Associations Between Domain-specific Sedentary Behaviour And Perceived Cognitive Function Of Cancer Survivors
Bibliographic record
Abstract
Cancer survivors spend the majority (~66%) of time in sedentary behaviours (SED), which can lead to deleterious health effects such as including cancer-related cognitive impairment (CRCI) that impact quality of life. Reducing SED has shown to reduce the impact of CRCI, however, the relationship between domain-specific SED and cognitive function in cancer survivors remains unknown. PURPOSE: To examine the associations between domain specific-SED and perceived cognitive function in cancer populations during the COVID-19 pandemic. METHODS: This study was a secondary analysis from an online survey in adult cancer survivors globally. Demographic (e.g., age, sex), and medical (e.g., cancer type) were self-reported. SED was assessed using the Domain-specific Sitting Time Questionnaire. Perceived cognitive function was assessed using the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) scale, which comprised of four subscale domains (e.g., perceived cognitive abilities [PCA]). Linear regressions were used to examine the association between change in SED domains (i.e., total, transport, work, television, computer, leisure) and FACT-Cog scores. RESULTS: Participants (N = 393, Mage = 48.4 ± 0.8) were primarily post-treatment (61.6%), breast (26.8%) cancer survivors. Change in leisure-time SED was significantly associated with PCA scores (β = .10; 95% CI: 0.00, 0.02; p = .04), where decreasing leisure-time SED was associated with improvements on PCA. These results indicate that for a one-point change on PCA, participants would need to decrease their leisure-time SED by 125 min/day. Changes in other domain-specific SED did not result in significant improvements (p > 0.05) on the total FACT-Cog or subscales. CONCLUSIONS: A reduction in time spent on leisure-specific SED is associated with modest improvements in PCA. Interventions should consider reducing leisure-specific SED to enhance PCA. Future research should utilize longitudinal designs to supplement these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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".