Exercise Preferences in Young Adults with Cancer—The YOUEX Study
Bibliographic record
Abstract
(1) Background: Strong evidence supports the persuasive positive effects of exercise for cancer patients and survivors. Different approaches of exercise programs have been established; however, the special interests of young adults (YAs) with cancer have rarely been considered in exercise interventions. Therefore, the study YOUng EXercisers (YOUEX) aimed to investigate exercise preferences in YAs. (2) Methods: YOUEX was a three-arm, patient preference-based non-randomized, longitudinal, pre-post exercise intervention, offering three different exercise modules to YAs during or after acute therapy (Module 1: online supervised group-based (M1); Module 2: online unsupervised (M2); Module 3: in-person supervised (M3)). The intervention period was 12 weeks with another 12-week follow-up period, the modules could be changed or amended after 6 and 12 weeks. (3) Results: 92 YAs were allocated to the study. At baseline, 50 YAs (54%) chose M2, 32 YAs (35%) M1 and 10 YAs (11%) M3. The analysis revealed high acceptability and feasibility of the online exercise programs (M1, M2). There was a high impact of the COVID-19 pandemic on the execution of M3. YAs showed diverse preferences in module selection due to differences in, e.g., cancer therapy status or favored level of supervision. (4) Conclusions: YAs need personalized exercise programs that consider their individual interests and needs. Online exercise programs can be a promising addition to existing exercise opportunities. They are an effective way to increase physical activity levels in YAs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".