Mobile wellness innovation: a Qi Gong app to improve wellness andcognitive resiliency in older adults
Bibliographic record
Abstract
Introduction This pilot project explored the utility of a mobile health and wellness app to older adults interested in using low-impact exercise as a protective factor against memory loss and mood swings. While it is known that exercise is a protective factor in preventing further cognitive regression, it is shown that adults aged 55 and older spend ten hours or more each day sitting or lying down, leaving them even more compromised (Cavill, Richardson and Foster, 2012). The piloting of a health and wellness self-management tool through a mobile app featuring the Chinese exercise of Qi Gong represents an innovative, visual and accessible tool that supports daily physical activity while fostering a sense of personal empowerment and enhancing the quality of life. Relevance of exercise to Canada's aging population Canada is facing a dementia epidemic, with approximately 500,000 Canadians experiencing Alzheimer's or an associated dementia (Hopkins, 2010). It is the most significant and widespread form of disability among Canadians who are aged 65 years and older (Hopkins, 2010). By the year 2050 there will be 115 million people worldwide afflicted with this progressive disease (Alzheimer Society of Canada, 2010). While there remains no cure, mild-to-moderate exercise has been clinically shown to reduce the risk of subsequent dementia as well as function as a protective factor. Studies demonstrate that modified exercise programmes can slow down the decline in health-related quality of life among heterogeneous older persons residing in institutions (Kwak, Um and Son, 2008; Dechamps et al., 2010). Equally, if not more important, it supports older adults who prefer to remain in their homes within a familiar community, representing an additional protective factor against age-related loneliness and depression. Aside from the mental health component, increasing the general physical state of this population addresses the most prevalent risk, that of falling. A study conducted in British Columbia found that one in three adults over the age of 65 fall once a year, threatening their physical and emotional independence (BC Ministry of Health, 2014). Exercises that strengthen the core as a preventive measure of gait imbalance is key to fall prevention and overall stability in older adults, a health issue that is quickly gaining critical importance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.021 | 0.003 |
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".