Optimizing a Novel Smartphone App to Prevent Postpartum Depression Adapted From an Evidence-Based Cognitive Behavioral Therapy Program: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Postpartum depression (PPD) is more common among pregnant patients who have unmet social needs, such as financial stress or food insecurity, compared to those who do not. Mothers and Babies (MB) is a cognitive behavioral therapy (CBT)-based program that prevents up to 50% of de novo PPD when provided in-person to low-income Spanish- and English-speaking pregnant people without depression. MB's reach has been limited by the need for trained personnel to support the program. Transforming MB into a smartphone application (app) may mitigate this key barrier to scaling MB. OBJECTIVE: To utilize qualitative data from target end-users to create and optimize MBapp, a novel app centered on the MB program. METHODS: Draft wireframes of MBapp were created in English and Spanish with CBT-based modules adapted from MB. These wireframes included several features shown previously to sustain app engagement: 1) push notifications delivered at participant-preferred times; 2) text-, graphic-, and video-based content; and 3) gamification with digital rewards for app engagement. English- or Spanish-speaking individuals with public health insurance who were between 32 weeks' gestation and six months postpartum and owned smartphones were eligible to consent for individual in-depth interviews. Individuals with prior or current depression were excluded. Interviews were recorded, transcribed, and analyzed using deductive and inductive codes to characterize opinions about MBapp and perceptions of challenges and facilitators of use of MBapp or other perinatal or mental health apps. End-user feedback led to major modifications to the wireframes. Each of these changes was categorized according to the Framework for Modification and Adaptation (FRAME), an established method of systematically reporting adaptations and modifications to evidence-based interventions via end-user feedback. Recruitment ceased with content saturation, defined as three successive participants providing only positive feedback on MBapp's wireframe, without further suggestions for improvement. RESULTS: 25 interviews were completed. Participants were racially and ethnically diverse, generally representing our target end-user population, and 48% of interviews were conducted in Spanish. Participants' suggestions to improve MBapp were categorized within the FRAME as adaptations that improved either content or context to optimize reach, retention, engagement, and fit for end users. Specifically, the following features were added to MBapp secondary to end-user feedback: 1) audio narration; 2) "ask a clinician" non-urgent questions; 3) on-demand module summaries accessible upon module completion; and 4) choice to defer assessments and start the next module. Participants also provided insights into features of perinatal or mental health apps they found appealing or unappealing to understand preferences, challenges, and (non)negotiables for MBapp. CONCLUSIONS: Adapting MBapp to incorporate end users' perspectives optimized our digital PPD prevention intervention, ideally increasing its appeal to future users. Our team's next steps will confirm that MBapp is a feasible, acceptable intervention among English- and Spanish-speaking perinatal people at risk of PPD.
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 | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».