Diet and Nutrition Risk Affect Mobility and General Health in Osteoarthritis: Data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: This study examined whether aspects of diet and nutrition risk explain variance in physical capacity and general health, after controlling for covariates, in Canadian adults with osteoarthritis (OA). METHODS: This was a cross-sectional study of baseline data from the Canadian Longitudinal Study on Aging (CLSA). Data from 1,404 participants with hand, hip, and/or knee OA were included. A series of regression analyses were conducted with independent variables of food intake (fiber and high calorie snack intake) and nutrition risk; and dependent variables of physical capacity and general health. Physical capacity was characterized through grip strength and a pooled index of four mobility tests. General health was characterized through an index of self-reported general health, mental health, and healthy aging. RESULTS: Higher fiber intake was related to greater mobility (p = .01). Food intake was not related to any other outcome. Nutrition risk was significantly associated with mobility (p < .001) and general health (p < .001); those with a high nutrition risk classification had poorer general health (p < .001, d = 0.65) than those at low nutrition risk. As well, those with moderate nutrition risk had poorer general health than those with low nutrition risk (p = .001, d = 0.31). CONCLUSIONS: Nutrition risk screening for older adults with OA provides insight into behavioral characteristics associated with reduced mobility and poorer general health. Also, those consuming greater amounts of fiber demonstrated better mobility. Thus, this research suggests that quality of diet and nutritional behaviors can impact both physical and mental aspects of health in those with OA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".