Implementation of an exercise behavior change clinic for men with prostate cancer: Baseline results.
Bibliographic record
Abstract
169 Background: Regular physical activity (PA) and resistance training (RT) has been shown to improve the health of prostate cancer survivors (PCS), however participation rates are low. To address survivorship needs and support behaviour change, the Prostate Cancer Supportive Care (PCSC) program implemented an Exercise Physiology (EP) clinic using one-on-one exercise counselling. Our objective was to understand the PA and RT characteristics of PCS and examine the effect of an EP clinic on behaviour change. Methods: After enrolment in the PCSC program, PCS were offered a consultation with an Exercise Physiologist. Subsequent consultations occurred at 3, 6 and 12 months. Clinical data collected at each consultation included physical measures, stage of change (TTM), current exercise (GLTEQ and exercise summary), fatigue (FACIT Fatigue and SIFIR), barriers and facilitators to exercise and a safety screening (PAR-Q+). Information was used during the EP clinic to set PA goals and create an action plan for behaviour change, reviewed each visit. Results: 71 PCS, median age 67 (range 50-86) attended the EP clinic between July 2015 and August 2016. 76% had received primary treatment and 24% were on active surveillance. At first consultation, 63% had an at-risk health profile, with an average BMI of 27kg/m2 (overweight), average waist circumference of 102.6cm (high) and insufficient PA, with only 15% meeting both PA and RT guidelines (150 mins moderate-to-vigorous PA and 2 RT sessions weekly). Average reported fatigue was moderate (4/10 on SIFIR). Stage of change (TTM) showed 44% of PCS at pre-contemplation, contemplation or preparation stages and 54% at action or maintenance stages. Self-reported barriers to exercise included lack of time (24%), laziness (19.7%) and fatigue (18%), while exercising with a spouse/friend/team was the main facilitator to exercise (16.9%). Conclusions: The baseline PA and RT levels of EP clinic PCS is similar to population norms in that the majority of PCS are not meeting current guidelines. Our results demonstrate that the implementation of an EP clinic is feasible. Future follow-up of this cohort will evaluate whether the program creates sustainable PA behaviour change in PCS.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".