The Physical Activity and Cancer Control (PACC) framework: update on the evidence, guidelines, and future research priorities
Bibliographic record
Abstract
BACKGROUND: We proposed the Physical Activity and Cancer Control (PACC) framework in 2007 to help organise, focus, and stimulate research on physical activity in eight cancer control categories: prevention, detection, treatment preparation/coping, treatment coping/effectiveness, recovery/rehabilitation, disease prevention/health promotion, palliation, and survival. METHODS: This perspective paper provides a high-level overview of the scientific advances in physical activity research across cancer control categories, summarises current guidelines, updates the PACC framework, identifies remaining and emerging knowledge gaps, and provides future research directions. RESULTS: Many scientific advances have been made that are reflected in updated physical activity guidelines for six of the cancer control categories apart from detection and palliation. Nevertheless, the minimal and optimal type, dose, and timing of physical activity across cancer control categories remain unknown, especially for the understudied population subgroups defined by cancer type, age, race/ethnicity, and resource level of regions/countries. CONCLUSION: To achieve the full benefit of physical activity in cancer control, future research should use innovative study designs that include diverse at-risk populations and understudied cancer sites. Additionally, effective behaviour change strategies are needed to increase physical activity levels across populations that use implementation science to accelerate the translation from evidence generation into practical, real-world interventions.
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 | 0.062 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".