Impact of prostate cancer treatments on exercise behaviors of men attending an exercise counselling service implemented as standard care.
Bibliographic record
Abstract
e22057 Background: Exercise is an effective management strategy to reduce treatment side effects experienced by prostate cancer survivors (PCS), however, most PCS are insufficiently active. Exercise counselling implemented into medical clinics has the potential to address this gap. Currently, little is known about the effectiveness of exercise counselling in PCS and the impact of prostate cancer treatments on exercise behaviors. Methods: We conducted a retrospective chart review on PCS who attended an exercise counselling service implemented as standard care within the Prostate Cancer Supportive Care (PCSC) program. The service is one-one-one with a certified exercise physiologist and is offered to PCS at any disease state after diagnosis. Follow-up appointments are encouraged at 3-, 6- and 12-months. Physical measures, exercise volume, treatment side effects and goal setting are recorded each visit, with the overall aim of increasing physical activity. The analysis compared baseline and final visit exercise levels and the effect of treatment type, from July 2015 to October 2017. Results: 128 PCS (mean age = 67.6 years, SD ±7.0) attended an average of 2.5 exercise counselling sessions over 182 days. From baseline to final visit, clinically meaningful and significant increases were seen in weekly moderate-to-vigorous physical activity (MVPA) minutes (+65, p < 0.01), total weekly activity (Leisure Score Index) (+11.8, p < 0.0001), weekly resistance training sessions (+0.67, p < 0.001) and the proportion of PCS meeting the current MVPA and resistance training guidelines (+20.2% and +22.7%, p < 0.01). The largest improvements in MVPA were seen in PCS on active surveillance (p = 0.01), in PCS aged 60 to 70 years (p = 0.02) and in PCS who had received surgery (p < 0.0001). Men treated with brachytherapy were less likely to meet aerobic exercise guidelines at their final appointment (p < 0.05). Conclusions: An exercise counselling service as standard care was effective at improving exercise behaviors in PCS, however the magnitude of change varied depending on the nature of treatment. Future work will focus on the treatment-specific needs of PCS when implementing exercise interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".