Can Physical Activity and Healthy Diet Help Long-Term Cancer Survivors Manage Their Fear of Recurrence?
Bibliographic record
Abstract
Objective: Fear of cancer recurrence (FCR) adversely affects quality of life, but health behaviors such as physical activity (PA) and fruit and vegetable intake (FVI) may help alleviate FCR for some survivors. This cross-sectional study tested the common-sense model (CSM) of FCR by investigating associations between constructs from the CSM (perceived illness consequences, control over health, and timeline), and survivors’ health behaviors, health self-efficacy, and FCR. Methods: Using wave 3 data from the American Cancer Society Longitudinal Study of Cancer Survivorship-I, path analyses were conducted among mixed-cancer participants (N = 2,337) who were on average 8.8 mean years post-diagnosis. Results: A final good fitting model [χ2 (5, N = 2,337) = 38.12, p < 0.001; SRMR = 0.02; CFI = 0.99; RMSEA = 0.05] indicated that perceiving fewer illness consequences, and greater control over one’s health, were directly associated with higher PA (β = 0.15 and −0.24, p < 0.01, respectively) and higher health self-efficacy (β = 0.24, −0.38, p < 0.01, respectively). Timeline (i.e., perceiving cancer as chronic) was directly associated with lower health self-efficacy (β = −0.15, p < 0.01) and higher FCR (β = 0.51, p < 0.01). Both greater PA and FVI were directly associated with higher health self-efficacy (β = 0.10 and 0.11, p < 0.01, respectively) which in turn showed a direct association with lower FCR (β = −0.15, p < 0.01). Conclusion: Increasing survivors’ sense of control over health, decreasing perceived chronicity of the illness, and mitigating its consequences may increase their health behaviors and health self-efficacy, which in turn could decrease their FCR. Longitudinal and experimental studies are needed to confirm these findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".