Baseline Characteristics of Individuals with Metastatic Cancer Enrolled in the Alberta Cancer Exercise Study and 12-Week Findings for Symptom-Related and Physical Fitness Measures
Bibliographic record
Abstract
Exercise has been found to be safe and beneficial for people with advanced cancers, but more research is needed to understand how best to design and implement exercise programming. The Alberta Cancer Exercise (ACE) study examines the effectiveness and implementation of a 12-week community-based exercise program in Alberta, Canada, for people diagnosed with cancer. Here, we describe the characteristics of individuals with metastatic cancer enrolled in the ACE program and report 12-week changes in self-reported and objective outcomes. Of 306 participants, 274 (89.5%) completed the 12-week study. Many participants were female (65.4%), with ≥1 comorbidity (71.9%), and on active cancer treatment (74.8%). Common cancer types included breast (33.7%), genitourinary (16.7%), and digestive (15.0%). Frequent sites of metastasis were bone (44.8%), liver (28.8%), and lung (25.8%). The mean exercise attendance rate was 73.6%. One exercise-related adverse event (0.3%) and one non-exercise-related adverse event (0.3%) occurred, both in individuals with brain metastases. Participants demonstrated strong interest and engagement in exercise, with significant improvements in weekly physical activity, symptoms, quality of life, and physical fitness. Greater benefits were found among subgroups of male participants, those not undergoing chemotherapy, and those receiving group personal training or virtual circuit training. A low rate of adverse events is anticipated.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".