Associations of device‐measured physical activity and sedentary time with quality of life and fatigue in newly diagnosed breast cancer patients: Baseline results from the AMBER cohort study
Bibliographic record
Abstract
BACKGROUND: This study examined associations of device-measured physical activity and sedentary time with quality of life (QOL) and fatigue in newly diagnosed breast cancer patients in the Alberta Moving Beyond Breast Cancer (AMBER) cohort study. METHODS: After diagnosis, 1409 participants completed the SF-36 version 2 and the Fatigue Scale, wore an ActiGraph device on their right hip to measure physical activity, and an activPAL device on their thigh to measure sedentary time (sitting/lying) and steps. ActiGraph data was analyzed using a hybrid machine learning method (R Sojourn package, Soj3x) and activPAL data were analyzed using activPAL algorithms (PAL Software version 8). Quantile regression was used to examine cross-sectional associations of QOL and fatigue with steps, physical activity, and sedentary hours at the 25th, 50th, and 75th percentiles of the QOL and fatigue distributions. RESULTS: Total daily moderate and vigorous physical activity (MVPA) hours was positively associated with better physical QOL at the 25th (β = 2.14, p = <.001), 50th (β = 1.98, p = <.001), and 75th percentiles (β = 1.25, p = .003); better mental QOL at the 25th (β = 1.73, p = .05) and 50th percentiles (β = 1.07, p = .03); and less fatigue at the 25th (β = 4.44, p < .001), 50th (β = 3.08, p = <.001), and 75th percentiles (β = 1.51, p = <.001). Similar patterns of associations were observed for daily steps. Total sedentary hours was associated with worse fatigue at the 25th (β = -0.58, p = .05), 50th (β = -0.39, p = .06), and 75th percentiles (β = -0.24, p = .02). Sedentary hours were not associated with physical or mental QOL. CONCLUSIONS: MVPA and steps were associated with better physical and mental QOL and less fatigue in newly diagnosed breast cancer patients. Higher sedentary time was associated with greater fatigue symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".