Joint Associations of Sedentary Time and Intensity-Specific Physical Activity With Cancer Mortality: A Device-Based Cohort Study of 72,458 UK Adults
Bibliographic record
Abstract
BACKGROUND: There are no studies examining the prospective joint association of device-based measures of sedentary time and physical activity (PA) with cancer mortality. We examined the joint associations of sedentary time and intensity-specific PA with cancer mortality in 72,458 adults from UK Biobank. METHODS: Participants wore an Axivity AX3 accelerometer on their dominant wrist for at least 3 days (with at least 1 weekend day). Cox regression was performed to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for joint associations of sedentary time and intensity-specific PA (light [LPA], moderate [MPA], and vigorous PA [VPA]) with cancer mortality (reference group: high intensity-specific PA and low sedentary time) adjusted for confounders and mutually adjusted for other PA intensities. RESULTS: Associations between sedentary time and cancer mortality were stronger among participants with low PA, irrespective of the intensity. Compared with participants with lower sedentary time (<11 h/d) and high MPA (median of 49 min/d), HR were 1.27 (95% CI, 0.90-1.78) for high sedentary time and high MPA, 1.35 (95% CI, 1.03-1.77) for high sedentary time and medium MPA (49 min/d), and 1.49 (95% CI, 1.15-1.92) for high sedentary time and low MPA (13 min/d). HR for high sedentary time and low light PA (61 min/d) and high sedentary time and low vigorous PA (1 min/d) were 1.25 (95% CI, 1.02-1.59) and 1.57 (95% CI, 1.20-2.06), respectively. CONCLUSIONS: Relatively large amounts of LPA and MPA and small amounts of VPA appeared to attenuate the association between sedentary time and cancer mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".