MP21-08 DEVELOPMENT AND EVALUATION OF A MOBILE HEALTH APPLICATION OFFERING REPRODUCTIVE HEALTH INFORMATION TO MEN IN THE GENERAL PUBLIC
Notice bibliographique
Résumé
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 ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,020 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».