A feasibility study examining the effectiveness of a mobile intervention for reducing sitting time in prostate cancer survivors: RiseTx
Bibliographic record
Abstract
Background: Prostate cancer survivors (PCS) prescribed androgen deprivation therapy (ADT) are at risk for chronic disease that may be preventable with a reduction in sedentary behaviour (SED)–yet there are no known SED interventions among cancer survivors. This study aims to develop and test a SED intervention using a mobile application to reduce sitting time among PCS. Methods: Nine focus groups of 90 minutes each were conducted. PCS were recruited from local hospitals and a community group in Toronto, Ontario between Nov 2013-Apr 2014. Probe questions assessed perceptions regarding SED, strategies to interrupt SED, and design preferences for a mobile application. Data were transcribed verbatim and a thematic analysis was conducted. Results: The sample was 27 PCS with a mean age of 73.5±8.1 years, mean BMI was 27.1 ± 4.3, mean number of months since diagnosis was 98.8 ± 69.4, 55.7% had localized prostate cancer, and 64.0% had ADT administered continuously. More than half of PCS were aware of the health risks with SED and were interested in a mobile application to reduce SED. PCS expressed that the design should be easy to use; have an alerting function; have the ability to track and monitor activity; tailored to the individual; and have a goal-setting function. Briefly, PCS in the intervention will receive a Jawbone (i.e., a wrist-worn device that provides sensory feedback to stand after prolonged sitting), and a mobile application that will provide various support tools over a 12-week period. Conclusions: PCS were aware of some of the health risks of SED and expressed interest in a mobile application to reduce SED that is easy to use and individualized. These findings are now being used to develop and evaluate a novel mobile application to improve health outcomes among PCS. Acknowledgments: This study was funded by Prostate Cancer Canada-Movember Discovery Grants
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".