Predictors of adherence to an Iyengar yoga program in breast cancer survivors
Bibliographic record
Abstract
CONTEXT: Despite the known health benefits of physical activity, participation rates in cancer survivor groups remain low. Researchers have attempted to identify alternative modes of nontraditional physical activities that may increase participation and adherence rates. This study investigated the determinants of yoga in breast cancer survivors. AIM: To examine predictors of Iyengar yoga adherence in breast cancer survivors using the theory of planned behaviour. SETTINGS AND DESIGN: Classes were held either in Campus Recreation facilities or at the Behavioral Medicine Fitness Center at the University of Alberta in Edmonton, Canada. The study was an evaluation of an existing yoga program. MATERIALS AND METHODS: Twenty-three post adjuvant therapy breast cancer survivors participating in a community-based, twice weekly, 12 week Iyengar yoga program were asked to complete baseline measures of the theory of planned behavior, demographic, medical, health/fitness, and psychosocial variables. Adherence was measured by objective attendance to the classes. STATISTICAL ANALYSIS: We analyzed univariate associations between predictors and yoga adherence with independent t-tests. RESULTS: Adherence to the Iyengar yoga program was 63.9% and was predicted by stronger intention (P<0.001), greater self-efficacy (P=0.003), more positive instrumental attitude (Ps=0.025), higher disease stage (P=0.018), yoga experience in the past year, (P=0.044), diagnosis of a second cancer (P=0.008), lower fatigue (P=0.037), and greater happiness (P=0.023). CONCLUSIONS: Adherence to Iyengar yoga in breast cancer survivors was strongly related to motivational variables from the theory of planned behaviour. Researchers attempting to improve yoga adherence in breast cancer survivors may benefit from targeting the key constructs in the theory of planned behaviour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".