Factors Influencing Physical Activity in Cancer Patients During Oncological Treatments: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Although the literature supports the importance of physical activity in the oncological context, in Italy a large number of patients are not sufficiently active. METHODS: The present study aimed to explore factors influencing an active lifestyle in cancer patients during oncological treatments. Semi-structured focus groups, including 18 patients with different cancer types, were conducted at the Oncology Unit in the University Hospital Trust of Verona (Italy). The interviews were audio-recorded, transcribed verbatim, and analyzed with content analysis. RESULTS: According to the Health Belief Model, transcripts were categorized into the following themes: benefits, barriers, and cues to action. Patients reported a series of physical, physiological, and psychological benefits deriving from an active lifestyle. The main barriers hampering the physical activity participation were represented by treatment-related side effects, advanced disease, and some medical procedures, for example, ileostomy. Several strategies that can trigger patients to exercise were identified. Medical advice, social support from family and friends, features such as enjoyment, setting goals, and owning an animal can motivate patients to perform physical activity. At the same time, an individualized program based on patients' characteristics, an available physical activity specialist to consult, more detailed information regarding physical activity in the oncological setting, and having accessible structures were found important facilitators to implementing active behavior. CONCLUSIONS: Overall, patients have a positive view regarding physical activity, and a variety of obstacles and cues to action were recognized. Considering this information may help to improve adherence to a physical activity program over time, consequently increasing the expected benefits.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".