Using the PowerMom Digital Health Platform to Support Prenatal Mental Health and Maternal Health Outcomes: Observational Cohort Study
Notice bibliographique
Résumé
Background: Mental health disorders such as anxiety and depression are common among individuals of childbearing age. Such disorders can affect pregnancy and postpartum well-being. This study aims to study the impact of prenatal mental health on the pregnancy journey and highlights the use of mobile health technologies such as PowerMom for symptom tracking and screening. objectives: We collected data in a decentralized digital trial using the PowerMom platform to investigate the impact of maternal mental health throughout pregnancy. The goal was to understand how anxiety and depression influence pregnancy-related symptoms, pregnancy outcomes, and postpartum well-being. Methods: Survey data were collected via PowerMom, a bilingual mobile research platform that integrates patient-reported outcomes, wearable data, and electronic health records. Participants were divided into 2 cohorts: those who reported receiving treatment for anxiety or depression during pregnancy (n=571) and those who reported not receiving treatment (n=1505). We compared self-reported symptoms, prepregnancy conditions, complications from past pregnancies, delivery outcomes, and postpartum mental health between cohorts, using the Fisher exact test and the Kruskal-Wallis test for statistical analysis. Results: Participants receiving treatment for anxiety or depression reported higher instances of physical symptoms than those untreated, with significant differences for 13 symptoms including fatigue (80.2% vs 65.4%; adjusted P<.001), nausea and vomiting (69.7% vs 52.7%; adjusted P<.001), and stomach cramping and abdominal pain (64.0% vs 50.4%; adjusted P<.001). Participants receiving treatment also had a higher prevalence of several conditions prior to pregnancy than those not receiving treatment, with significant differences noted in 4 out of 10 conditions: endometriosis (14.0% vs 8.8%; adjusted P=.007), hypertension (10.9% vs 3.9%; adjusted P<.001), eating disorder (7.7% vs 3.1%; adjusted P<.001), and heart disease (2.8% vs 0.5%; adjusted P<.001). Participants receiving treatment also reported a higher prevalence of complications in past pregnancies than those not receiving treatment, with significant differences noted in 2 out of 7 complications: high blood pressure (9.9% vs 5.8%; adjusted P=.016) and preeclampsia (9.2% vs 5.5%; adjusted P=.021). No significant differences were observed in mode of delivery, epidural use, stillbirth, and miscarriage rates between the 2 cohorts. Postpartum, treated participants reported significantly higher mental health composite scores, indicating more severe mental health symptoms. A higher percentage of treated participants were at high risk for having perinatal mood disorder (38/83, 45.8%) than untreated participants (36/196, 18.4%; P<.001). Conclusions: The PowerMom platform demonstrated its value in facilitating remote, scalable data collection for maternal mental health research. Pregnant individuals reporting treatment for anxiety or depression experienced more physical symptoms and worse postpartum mental health outcomes than untreated individuals. These findings underscore the potential for mobile health technologies to support future interventional studies aimed at improving maternal mental health outcomes during pregnancy.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».