MP21-08 DEVELOPMENT AND EVALUATION OF A MOBILE HEALTH APPLICATION OFFERING REPRODUCTIVE HEALTH INFORMATION TO MEN IN THE GENERAL PUBLIC
Bibliographic record
Abstract
You have accessJournal of UrologyInfertility: Epidemiology & Evaluation I (MP21)1 Sep 2021MP21-08 DEVELOPMENT AND EVALUATION OF A MOBILE HEALTH APPLICATION OFFERING REPRODUCTIVE HEALTH INFORMATION TO MEN IN THE GENERAL PUBLIC Mohammed Hassan, Ekaterina Kruglova, Eden Gelgoot, Kirk Lo, Peter Chan, Zeev Rosberger, and Phyllis Zelkowitz Mohammed HassanMohammed Hassan More articles by this author , Ekaterina KruglovaEkaterina Kruglova More articles by this author , Eden GelgootEden Gelgoot More articles by this author , Kirk LoKirk Lo More articles by this author , Peter ChanPeter Chan More articles by this author , Zeev RosbergerZeev Rosberger More articles by this author , and Phyllis ZelkowitzPhyllis Zelkowitz More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002006.08AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Infertility is defined as the inability to achieve pregnancy after 12 months of unprotected sexual intercourse. The diagnosis of male infertility places increasing burden and stress on couples undergoing fertility management. Our team previously performed a needs assessment survey of Canadian men about their fertility knowledge, which prompted our decision to develop a mobile health application (mHealth app) to provide reliable and accessible fertility information to men. This study evaluates if the app, Infotility XY, increased fertility knowledge in a sample of men in the general public. METHODS :The app content was written and vetted for accuracy and relevance by healthcare providers and experts in patient-centered care. A market research company recruited participants based on eligibility criteria: identified as male; 18-45 years old; had no children; not on fertility treatment; able to read and write in English/French; had Internet access. Participants first completed pre-questionnaires which asked about demographic characteristics and assessed knowledge of 24 known infertility risk factors (ex. age, smoking) and 9 factors that do not affect fertility (“non-risk factors”; ex. migraines). Participants then obtained access to the app for 2 weeks, after which they completed post-questionnaires. Continuous scores were calculated ranging from 0-24 for risk factors and 0-9 for non-risk factors; higher scores represent higher fertility knowledge. A paired samples t-test and Wilcoxon signed-rank test were used to determine whether mean knowledge scores significantly changed after using the app. RESULTS: 50 participants completed the study with a mean age of 31.4 years (SD=6.0). Overall, 96% of men reported that the app increased their fertility knowledge. Objectively, men correctly identified more risk factors after using the app (M=17.28, SD=4.38) compared to before (M=11.38, SD=4.84; t(49)=8.17, p<.001). However, on average, men correctly identified fewer non-risk factors after using the app (M=6.0) compared to before (M=7.0; Z=- 4.39, p<.001). CONCLUSIONS: Our study demonstrated that the app could increase fertility awareness among men. While participants have gained knowledge on the known risk factors for male infertility, it is more difficult to demystify the non-risk factors. These results along with those from additional evaluations in upcoming studies will allow us to further improve the contents and impact of this app for clinical use in counseling infertile couples. Source of Funding: CIHR (Canadian Institute of Health Research) © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e349-e349 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Mohammed Hassan More articles by this author Ekaterina Kruglova More articles by this author Eden Gelgoot More articles by this author Kirk Lo More articles by this author Peter Chan More articles by this author Zeev Rosberger More articles by this author Phyllis Zelkowitz More articles by this author Expand All Advertisement Loading ...
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.020 |
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".