Weight Management to Reduce Prostate Cancer Risk: A Survey of Men’s Needs and Interests
Bibliographic record
Abstract
Obese men have a higher rate of prostate cancer-related death than non-obese men, and obesity increases the risk of prostate cancer progression and biochemical recurrence. The purpose of this study was to assess needs and interests of men for a technology-driven weight loss intervention to reduce prostate cancer risk. We distributed a survey collecting demographic characteristics, health history, exercise and eating habits (and perception of those habits), current and prior attempts of health behavior change, and technology use. Survey answers were summarized by count and percent of total respondents. Completed surveys (N = 109) described men with a family history of prostate cancer (25%), a history of elevated prostate specific antigen (26%), and prostate cancer survivors (22%). We compared body mass index (BMI) to perception of weight; overweight and obese men perceived their weight as more normal than their BMI category suggests. Most men reported their diet needed minor improvement (74%), and 65% of men reported they are either currently trying to lose weight or interested in weight loss. Most respondents access the internet (92%), while text messaging (60%) and smartphone application use (40%) are less frequent, especially in men over 60. Our results revealed a need and willingness for lifestyle modification and suggest a need for evidence-based weight loss strategies and for addressing the misperception of weight status. A male-tailored intervention that implements technology could improve energy balance, hold men accountable to healthy behavior change, and promote dietary patterns in order to reduce prostate cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".