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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".