Exercise behavior in cancer survivors and associated factors
Bibliographic record
Abstract
INTRODUCTION: Physical activity is an important component in promoting a healthy life style in cancer survivors. We estimated the proportion of cancer survivors who are physically active, defined as meeting public health exercise guidelines, and changes in level of physical activity (LPA) from before diagnosis to after treatment. We also identified medical and demographic factors associated with LPA and its changes. METHODS: A cross-sectional survey assessing LPA before diagnosis and after treatment, together with demographic and medical variables in 975 cancer survivors. RESULTS: Forty-five percent of the cancer survivors were physically active after treatment. Before diagnosis and after treatment 33% were active, whereas 40% were inactive at both time points. Fifteen percent were active before diagnosis but inactive after treatment, and 12% were inactive before diagnosis but active after treatment. Increasing age and weight, low education, comorbidity and smoking were associated with physical inactivity after treatment. Change in LPA from active to inactive was associated with comorbidity, distant disease and smoking, while a change from inactive to active was associated with high education. CONCLUSIONS: Less than half of cancer survivors were physically active. Almost three quarters of cancer survivors remained stable in LPA. The remaining quarter changed LPA, with slightly more cancer survivors becoming inactive than active. Age, weight, education, comorbidity, disease stage and smoking can identify survivors at risk of physical inactivity after treatment. IMPLICATIONS FOR CANCER SURVIVORS: Recognizable variables can be used to identify physically inactive cancer survivors after treatment and give these survivors support to start or maintain LPA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".