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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".