Active video gaming in patients with renal transplant: a pilot study
Bibliographic record
Abstract
BACKGROUND: Patients with renal transplant are at higher risk of mortality from cardiovascular disease (CVD) compared with the general population. Physical activity has been shown to reduce the risk of CVD mortality in these patients. Unfortunately, barriers such as the harsh Canadian climate prevent patients from engaging in and harvesting the health benefits of physical activity. This pilot study explored active video gaming (AVG) as a way for patients with renal transplant to obtain physical activity and examined its effect on their functional status and quality of life (QOL). MAIN TEXT: We recruited nine patients for an 8-week prospective pilot study. All patients received a Microsoft Xbox 360™ video gaming console, a Microsoft Kinect™ sensor, and the video game Your Shape Fitness Evolved 2012. Assessment of each participant before and after the intervention included blood pressure measures, a 6-minute walk test, and the Godin Leisure Time Questionnaire (GLTQ). We analyzed all nine patients at the end of the 8-week study period, and found no changes in blood pressure or GLTQ scores. However, there was a significant increase in the 6-minute walk distance (P = 0.022), which represented a consistent increase for most patients (correlation = 0.977). In addition, participants over the age of 45 years (n = 4) were more likely to use the AVG system (P = 0.042). CONCLUSION: AVG has the potential to improve the functional status in patients with renal transplant. Further research is required to corroborate the full health benefits of AVG in this patient population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".