Chatbot-Based Version of a World Health Organization–Validated Intervention for Stress Management in Patients With Breast Cancer (Self-Help Plus): Protocol for a Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Emerging digital tools play an innovative and key role in supporting women's psychological well-being throughout the different stages and challenges of cancer. The development and adoption of digital interventions, including chatbots and virtual coaches within smartphone apps, are increasingly recognized as valuable resources for enhancing women's mental health. OBJECTIVE: The aim of this paper is to present the research protocol for a pilot study designed as a proof-of-concept investigation. The study evaluates the feasibility, acceptability, and perceived utility of a mobile app delivering an acceptance and commitment therapy-based stress management intervention. The intervention is delivered through ALBA (A Well-Being Assistant), a virtual coach embedded within the TreC (an acronym for cartella clinica del cittadino, meaning "citizen's electronic health record") research platform-a mobile health ecosystem designed to support research and digital health interventions. ALBA guides users through 5 coaching sessions tailored for women undergoing breast cancer (BC) treatment. The chatbot-delivered app is an adaptation of Self-Help Plus, a World Health Organization (WHO)-validated stress management intervention, and is provided in text, audio, and video formats. The intervention's potential impact on participants' psychological well-being is also explored. METHODS: A convenience sample size of 50 participants will be identified to meet the study's objectives. Participants will be recruited using a convenience sampling approach from women receiving care at the Breast Unit of the Azienda Provinciale per Servizi Sanitari di Trento. ALBA will interact with the participants for 6 weeks. Specifically, there will be 1 coaching session per week, followed by weekly assigned acceptance and commitment therapy exercises to be performed between sessions. RESULTS: The app is expected to demonstrate high usability and engagement, aligning with the WHO Self-Help Plus protocol. Improvements in psychological well-being and quality of life are anticipated. Data from this pilot will be analyzed using both quantitative and qualitative methods, with a focus on assessing feasibility, acceptability, and perceived utility and usability in supporting women during BC treatment. CONCLUSIONS: Existing literature indicates a promising role for new technologies in delivering validated mental health interventions, highlighting the potential of digital interventions to address barriers related to social stigma and seeking assistance. This pilot is expected to provide valuable insights on the potential acceptability and usefulness of providing consistent mobile health psychoeducational support to women throughout the course of BC. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/65837.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.011 |
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".