Evaluation of a Mobile Health App Offering Fertility Information to Male Patients With Cancer: Usability Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Cancer and its treatment can adversely affect male fertility. Although sperm banking is an effective fertility preservation method, there is an unmet need for information and support surrounding these issues. OBJECTIVE This usability study evaluates a mobile health app providing male patients with cancer with credible information about the impact of cancer and its treatment on fertility and fertility preservation. METHODS Participants were recruited by a market research firm. Eligibility criteria were men who were 18-45 years of age, identified as male, diagnosed with new or recurring cancer within 1 year, not in fertility treatment, able to read and write in English or French, and had internet access. App usage was tracked for 2 weeks. After app use, participants provided qualitative feedback about their experiences using the app as well as quantitative data regarding their sperm banking decisions, perceived change in fertility knowledge, evaluation of the app’s information on the Information Assessment Method, and the app’s quality on the user version of the Mobile App Rating Scale. RESULTS The sample included 40 men aged 27-45 years. Approximately 68% (27/40) indicated that no one had previously spoken to them about the impact of cancer on fertility, and 85% (34/40) had not received information on fertility preservation. Approximately 83% (33/40) found the app’s information relevant, and 85% (34/40) said that it increased their fertility knowledge. Approximately 23% (9/40) made a decision about sperm banking after using the app. Participants rated the app’s quality highly, with mean scores (out of 5) of 4.14 for information, 4.06 for functionality, 3.84 for aesthetics, and 3.63 for engagement. CONCLUSIONS The app proved to be useful for male patients with cancer, suggesting that mobile health resources could be beneficial to incorporate into clinical care to enable shared decision-making about fertility.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".