Improving adherence and health outcomes in testicular cancer survivors using a mobile health-based intervention: A mixed-methods pilot study
Bibliographic record
Abstract
Objective: Testicular cancer (TC) is one of the most common cancers among young men, with survival rates exceeding 97% due to effective treatments. Post-treatment follow-up care is important for long-term survival and monitoring psychosocial symptoms, yet TC survivors (TCS) show poor adherence to post-treatment care. Mobile-health-based interventions show high acceptability in men with cancer. This study will examine the feasibility of using the Zamplo health app to improve adherence to post-treatment care and support psychosocial outcomes in TCS. Methods: This mixed-methods, longitudinal, single-arm pilot study will recruit N = 30 patients with a diagnosis of TC who finished treatment within ≤ 6 months and are currently aged ≥18 years old. Adherence to follow-up appointments (e.g. blood work, scans) will be assessed (primary outcome), and measures for fatigue, depression, anxiety, sexual satisfaction and function, social roles satisfaction, general mental and physical health and body image (secondary outcomes) will be completed at four-time points: baseline, 3, 6 and 12 months. One-on-one semi-structured interviews will be conducted post-intervention (month 12). Results: Improvements in post-treatment follow-up appointment adherence and psychosocial outcomes will be analyzed using descriptive statistics, paired samples t-tests to determine changes across time points 1 through 4, and correlation analysis. Qualitative data will be analyzed using thematic analysis. Conclusion: Findings will inform future, larger trials that incorporate evaluation of sustainability and economic implications to improve adherence to TC follow-up guidelines. Findings will be disseminated via infographics, social media, publications and presentations conducted in partnership with TC support organizations and at conferences.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.005 | 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".