A Digital Behavioral Activation Intervention (JuNEX) for Pregnant Women With Subclinical Depression Symptoms: Explorative Co-Design Study
Notice bibliographique
Résumé
BACKGROUND: Digital interventions are gaining increasing interest due to their structured nature, ready availability, and self-administered capabilities. Perinatal women have expressed a desire for such interventions. In this regard, behavioral activation interventions may be particularly suitable for digital administration. OBJECTIVE: This study aims to exploratorily investigate and compare the feasibility of the internet-based self-help guided versus unguided version of the Brief Behavioral Activation Treatment for Depression-Revised, an empirically supported in-person behavioral activation protocol, targeting pregnant women with subclinical depression symptoms. A user-centered design is used, whereby data are collected with the intent of evaluating how to adjust the intervention in line with pregnant women's needs. Usability and user engagement were evaluated. METHODS: A total of 11 Italian pregnant women with subclinical depressive symptoms based on the Patient Health Questionnaire-9 (scoring<15) participated in this study; of them, 6 (55%) women were randomly assigned to the guided group (age: mean 32.17, SD 4.36 years) and 5 (45%) to the unguided group (age: mean 31, SD 4.95 years). The Moodle platform was used to deliver the interventions in an e-learning format. It consisted of 6 core modules and 3 optional modules; the latter aimed at revising the content of the former. In the guided group, each woman had weekly chats with their assigned human guide to support them in the homework revisions. The intervention content included text, pictures, and videos. Semistructured interviews were conducted, and descriptive statistics were analyzed. RESULTS: Collectively, the data suggest that the guided intervention was better accepted than the unguided one. However, the high rates of dropout (at T6: guided group: 3/6, 50%; unguided: 4/5, 80%) suggest that a digital replica of Behavioral Activation Treatment for Depression-Revised may not be feasible in an e-learning format. The reduced usability of the platform used was reported, and homework was perceived as too time-consuming and effort-intensive. Moreover, the 6 core modules were deemed sufficient for the intervention's goals, suggesting that the 3 optional modules could be eliminated. Nevertheless, participants from both groups expressed satisfaction with the content and found it relevant to their pregnancy experiences. CONCLUSIONS: Overall, the findings have emphasized both the intervention's merits and shortcomings. Results highlight the unsuitability of replicating an in-person protocol digitally as well as of the use of nonprofessional tools for the implementation of self-help interventions, ultimately making the intervention not feasible. Pregnant women have nonetheless expressed a desire to receive psychological support and commented on the possibilities of digital psychosocial supports, particularly those that are app-based. The information collected and the issues identified here are important to guide the development and co-design of a more refined platform for the intervention deployment and to tailor the intervention's content to pregnant women's needs.
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 | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».