Associations between Physical Activity and Quality of Life in Ovarian Cancer Survivors
Bibliographic record
Abstract
Improved survival rates for ovarian cancer has placed greater importance on understanding the factors that contribute to quality of life (QoL) for survivors. Studies with other cancer survivor populations have demonstrated a positive association between QoL and physical activity (PA), but no study to date has investigated this in ovarian cancer survivors. PURPOSE: To determine the prevalence of PA among ovarian cancer survivors, and to examine differences in QoL between those meeting, and not meeting, public health guidelines for PA. METHODS: Ovarian cancer survivors identified from a provincial cancer registry were mailed a questionnaire measuring self-reported PA (Godin Leisure Time Exercise Questionnaire) and cancer specific-QoL (Functional Assessment of Cancer Therapy - Ovarian). RESULTS: 359 ovarian cancer survivors completed the questionnaire (51.4% response rate). 31.2% reported achieving recommended levels of PA in the past month (i.e., at least 60 minutes of vigorous or 150 minutes of moderate PA per week). Overall QoL was significantly higher (mean difference: 12.5; 95% CI: 9.1 to 16.0; p < 0.001) in those meeting guidelines (120.9 ± 13.3) compared to those not meeting guidelines (108.4 ± 19.2). Disease status was the only variable to moderate this association. Among participants currently disease free, those meeting guidelines had a 9.2 (95% CI: 5.5 to 13.0; p < 0.001) higher QoL score, whereas among participants with current disease, the advantage for those meeting guidelines was 26.3 (95% CI: 18.6 to 34.1; p < 0.001). Analysis of QoL dimensions indicated higher scores for those meeting guidelines for physical (p < 0.001), functional (p < 0.001), emotional (p < 0.001), and social wellbeing (p = 0.045), and the ovarian cancer-specific subscale (p < 0.001). Adjusting for important demographic and medical variables did not alter any of these findings. CONCLUSIONS: Few ovarian survivors are meeting public health guidelines for PA, but those that are meeting guidelines report significantly and meaningfully higher QoL. These findings are compatible with evidence from other cancer survivor populations, and suggest that clinical trials investigating the causal effects of PA on QoL in ovarian cancer survivors are warranted.
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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 | 0.004 |
| 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 | 0.000 |
| 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".