Clinical trial · Interventional
Developing and Testing the Efficacy of AI-Chatbot-Based
Developing and Testing the Efficacy of AI-Chatbot-Based Delivering Body-Mind- Spirit Integrated With Mindfulness Compassion Therapy in Improving Holistic Well-being and Depression for Breast Cancer Survivors: A Large-scale Multi-sites Study
NCT07791368CI-TRIAL-00124199not yet recruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
this study aims to develop the Calm-Mind-Recovery-Journey AI-Chatbot delivering BMS-MC therapy for breast cancer survivors and examine its feasibility, acceptability, safety, and preliminary effects of holistic well-being and depressive levels, and coping skills (mindfulness status, self-compassion, emotional regulations), and breast cancer-specific QOL. The experiences of participants using AI-Chatbot will be explored.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AIAI-Chatbot CalmMindRecoveryJourney delivering BMS-MC therapy | Behavioral | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- experimental group
- description
- AIAI-Chatbot CalmMindRecoveryJourney delivering BMS-MC therapy
- interventionNames
- Behavioral: AIAI-Chatbot CalmMindRecoveryJourney delivering BMS-MC therapy
- type
- ACTIVE_COMPARATOR
- label
- waitlist control group
- description
- waitlist control group
- interventionNames
- Behavioral: AIAI-Chatbot CalmMindRecoveryJourney delivering BMS-MC therapy
Primary outcomes (2)
- measure
- Patient Health Questionnaire-9, PHQ-9
- timeFrame
- Measurement time-points are at baseline (T1), the 6 week (the end of AI-Chatbot guide) (T2), the 12 week (self-practices period) (T3)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: Diagnosis of breast cancer completing the active cancer treatments (surgery, chemotherapy, radiotherapy) Aged at least 20 Owned a smartphone (Android or iOS) with Line access Ability to communicate with the AI chatbot Willingness to participate in the research and interact with the AI-chatbot during the intervention. Exclusion Criteria: Diagnosis of another type of cancer Current receipt of psychotherapy Another online psychosocial or mental health intervention The presence of cognitive impairment or severe mental health conditions
References
Publications (1)
- BACKGROUNDAbd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020). Effectiveness and safety of using chatbots to improve mental health: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(7), e16021. https://doi.org/10.2196/16021 Boie, S. D., Glastetter, E., Lux, M. P., Balzer, F., von Kalle, C., Lenz, C., & Müller, U. (2025). Evaluating a chatbot as a companion for patients with breast cancer: Collaborative pilot study. JMIR Cancer, 11, e68426. Brown, T., Cruickshank, S., & Noblet, M. (2021). Specialist breast care nurses for support of women with breast cancer. The Cochrane Database of Systematic Reviews, 2(2), CD005634. https://doi.org/10.1002/14651858.CD005634.pub3 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. Dec 1983;24(4):385. Bornemann B, Singer T. Taking time to feel our body: steady increases in heartbeat perception accuracy and decreases in alexithymia over 9 months of contemplative mental training. Psychophysiology. Mar 2017;54(3):469-482. Chakraborty, C., Pal, S., Bhattacharya, M., Dash, S., & Lee, S.-S. (2023). Overview of chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science. Frontiers in Artificial Intelligence, 6. Conţ, M., Ciupe, A., Orza, B., Cohuţ, I., & Niţu, G. (2022). Career counseling chatbot using Microsoft bot frameworks. In I. Kallel, H. Kammoun, & L. Hsairi (Eds.), 2022 IEEE Global Engineering Education Conference (EDUCON) (pp. 1387-1392). IEEE. Duan, W., Klibert, J., Schotanus-Dijkstra, M., Llorens, S., van den Heuvel, M., Mayer, C. H., Tomasulo, D., Liao, Y., & van Zyl, L. E. (2022). Editorial: Positive psychological interventions: How, when and why they work: Beyond WEIRD contexts. Frontiers in Psychology, 13, 1021539. https://doi.org/10.3389/fpsyg