Effects of physical activity and exercise on physical and mental health outcomes in female older adults with arthritis
Bibliographic record
Abstract
Background: Arthritis is a chronic, degenerative disease, which affects two million older Canadians, of which the majority are older females (65+ years). With no cure, methods to improve arthritic symptoms are essential to maintain physical and mental health. Physical activity (PA) and exercise may be advantageous strategies for improving arthritis-related symptoms and mental health outcomes, yet there is a lack of consistent evidence surrounding these terms. Aims and Significance: The aim of this cross-sectional study was to evaluate the health-related benefits of PA and exercise and assess the relationship between leisure-time activity levels and pain; discomfort; physical function; range of motion (ROM); mobility, and health-related quality of life (HRQOL) outcomes in females aged 65 years and older. Methods: 40 older females residing in the Durham Region of Ontario participated in the study of which 60% (N=24) were categorized as active (71 years mean age) and 40% (N=16) were considered inactive (82 years mean age). Self-reported questionnaires were employed to measure health outcomes including a visual analog scale (VAS), a health questionnaire, medical outcomes short form-12 (SF-12) and activity levels questionnaire for older adults (ALQOA). Results: Older active arthritic females reported less pain (p<0.001); less discomfort (p<0.001); higher physical function (p<0.0001); higher ROM (p<0.001); higher mobility (p<0.0001), and higher HRQOL (p<0.0001) scores, in comparison to their inactive counterparts. Conclusion: In support of my hypotheses, older females with arthritis who were active reported significantly: (i) Less pain; (ii) lower discomfort; (iii) higher HRQOL; (iv) higher mobility; (v) higher physical function, and (vi) higher ROM. These preliminary findings suggest that older females with arthritis living an active lifestyles can have both physical and mental health benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".