Decline in Physical Activity Level in the Childhood Cancer Survivor Study Cohort
Bibliographic record
Abstract
BACKGROUND: We aimed to identify demographic and health-related predictors of declining physical activity levels over a four-year period among participants in the Childhood Cancer Survivor Study. METHODS: Analyses included 7,287 ≥5-year childhood cancer survivors and 2,107 siblings who completed multiple follow-up questionnaires. Participants were classified as active if they met the Centers for Disease Control and Prevention guidelines for physical activity. Generalized linear models were used to compare participants whose physical activity levels declined from active to inactive over the study to those who remained active. In addition, selected chronic conditions (CTCAE v4.03 Grade 3 and 4) were evaluated as risk factors in an analysis limited to survivors only. RESULTS: The median age at last follow-up among survivors and siblings was 36 (range, 21-58) and 38 (range, 21-62) years, respectively. The rate of decline did not accelerate over time among survivors when compared with siblings. Factors that predicted declining activity included body mass index ≥30 kg/m(2) [RR = 1.32; 95% confidence interval (CI), 1.19-1.46, P < 0.01], not completing high school (RR = 1.31; 95% CI, 1.08-1.60, P < 0.01), and female sex (RR = 1.33; 95% CI, 1.22-1.44, P < 0.01). Declining physical activity levels were associated with the presence of chronic musculoskeletal conditions (P = 0.034), but not with the presence of cardiac (P = 0.10), respiratory (P = 0.92), or neurologic conditions (P = 0.21). CONCLUSIONS: Interventions designed to maximize physical activity should target female, obese, and less educated survivors. Survivors with chronic musculoskeletal conditions should be monitored, counseled, and/or referred for physical therapy. IMPACT: Clinicians should be aware of low activity levels among subpopulations of childhood cancer survivors, which may heighten their risk for chronic illness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".