A Chatbot-Based Version of a World Health Organization–Validated Intervention (Self-Help Plus) for Stress Management in Pregnant Women: Protocol for a Usability Study
Notice bibliographique
Résumé
BACKGROUND: Pregnancy is a complex period involving significant physical, mental, and social changes in a woman's life, affecting her psychological well-being. According to the literature, anxiety, stress, and depression are common symptoms among pregnant women. Promoting a healthy lifestyle with a focus on mental health is essential. In this context, digital solutions such as coaches on smartphones are emerging as valuable tools to support the psychological well-being of pregnant women without existing disorders. OBJECTIVE: This study aims to present the research protocol of a pilot study designed as a proof-of-concept investigation. The study evaluates the feasibility, acceptability, and utility of an acceptance and commitment therapy-based stress management mobile app. The primary objective is to explore the feasibility of using a coach, ALBA (A Well-Being Assistant), developed within the TreC Ricerca app, to promote women's psychological well-being during pregnancy through 5 sessions based on acceptance and commitment therapy. The pre- and postintervention effects on psychological well-being will also be explored as a secondary objective, serving as a proxy for the potential impact of the intervention. METHODS: The study serves as a proof-of-concept investigation, where a small sample size (N=50) is deemed adequate to fulfill the study's objectives. Participant recruitment will be conducted among pregnant women affiliated with the pregnancy care services of the Azienda Provinciale per i Servizi Sanitari di Trento, using a convenience sampling approach. ALBA will interact with the participating women for 6 weeks, between the 14th and 26th weeks of gestation. Specifically, there will be 1 session per week, which the woman can choose, to allow more flexibility regarding her needs, supplemented by ALBA-supported exercises to be performed between sessions. This study adopts a mixed methods approach, combining quantitative and qualitative data collection and analysis. Usability and engagement are assessed using the System Usability Scale, Chatbot Usability Questionnaire, User Engagement Scale-Short Form, and the User Mobile Rating Scale. Moreover, other quantitative outcome measures include levels of stress, anxiety, depression, emotional regulation, psychological flexibility, coping strategies, self-efficacy, and overall well-being, along with qualitative data from semistructured interviews. Finally, the analysis of the data gathered in this study will primarily adopt descriptive statistics and a text mining approach, focused on evaluating the attainment of the study objectives and changes over experimental time. RESULTS: The psychoeducational approach aims to yield notable outcomes regarding the usability and engagement of women with ALBA. Furthermore, an anticipated enhancement in psychological well-being and quality of life is expected. CONCLUSIONS: Existing literature indicates a preference among women in the perinatal period for online support, highlighting the potential of digital interventions to address barriers related to social stigma and seeking assistance. In this context, ALBA emerges as a valuable resource, providing consistent psychoeducational support for women throughout pregnancy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/53891.
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 | 0,016 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,007 |
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 ».