Testing the Associations Between Body Image, Social Support, and Physical Activity Among Adolescents and Young Adults Diagnosed With Cancer
Bibliographic record
Abstract
Physical activity (PA) is important for managing the side effects and long-term outcomes of cancer treatment, yet many adolescents and young adults diagnosed with cancer (AYAs) are not meeting PA guidelines. Body image and social support are two factors that can influence PA behavior and require further attention in this population. The purpose of this study was to examine the associations between body image, social support, and PA among AYAs. An online cross-sectional survey administered through the Research Electronic Data Capture platform was used to assess self-reported body image (body-related self-conscious emotions of appearance and fitness shame, guilt, authentic pride, and hubristic pride), social support (general and cancer-specific), and PA (mild, moderate-to-vigorous, and resistance exercise) in AYAs ( N = 119; M age = 34.5 ± 5.5 years). Based on findings from path analyses, body image and social support were directly associated with PA ( R 2 = 0.09–0.33). Social support was also directly associated with body image. However, there were no indirect effects. These findings provide preliminary support for the influential role of appearance and fitness body-related emotions and cancer-specific social support on PA. The results have important implications for the development of targeted strategies aimed at improving body image (e.g., cognitive dissonance and compassion-focused interventions) and social support (e.g., facilitating the provision of cancer-specific support), with the overall goal of increasing AYAs’ PA.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".