Landscape of Physical Activity and Quality of Life Research in Breast Cancer Survivors: Topic Modeling Analysis
Bibliographic record
Abstract
Background/Objectives: Physical activity (PA) is widely recognized as a beneficial approach to improving the health-related quality of life (HRQoL) of breast cancer survivors. This study explored key research topics and emerging trends in studies related to PA and HRQoL among breast cancer survivors. Methods: Titles and abstracts of 3847 English-language research articles (2000–2024) were retrieved from PubMed, EMBASE, Web of Science, and Scopus using keywords related to ‘breast cancer’, ‘PA/exercise’, and ‘HRQoL’. A text-mining algorithm based on the Dirichlet-multinomial regression approach in Python was applied to identify the top 10 research topics and their trends over time. Results: In total, 10 key topics emerged: (1) Quality of Life and Well-being, (2) Cancer Treatment and Health-Related Fitness, (3) Supportive Care and Psychosocial Factors, (4) Survivorship, Palliative Care, and Integrative Medicine, (5) Physical Activity and Sedentary Behaviors, (6) Upper Limb-Related Side Effects, (7) Cancer-Related Fatigue and Symptoms, (8) Epidemiological and Clinical Factors, (9) Side Effects of Cancer Treatment, and (10) Weight Management. Among these, Topics 1, 2, 3, 8, and 9 followed upward trajectories, while others showed relatively stable trends. Conclusions: Findings highlight that PA research on breast cancer survivors’ HRQoL spans all stages of survivorship and considers both clinical outcomes and psychosocial and emotional well-being. Understanding how PA and HRQoL have been represented in research helps clarify which survivor needs have received attention and which remain underexplored. These thematic patterns underscore growing acknowledgement of survivors’ lived experiences and offer a roadmap for addressing future research and care gaps.
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 | 0.042 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".