), 22.2 ± 7.3, 29.2 ± 9.9 L/min and 3.3 ± 1.1 METs; and Squat me to the Moon game (sitting to standing), 27.8 ± 6.7, 36.8 ± 11.1 L/min and 4.4 ± 1.1 METs. CONCLUSION: Knowing the pleasure reported by the participants, the safety, and the ability to use it with assistance, it seems that the games could be a good tool in order for COPD patients to exercise at home. However, further investigation needs to be completed in order to observe the benefits in comparison to a traditional training program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".