Sedentary time transitions and associations with quality of life in cancer survivors during the COVID-19 pandemic
Bibliographic record
Abstract
Background Patterns in sedentary time (SED) and its impact on quality of life (QoL) in cancer survivors during the COVID-19 pandemic remains unknown. The purpose of this study was to 1) compare total and domain-specific SED before and during the pandemic; and 2) examine its association with QoL in a global sample of cancer survivors.Methods In an online survey, cancer survivors retrospectively self-reported domain-specific SED (e.g. transportation, television) before and during the pandemic via the Domain-Specific Sitting Time Questionnaire. QoL was assessed via the Functional Assessment of Cancer Therapy (FACT)-General and FACT-Fatigue. Paired t-tests compared daily SED before and during the pandemic. Analysis of covariance compared QoL among: those who remained high (>8 h/day), remained low (<8 h/day), increased (<8 h/day to >8 h/day), or decreased (>8 h/day to <8 h/day) daily SED.Results Among cancer survivors (N = 477, Mage=48.5 ± 15.4), 60.8% reported that their SED remained high, 19.7% remained low, 7.5% increased SED, and 11.9% decreased SED. Computer and television screen time significantly increased (p’s<.001), while SED during transportation significantly decreased (p<.001). Sub-group analyses revealed that those who reduced SED who were normal or underweight (p=.042) or were meeting physical activity guidelines (p=.031) had significantly less fatigue than those who increased or remained high in SED, respectively. Those who remained high in SED with <3 comorbidities (p’s =.005) had significantly better social well-being than those who increased SED.Conclusions As we transition to a post-pandemic era, behavioral strategies for cancer survivors should focus on reducing screen time to improve QoL and fatigue.
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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 | 0.002 |
| 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 | 0.000 | 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".