Are Physical Activity Levels Linked to Nutrient Adequacy? Implications for Cancer Risk
Bibliographic record
Abstract
Cancer prevention guidelines recommend a healthy body mass index, physical activity, and nutrient intake from food rather than supplements. Sedentary individuals may restrict energy intake to prevent weight gain and in so doing may compromise nutritional intake. We conducted a cross-sectional analysis to determine if adequacy of micronutrients is linked to physical activity levels (PALs) in healthy-weight adults. Tomorrow Project participants in Alberta, Canada (n = 5333), completed past-year diet and physical activity questionnaires. The percent meeting Dietary Reference Intakes (DRIs) was reported across low and high PAL groups, and the relation between PAL and percent achieved DRI was determined using multiple linear regression analyses. Overall, <50% of healthy-weight participants met DRIs for folate, calcium, and vitamin D. Percent achieved DRI increased linearly with increasing PAL in both genders (P < 0.01). A hypothetical increase in PAL from 1.4 to 1.9 was associated with a DRI that was 8%-13% higher for folate and vitamin C (men) and 5%-15% higher for calcium and iron (women). Healthy-weight adults at higher PALs appear more likely to meet DRIs for potential cancer-preventing nutrients. The benefits of higher PALs may extend beyond the usual benefits attributed to physical activity to include having a more favorable impact on nutrient adequacy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".