Large Language Model-Based Emotional Management for Breast Cancer Patients

Summary

This study (study type: randomized controlled trial) aims to evaluate whether a large language model-based precision emotional management intervention can improve health-related quality of life among patients with primary breast cancer. The study will enroll adult women (≥18 years old) with pathologically confirmed primary breast cancer who own a smartphone, regularly use social media platforms, and have an expected survival of at least 6 months.

The primary questions this study seeks to answer are:

1. Does the intervention improve health-related quality of life (EORTC QLQ-C30 summary score) at 6 months post-intervention?
2. Does the intervention reduce anxiety, depression, cancer-related symptom burden, and unplanned hospitalizations in this population? With a control group in place, researchers will compare changes in health-related quality of life, anxiety, depression, and cancer-related symptom burden between the intervention group (receiving digital precision emotional management plus usual care) and the control group (receiving usual care only) to determine whether the intervention leads to significant improvements in these outcomes.

Participants will be required to:

1. Complete baseline and follow-up assessments of health-related quality of life, anxiety, depression, and cancer-related symptoms at 1, 3, and 6 months post-randomization.
2. For those in the intervention group, use the digital precision emotional management system autonomously for 6 months, including submitting self-reported emotional diaries and completing biweekly skill translation assessments.
3. For all participants, report any adverse events and unplanned hospitalizations during the study period.

Conditions

• Primary Breast Cancer

Interventions

BEHAVIORAL

Large Language Model-Based precision emotional intervention system

This intervention is a digital precision emotional management system based on a large language model, classified as a behavioral intervention. The system automatically analyzes text data from social media and in-app platforms to detect six basic emotions (joy, anger, sadness, fear, disgust, and surprise) in near real-time, and generates personalized patient labels by integrating patients' physical symptoms. Leveraging an evidence-based knowledge base, the system delivers tailored strategies for emotion regulation and symptom management, including emotion management techniques, symptom relief recommendations, and health education content. Patients in the intervention group autonomously use this system for 6 months in addition to receiving usual care, and complete biweekly skill translation assessments to facilitate the application of intervention skills in daily life.

BEHAVIORAL

Usual Care Group

Participants allocated to the control group receive usual emotional and symptom management for breast cancer, with no exposure to the digital intervention system. Standard care includes routine follow-up examinations, symptom management, psychological support, health education, and guidance on wound care, drainage tube management, diet, rest, and physical activity. All care is delivered in accordance with established clinical nursing procedures at the treating hospital. During follow-up, participants experiencing severe psychological distress (e.g. suicidal ideation) are referred to psychiatric services in line with standard clinical practice. After completion of the trial, control-group participants are offered the option to access the intervention system voluntarily.

Sponsors & Collaborators

• Nanfang Hospital, Southern Medical University

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

SUPPORTIVE_CARE

Masking

SINGLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-03-10

Primary Completion

2026-10-01

Completion

2026-11-01

Countries

• China

Study Locations