Can Step-Based Metrics Predict Current and Future Health-Related Fitness and Patient-Reported Outcomes among Women Diagnosed with Breast Cancer?
Bibliographic record
Abstract
PURPOSE: Wearable sensors that track physical activity in daily life may offer insights that help health care providers optimize care plans for individuals with cancer. Therefore, we examined the links between lower health-related fitness and worse patient-reported health and various step-based metrics. METHODS: The Alberta Moving Beyond Breast Cancer Study enrolled 1528 women recently diagnosed with breast cancer and measured health-related fitness and patient-reported health outcomes near diagnosis and 1 yr later. Step counts and intensity (cadence, peak steps) were measured by activPAL® over 7 d at baseline. We estimated cross-sectional associations (odds ratios (OR)) at baseline and prospective associations between low baseline stepping and low fitness and poorer health at 1 yr, adjusting for age, demographics, height, weight, and cancer diagnosis/treatment. RESULTS: At baseline, 1408 breast cancer survivors (mean age, 56 yr; early stage (90%)) provided valid activPAL measures (mean, 5.5 d of wear). Taking <5000 steps per day (lower quintile) at baseline was associated with lower aerobic fitness, muscular strength and endurance, lower physical and mental quality of life, and greater fatigue and upper extremity disability at baseline and 1 yr later. Taking <5000 steps per day at baseline was associated with a greater risk of moving from favorable to unfavorable categories of aerobic fitness (OR, 2.64), curlups (OR, 1.84), chest endurance (OR, 2.38), self-reported health (OR, 2.37), physical quality of life (OR, 2.13), and fatigue (OR, 1.81) 1 yr later. Preferred cadence and peak stepping were inconsistently associated after adjustment for total steps. CONCLUSIONS: Although our findings need to be replicated, they suggest that simple step counts measured near diagnosis could help health care providers assess the fitness and health status of women recently diagnosed with breast cancer and improve their survivorship care plans.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".