Perceptions of Patient Engagement Applications During Pregnancy: A Qualitative Assessment of the Patient’s Perspective
Notice bibliographique
Résumé
BACKGROUND: With growing demand for medical information and health applications in pregnancy, the potential of electronic health (eHealth) and mobile health (mHealth) solutions in clinical care is increasingly unfolding. However, we still do not know how pregnant women engage with mobile apps, how such apps impact routine medical care, and whether benefit expectations are met. Whereas recent research has raised the subject of user distribution and analyzed the content of pregnancy applications, there is still a significant knowledge gap regarding what pregnant women like and dislike about pregnancy tools, along with how such interventions could be improved. OBJECTIVE: The aim of the study was to examine the perceptions and expectations of mobile and Web-based patient-engagement pregnancy applications. We assessed usability requirements, general acceptance of eHealth, and the impact of eHealth and mHealth pregnancy applications on the doctor-patient interaction and daily clinical routine. METHODS: A qualitative study was conducted at the maternity department of a major German university hospital. The sample included 30 women with low- to medium-risk pregnancies. Half of the patients were seen during outpatient care and half were hospitalized for several days. The extent and frequency of Web- and mobile phone app usage were assessed. Semistructured interviews were conducted and analyzed using systematic thematic analysis. RESULTS: Patients had a high demand for Web-based pregnancy applications. Study findings suggested a strong request for personalization, monitoring, and accessibility for frequent use as main themes derived from the interviews. Fostering patient empowerment in the doctor-patient relationship was also highly valued for a pregnancy app. Participants favored further integration of medical apps in their daily routine and pregnancy care. However, concerns were raised about content quality, trustworthiness of Web sources, and individual data security. CONCLUSIONS: eHealth and mHealth applications are a highly frequented source of information. Expectations and usability requirements for those applications are also high, thus posing a challenge to interdisciplinary service providers. Patients' attitude toward integrating apps in routine care settings was positive with a favorable influence on patient empowerment. Health care professionals should guide pregnant women toward a successful integration of these educational tools in pregnancy care.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,006 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».