The Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Gestational diabetes is a type of diabetes that develops during pregnancy and increases the risk of developing type 2 diabetes later in life. The rising prevalence of gestational diabetes mellitus (GDM) highlights the need for more comprehensive treatment strategies, with a particular emphasis on supporting maternal self-management. We showed recently that a mobile app, eMOM, where glucose, nutrition, and physical activity are combined within a single app, significantly improves multiple clinical outcomes among persons with gestational diabetes. OBJECTIVE: This study aims to explore the effects of the eMOM on maternal self-discovery and learning, autonomous motivation to manage GDM, and psychological well-being. Additionally, we examine the correlation between improved maternal clinical outcomes and change in autonomous motivation. We also assess the acceptance and usability of the eMOM app. METHODS: Building upon the original randomized controlled trial (RCT), in which the intervention arm used a mobile app (eMOM), we conducted a mixed methods study that included an investigation of eMOM log files, semistructured interviews on self-discovery, and an examination of questionnaires assessing motivation (Treatment Self-Regulation Questionnaire and Perceived Competence Scale), depression (Edinburgh Postnatal Depression Scale), technology use and acceptance (Unified Theory of Acceptance of Use of Technology questionnaire), and usability (modified Software Usability Measurement Inventory). Additionally, we monitored participants' stress levels using wearable electrocardiographic devices (FirstBeat Bodyguard 2). A total of 148 individuals participated in the original RCTs, with 76 in the intervention arm and 72 in the control arm. From the intervention arm, 18 participants were randomly selected for interviews in this study. RESULTS: Results show that the use rate of eMOM was high, and novel visualization supported self-discovery in persons with GDM. Most participants (17/18, 94%) indicated that the eMOM app helped to find the associations between their daily activities and glucose levels. Especially having nutrition visualized together with glucose was highly appreciated. Participants also reported learning about the associations between physical activity and glucose levels. No differences were observed between the intervention and control arms in autonomous motivation, depression, or stress. Furthermore, there were no correlations between improved clinical outcomes and changes in motivation. Accessibility and usability ratings were consistently high throughout the intervention. CONCLUSIONS: The eMOM mobile app combining data from continuous glucose monitor, food diary, and physical activity tracker supports maternal self-discovery related to GDM without contributing to depression or adding extra stress. This encourages the use of such mobile apps in maternity care. Notably, motivational factors did not correlate with the positive outcomes observed in our prior RCT, suggesting that self-discovery has a greater impact on clinical results. TRIAL REGISTRATION: ClinicalTrials.gov NCT04714762; https://www.clinicaltrials.gov/study/NCT04714762.
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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 | 0,010 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».