Relationship between physical activity and cardiovascular disease risk factors in cancer survivors.
Bibliographic record
Abstract
e13067 Background: Cardiovascular disease (CVD) is one of the most prevalent comorbidities among cancer survivors. Physical activity has been well established to positively control CVD risk factors in the general population. In this study, we investigated the association between physical activity and CVD risk factors in cancer survivors. Methods: Data from the 2007-2014 Korean National Health and Nutrition Examination Survey (KNHANES) were analyzed. Of the total sample from the survey, a total of 1,596 cancer survivors were included for data analysis. CVD risk factors included total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), fasting glucose (FG), systolic blood pressure (SBP), diastolic blood pressure (DBP), and triglycerides (TG). A high risk level associated with each factor was defined according to the Adult Treatment Panel (ATP) III guidelines. Physical activity was calculated on the basis of the total minutes spent in moderate and vigorous physical activity per week. Complex sample logistic regression analyses were used to identify whether there was a difference in CVD risks between physical activity groups. Results: Of a total of 1,596 subjects, 36.4% were men, mean age was 61.4±12.7, and mean BMI was 23.4±3.3. 48.8% of respondents were completely inactive (0 min/wk.), 22.0% were insufficiently active ( < 150 min/wk.), and 29.3% met the ACSM/ACS physical activity guidelines ( ≥ 150 min/wk.). Among CVD risk factors, the risks of low HDL-C (OR = 0.70, 95% CI = 0.52 – 0.94), high SBP (OR = 0.58, 95% CI = 0.44 – 0.76) and high TG (OR = 0.65, 95% CI = 0.48 – 0.89) were lower for those who met the physical activity guidelines compared to those who were completely inactive, after adjustment for age, sex, and body mass index. Furthermore, the OR for having two or more CVD risk factors was 0.55 (95% CI, 0.39 – 0.77). Conclusions: Meeting the physical activity guidelines ( ≥ 150 min/wk.) may mitigate CVD risk factors, and thereby reduce the risk that cancer survivors will develop.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".