Physical Activity among Lung Cancer Survivors: Changes across the Cancer Trajectory and Associations with Quality of Life
Bibliographic record
Abstract
BACKGROUND: Regular physical activity may offer benefits to lung cancer survivors, many of whom experience quality-of-life (QOL) impairments. However, little is know about lung cancer survivors' engagement in physical activity across the cancer trajectory. The current study addressed this research gap and also examined the association between lung cancer survivors' physical activity and their QOL. METHODS: The study participants were 175 individuals who completed surgical treatment for early-stage non-small cell lung cancer 1 to 6 years previously. Participants completed a one-time survey regarding their current QOL and their engagement in physical activities currently, during the 6 months after treatment, and during the 6 months before diagnosis. RESULTS: Participants' reported engagement in both moderate and strenuous intensity activities was lower during the post-treatment period compared with before diagnosis and at the current time. Engagement in light intensity activities did not differ for the three time points. Almost two-thirds of participants did not engage in sufficient activity to meet national physical activity guidelines for any of the three time points. Lung cancer survivors who currently met physical activity guidelines reported better QOL in multiple domains than less active individuals. CONCLUSIONS: Engagement in physical activity among lung cancer survivors is particularly low during the early post-treatment period. Current engagement in physical activity is associated with better QOL. However, most lung cancer survivors do not meet physical activity guidelines and may benefit from interventions to promote engagement in regular physical activities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".