An mHealth App Designed for Fertility Patients: From Conception to Pilot Testing
Notice bibliographique
Résumé
Background Infertility is a distressing chronic condition affecting one in six couples; many of them seek to achieve a pregnancy via assisted reproductive technologies. Online resources for information and support are a mainstay of the self-help strategies of fertility patients. Patients seek explanations online about their diagnoses and treatment options, and hope to connect with others who have lived through a diagnosis of infertility. However, medical information found online is often inaccurate or hard to understand. Importantly, online forums that might provide social support are seldom monitored, allowing for the dissemination of potentially misleading information. In this study we describe the development of an mHealth app, Infotility, designed to provide evidence-based reproductive health information and a monitored message board to provide social support to users. Objective The objective was to describe the steps involved in the production of an mHealth app created specifically for fertility patients. Methods Our team followed guidelines established for the development of complex health interventions. To evaluate the existing online information sources, we assessed web-based information on infertility using standardized tools for readability, suitability and quality. To determine our stakeholders’ perspectives on what content to include in the app, a needs assessment survey was conducted in a sample of 289 male and 370 female fertility patients and 127 health care providers at clinics in Montreal and Toronto. A comprehensive review of the literature on the medical and psychosocial aspects of infertility was undertaken; summaries were then reviewed for accuracy and pertinence by patients, clinicians, researchers and professionals in the field of fertility. A technology partner was hired to create a user-friendly mobile app that contained the informational summaries, with separate portals for men and women, leading to content specifically curated for the user’s interests. There was also a closed discussion platform, “Connect”, monitored by 18 previous or current fertility patients. Peer monitors underwent one-on-one training and received an instructional manual created to assist with responding to forum messages from participants. Between November 2018 and April 2019, the app was pilot tested in a sample of 72 male and 187 female fertility patients to assess feasibility of recruitment, acceptability, and user satisfaction. Results Initial results show that men and women appreciated Infotility. The most popular sections included information on modifiable lifestyle risks (eg, diet, exercise, environment), and medical and psychosocial information. Men preferentially visited pages about lifestyle factors whereas the most common pages visited by women related to medical information. Importantly, the “Connect” social network logged 39 open forum conversations with 258 total posts, as well as 14 private messages. Both men and women lurked and posted on the board; women posted more often than men. Conclusions The design of a mobile health app for fertility patients should consider user experience and design along with the quality and accessibility of information. A fertility mHealth app should provide access to monitored social support through the interface and consider how to effectively tailor information to men and women.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».