Fruit and vegetable intake, physical activity, and functional fitness among older adults in urban Alaska
Bibliographic record
Abstract
Older adults often face barriers to obtaining recommended diet, physical activity, and fitness levels. Understanding these patterns can inform effective interventions targeting health beliefs and behavior. This cross-sectional study included a multicultural sample of 58 older adults (aged 55+ years, M=71.98) living in independent senior housing in urban Southcentral Alaska. Participants completed a questionnaire and the Senior Fitness Test that assessed self-reported fruit and vegetable intake, physical activity, self-efficacy, and functional fitness. T-tests and bivariate correlation analyses were used to test six hypotheses. Results indicated that participants had low physical activity but had a mean fruit and vegetable intake that was statistically significantly higher than the hypothesized "low" score. Only 4.26% of participants met functional fitness standards for balance/agility, and 8.51% met standards for lower-body strength. However, 51.1% met standards for upper-body strength and 46.8% met standards for endurance The results also indicated that nutrition self-efficacy and exercise self-efficacy were positively related to fruit and vegetable intake and physical activity levels, respectively. Interestingly, income was not related to nutrition or activity patterns. These data complicate the picture on dietary and physical activity patterns for older adults in Alaska and offer recommendations for future health promotion activities.
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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 | 0.001 | 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) | 0.001 | 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".