Pilot project: Feasibility of high-intensity active video game with COPD patients. Tools for home rehabilitation
Bibliographic record
Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a highly prevalent condition with a deterioration of the quality of life, physical function, and important morbidity. Exercise has been shown that it can improve physical capacity and a better quality of life. The objective was to observe the feasibility of using a new gameplay exergame approach safely and easily with this population. Furthermore, quadricep saturation was used to observe if this measurement can become a useful marker for the medical team during home rehabilitation. A total of 14 patients (8 men 69 ± 6 years, 6 women, 74 ± 6 years), with a moderate to severe COPD diagnostic performed 4 mini-games (Shape-Up, Ubisoft) adapted for their condition. Gaming sessions of 10 to 15 min duration were composed of 4 games of about 1.5 min each separated by rest. The average and peak minute ventilation, and peak METs were respectively: stationary knee lifting: 25.3±6.8, 33.5±8.2 L/min, and 4.2±1.5 METs; Target punching: 23.1±5.6, 31.8±9.8 L/min, and 3.7±1.2 METs; Core twisting: 22.2±7.3, 29.2±9.9 L/min, and 3.3±1.1 METs, and chair sit to stand from a: 27.8±6.7, 36.8±11.1 L/min, and 4.4±1.1 METs. These results were compared to the %quadricep saturation (Moxy) as a possible marker during home training. The current pilot-project suggests that a gaming-based exercise program was enjoyable and provided feasible high intensity exercise as a useful possibility after pulmonary rehabilitation maintenance at home. However, the %quadricep saturation measurement was insufficient to conclude if it can be useful marker for monitoring the training intensity at home. 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.002 |
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".