Can Measures Of Stepping Provide Insight Into Fitness And Health For Individuals With Breast Cancer?
Bibliographic record
Abstract
PURPOSE: Step-based measures recorded in daily life may help identify individuals with cancer who have low fitness levels and poor health—information that could aid health care providers in optimizing care. Thus, we examined links between low step counts and intensity (cadence, peak steps) and lower health-related fitness and patient reported health. METHODS: The Alberta Moving Beyond Breast Cancer Study enrolled 1,528 individuals newly diagnosed with breast cancer and measured 5 components of health-related fitness (12 measures), and patient reported health (9 indicators) near diagnosis, and one-year later. Step counts and intensity were measured at baseline using activPAL (7-days). Logistic regression estimated cross-sectional associations (odds ratios; OR) at baseline, and prospectively between lower quintiles of baseline stepping and low fitness and poorer health at one year, adjusting for age, demographics, height, weight, and cancer diagnosis/treatment. RESULTS: At baseline 1,408 individuals with breast cancer (mean age 56 yrs; early stage (90%)) provided valid activPAL measures (mean 14.8 hrs/d, 5.5 days of wear). Taking <5,000 steps/d (lower quintile) at baseline was associated with low fitness (9 of 12 measures) and poorer health (8 of 9 indicators) at baseline and remaining in low fitness (7 of 12 measures) and poorer health (5 of 9 indicators) one year later. Taking <5,000 steps/d at baseline was significantly associated with greater odds of moving from favorable to unfavorable/low fitness and poorer health categories one year later (VO2peak (OR = 2.6), curl ups (OR = 1.8), chest endurance (OR = 2.4), self-reported health (OR = 2.4), physical quality of life (OR = 2.1), fatigue (OR = 1.8)). Preferred cadence and peak stepping were inconsistently associated after adjustment for total steps. CONCLUSIONS: Easily obtained step counts measured near diagnosis could facilitate wellness planning across the treatment timeline and into survivorship for individuals with breast cancer. Supported by: Canadian Institutes of Health Research (Awards: 107534, 155952, 159927)
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".