Types of Social Support Predicting Physical Activity and Quality of Life in Group Exercise Programs for Adults Living with Cancer
Bibliographic record
Abstract
Social support (SS) and physical activity (PA) can improve quality of life (QoL) in cancer, meaning group PA programs are important for rehabilitation. However, there are many types of SS, and few studies have compared which SS concepts are more strongly associated with PA and QoL. This exploratory cross-sectional study examined the association between several types of SS provided by other people in group exercise oncology classes and PA and QoL among adults living with cancer. It was hypothesized all types of SS would be positively associated with PA and QoL. Participants (N = 72) completed a questionnaire assessing 11 SS predictors, five QoL outcomes, and one self-reported PA outcome, and wore a Fitbit to assess step count for 1 week. Hypotheses were tested using multiple regression. Reassurance of worth support predicted self-reported moderate-to-vigorous PA (R2 = .07, p = .03). Relatedness thwarting negatively predicted general (R2 = .07, p = .03) and social well-being (R2 = .10, p = .01). Social network predicted physical well-being (R2 = .07, F(1, 66) = 4.93, p = .03). There were no significant SS predictors of the other outcomes. Group exercise oncology programs should train instructors to promote reassurance of worth by recognizing or facilitating other participants to recognize participants’ PA competence and skills, encourage developing social relationships by creating opportunities to connect over time, and minimize relatedness thwarting by promoting belonging and inclusion. Future research should compare different types of SS in larger samples and diverse populations of adults living with cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".