Profiling sedentary behavior in breast cancer survivors: Links with depression symptoms during the early survivorship period
Bibliographic record
Abstract
Abstract Objective Depression symptoms are prevalent among breast cancer survivors (BCS). Reducing sedentary behavior (SED) may be a non‐pharmaceutical strategy for alleviating depression symptoms. However, little is known about SED among BCS. The present study aimed to: (i) describe SED behaviors among BCS and identify unique SED groups based on different SED dimensions; (ii) identify personal and cancer‐specific factors that discriminate SED clusters; and (iii) examine the association between SED clusters and depression symptoms. Methods Baseline self‐report demographic and medical information was collected from 187 BCS. SED and physical activity were assessed over seven days using an accelerometer. Self‐reported depression symptoms were reported three months later. Multiple dimensions of SED were identified and examined in cluster analysis. The clusters were examined for differences using multivariate analysis of variance and chi‐square analyses. The difference in depression symptoms among SED groups was assessed using an analysis of covariance. Results High and low SED groups were identified. Survivors in the high SED cluster were significantly older, heavier, less physically active, reported less education, and were more likely to have undergone lymph/axial node dissection. Women in the high SED cluster reported significantly higher depression symptoms prospectively (M = 9.50, SD = 6.07) compared to women in the low SED group (M = 6.89, SD = 5.18), F(8,179) = 4.97, p = 0.03, R2 = 0.34. Conclusions The importance of understanding multiple dimensions of SED among BCS was highlighted. Reducing SED during the early survivorship period may alleviate depression symptoms.
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 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 | 0.000 |
| 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".