Woebot for Postpartum Mood and Anxiety: A Randomized Controlled Trial Evaluating Feasibility, Acceptability, and Preliminary Efficacy of a Mobile CBT Intervention (Preprint)
Notice bibliographique
Résumé
BACKGROUND Postpartum psychological distress, ranging from transient mood and anxiety disturbances to full-syndrome postpartum depression (PPD), is prevalent. Many postpartum individuals lack access to evidence-based interventions due to stigma and insufficient provider availability. The treatment gap is particularly pronounced among historically marginalized groups, including Black, Hispanic/Latina, and low-income mothers, who face higher PPD prevalence and systemic barriers to care. Digital health interventions offer scalable, accessible, and culturally informed emotional support to address these disparities. OBJECTIVE To evaluate the feasibility, acceptability, and preliminary efficacy of a smartphone application-delivered intervention for managing stress, anxiety, and mood in a diverse postpartum population. METHODS This randomized controlled trial recruited participants from the PowerMom study, a digital platform for maternal health research. Eligible individuals (≥16 years, <3 months postpartum) were randomized to Woebot for Postpartum Mood and Anxiety (W-PPMA) or a waitlist control condition. W-PPMA, an investigational digital mental health intervention, features a relational agent delivering cognitive behavioral therapy (CBT)-based psychoeducation via text-based conversations. Primary outcomes included feasibility, acceptability, and satisfaction at 8-week end-of-intervention (EOI). The secondary outcome was change in self-reported depressive symptoms (PHQ-8) at 8-week EOI among participants with elevated baseline symptoms. Exploratory outcomes included anxiety (GAD-7), perinatal depression (EPDS), stress (PSS), mother-infant bond (MIB), and therapeutic alliance (WAI-SR), assessed at baseline, mid-treatment (4 weeks), EOI, and follow-ups at 12 and 16 weeks. The study followed CONSORT guidelines and received IRB approval. RESULTS Participants (N=267; W-PPMA=144, Waitlist=123) represented diverse sociodemographic, mental health, and pregnancy backgrounds. W-PPMA users engaged with the app a median (Q1, Q3) of 9.0 (5.0, 23.8) days over 4.0 (2.0, 7.0) active weeks and reported high feasibility, acceptance, and satisfaction (URPI-F= 31 (28, 34), URPI-A= 30 (28, 34), CSQ-8= 26 (24, 29)). The secondary outcome indicated a small but favorable effect of W-PPMA on depressive symptoms (Cohen’s d=-0.16). Exploratory analyses showed positive trends in perceived stress (PSS) and perinatal depression (EPDS) at EOI. Therapeutic alliance (WAI-SR) was highest among Black participants and those from socioeconomically disadvantaged neighborhoods (ADI ≥75) at Baseline, and at EOI, among those with military healthcare insurance and socioeconomically disadvantaged neighborhoods (ADI ≥75). Satisfaction (CSQ-8) was highest among those with a high school or GED education, highlighting accessibility. CONCLUSIONS Among a diverse postpartum cohort, W-PPMA demonstrated feasibility, acceptability, and modest preliminary efficacy in reducing depressive symptoms. Exploratory findings suggest broader benefits for stress and mood management. High engagement and satisfaction highlight W-PPMA’s potential as a scalable, accessible, and culturally informed digital mental health tool. These findings underscore its potential to bridge gaps in postpartum mental health care, particularly for marginalized populations. Further research is warranted. CLINICALTRIAL Clinicaltrials.gov NCT05662605
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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| 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 | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».