Reallocating Time to Sleep, Sedentary Time, or Physical Activity: Associations with Waist Circumference and Body Mass Index in Breast Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Moderate-to-vigorous intensity physical activity (MVPA) is inversely associated with waist circumference and body mass index (BMI) among breast cancer survivors. Limited research has focused on behaviors that account for larger portions of the day [sleep, sedentary time, and light-intensity physical activity (LPA)]. We investigated the interdependent associations of self-reported sleep, objectively assessed prolonged and short bouts of sedentary time, total LPA, and total MVPA with waist circumference and BMI. METHODS: A cross-sectional sample of breast cancer survivors (N = 256, mean age = 60 years; mean time since diagnosis = 3 years) wore an Actigraph GT3X+ accelerometer during waking hours for 7 days. Participants completed the Pittsburgh Sleep Quality Index and self-reported their waist circumference, height, and weight. An isotemporal substitution approach was used in linear regression models to explore the associations of reallocating time to sleep, sedentary and active behaviors on waist circumference, and BMI, after adjusting for potential confounders. RESULTS: Reallocating 30 minutes to MVPA was significantly associated with lower waist circumference when allocated from sleep (-2.50 cm), prolonged sedentary time (-2.51 cm), or LPA (-2.71 cm). Reallocating 30 minutes of prolonged sedentary time to nonprolonged sedentary time was significantly associated with lower waist circumference (-0.94 cm). Similar results were observed for BMI. CONCLUSIONS: Reallocating 30 minutes to MVPA was associated with significantly lower waist circumference and BMI, as was reallocating 30 minutes of prolonged sedentary time to 30 minutes of nonprolonged sedentary time. IMPACT: Increasing MVPA levels and decreasing time spent in prolonged, unbroken sedentary bouts may be avenues for improving body composition in this population. Cancer Epidemiol Biomarkers Prev; 26(2); 254-60. ©2016 AACR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".