World Health Organization Evidence-Based Self-Help Plus Intervention for Stress Management via Chatbot: Protocol for Adaptation to a Tech-Enabled Model
Bibliographic record
Abstract
BACKGROUND: This paper describes the adaptation of an evidence-based intervention for mental health, Self-Help Plus (SH+), to a digital platform, aiming to expand mental health support through innovative technological solutions. The SH+ intervention, designed by the World Health Organization (WHO), is a low-intensity, self-guided program aimed at improving mental well-being. The intervention has been integrated into a chatbot-driven mobile app called "ALBA" (Automated Well-Being Assistant). OBJECTIVE: This protocol describes the stages of transition (porting) from a traditional face-to-face, group-based approach to a tech-enabled model, tailored specifically for women with breast cancer and pregnant women. The adaptation of this approach is described, focusing on 2 key aspects: (1) the shift from in-person delivery to a tech-enabled intervention and (2) the customization of the intervention to address the unique needs of the selected target groups. METHODS: The development of the ALBA app involved a collaborative, multidisciplinary approach combining expertise in psychology, eHealth IT, interaction design, and specific domain knowledge related to pregnancy and oncology. The development process followed 2 primary methodologies: user-centered design and service design. These approaches emphasize understanding user needs, promoting iterative improvements, and continuously incorporating user feedback. The development followed the Obesity-Related Behavioral Intervention Trials (ORBIT) model, which involves a cycle of literature review, stakeholder consultation, content development, software development, and evaluation. At the same time, specific attention was dedicated to (1) adapting the SH+ protocol to suit the chatbot-driven ALBA platform, (2) tailoring content to the target population, and (3) incorporating interactive features to improve engagement and potential efficacy. RESULTS: The manuscript outlines the key steps and processes involved in adapting the SH+ intervention into the ALBA digital platform. This includes an overview of the challenges and opportunities of translating a face-to-face, group-based intervention into a tech-enabled model and provides insight into how customization for specific populations, such as women with breast cancer and pregnant women, was integrated into the design of the platform. This is in view of informing future studies on the development and adaptation of already validated protocols into mobile health (mHealth) interventions in the field of mental health and mental well-being. CONCLUSIONS: The digital adaptation of the SH+ intervention into the ALBA platform could represent a significant step forward in expanding the accessibility and personalization of mental health interventions in potentially different settings and target groups. The use of virtual coaching applications can play a central role in improving the availability and ease of access of psychological support, even in different and mHealth formats. Future developments could include further testing in real-world settings to expand (1) adaptations for various target groups and (2) the purpose of use to range from primary prevention to more clinically focused interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69644.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.012 |
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".