Expectations and Experiences of Participating in a Supervised and Home-Based Physical Exercise Intervention in Patients with Head and Neck Cancer during Chemoradiotherapy: A Qualitative Study
Bibliographic record
Abstract
(1) Background: Chemoradiotherapy (CRT) for head and neck cancer (HNC) is associated with severe toxicity resulting in fatigue and weight loss, including loss of skeletal muscle mass. Exercise interventions might positively affect physical fitness and quality of life. Sufficient adherence and compliance rates are necessary for optimal effects. This study aimed to gain insight into expectations and experiences and factors influencing adherence, retention and compliance of HNC patients participating in exercise intervention during CRT. (2) Methods: Consecutive participants were invited for semi-structured interviews, conducted pre- and post-intervention. A deductive approach was used to identify themes and factors influencing adherence, retention and compliance. (3) Results: Thematic saturation was reached after interviewing 14 patients pre-intervention. Five themes were identified: planning and time management, treatment toxicity, motivation to exercise, exercise intervention and supervision by a physiotherapist. The intensity of the treatment schedule and treatment toxicity were important barriers. Facilitators mentioned were physical and emotional benefits, social support as well as the simplicity and home-based setting of the intervention. (4) Conclusions: A personalised approach, considering the individual facilitators and barriers of HNC patients, is important to increase adherence, retention and compliance to exercise intervention and to reap the optimal effects of the program.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".