Exploring Psychosocial Burdens of Diabetes in Pregnancy and the Feasibility of Technology-Based Support: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Gestational diabetes mellitus and type 2 diabetes mellitus impose psychosocial burdens on pregnant individuals. As there is less evidence about the experience and management of psychosocial burdens of diabetes mellitus during pregnancy, we sought to identify these psychosocial burdens and understand how a novel smartphone app may alleviate them. The app was designed to provide supportive, educational, motivational, and logistical support content, delivered through interactive messages. OBJECTIVE: The study aimed to analyze the qualitative data generated in a feasibility randomized controlled trial of a novel mobile app designed to promote self-management skills, motivate healthy behaviors, and inform low-income pregnant individuals with diabetes. METHODS: Individuals receiving routine clinical care at a single, large academic medical center in Chicago, Illinois were randomized to use of the SweetMama app (n=30) or usual care (n=10) from diagnosis of diabetes until 6 weeks post partum. All individuals completed exit interviews at delivery about their experience of having diabetes during pregnancy. Interviews were guided by a semistructured interview guide and were conducted by a single interviewer extensively trained in empathic, culturally sensitive qualitative interviewing of pregnant and postpartum people. SweetMama users were also queried about their perspectives on the app. Interview data were audio-recorded and professionally transcribed. Data were analyzed by 2 researchers independently using grounded theory constant comparative techniques. RESULTS: Of the 40 participants, the majority had gestational diabetes mellitus (n=25, 63%), publicly funded prenatal care (n=33, 83%), and identified as non-Hispanic Black (n=25, 63%) or Hispanic (n=14, 35%). Participants identified multiple psychosocial burdens, including challenges taking action, negative affectivity regarding diagnosis, diet guilt, difficulties managing other responsibilities, and reluctance to use insulin. External factors, such as taking care of children or navigating the COVID-19 pandemic, affected participant self-perception and motivation to adhere to clinical recommendations. SweetMama participants largely agreed that the use of the app helped mitigate these burdens by enhancing self-efficacy, capitalizing on external motivation, validating efforts, maintaining medical nutrition therapy, extending clinical care, and building a sense of community. Participants expressed that SweetMama supported the goals they established with their clinical team and helped them harness motivating factors for self-care. CONCLUSIONS: Psychosocial burdens of diabetes during pregnancy present challenges with diabetes self-management. Mobile health support may be an effective tool to provide motivation, behavioral cues, and access to educational and social network resources to alleviate psychosocial burdens during pregnancy. Future incorporation of machine learning and language processing models in the app may provide further personalization of recommendations and education for individuals with DM during pregnancy. TRIAL REGISTRATION: ClinicalTrials.gov NCT03240874; https://clinicaltrials.gov/study/NCT03240874.
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 | 0,019 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».