A longitudinal examination of the interrelationships between multiple health behaviors in cancer patients
Bibliographic record
Abstract
Abstract Purpose A healthy lifestyle following a cancer diagnosis is associated with reduced risk for a cancer recurrence. Better understanding the interrelationships between multiple health behaviors (HB) in cancer survivors could inform the development of more effective interventions to promote a healthy lifestyle. Methods This prospective study assessed the longitudinal interrelationships between smoking, physical activity, alcohol intake, and caffeine consumption among patients with mixed cancer sites at the peri‐operative period and 2, 6, 10, 14, and 18 months later. A cross‐lagged design and structural equation modeling were used to assess the relationships between all four HBs over time. Results The study included 962 participants. The model showed a good fit to the data. For all four HBs, continuity paths consistently indicated that one particular health behavior was significantly predicted by the same health behavior at the previous time point. However, no consistent pattern of cross‐lagged relationships between HBs emerged. Physical activity at 14‐ and 18‐month evaluations was the HB most consistently involved either as a predictor as a predicted variable. Conclusion Overall, this study indicates that HBs assessed following cancer surgery are mostly independent and that interventions promoting HB changes during the cancer treatment trajectory need to target each health behavior separately.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".