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.

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.

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.

In this study, the EORTC QLQ-C30 Summary Score is used as the primary analysis endpoint, and assessments are conducted using the Chinese version of the EORTC QLQ-C30 questionnaire.

The EORTC QLQ-C30 Summary Score is calculated using the "13-domain mean method." Raw scores are first obtained from five functional scales and eight symptom scales, with all scores transformed to a 0-100 scale. All symptom scale scores are then reverse-scored, as higher original symptom scores indicate greater symptom burden; after transformation, higher scores uniformly indicate better outcomes, corresponding to milder symptoms. The arithmetic mean of the transformed scores across the 13 scales is subsequently calculated to generate the Summary Score, which remains on a 0-100 scale, with higher scores indicating better overall health-related quality of life.

The EORTC QLQ-C30 questionnaire comprises 15 dimensions, including five functional domains (physical, role, emotional, cognitive, and social functioning), three symptom domains (fatigue, pain, and nausea/vomiting), one Global Health Status/Quality of Life domain, and six single-item symptom scales (e.g., dyspnea, insomnia). Scoring is performed by first calculating the mean of the raw scores for each domain or item, which is then linearly transformed into a standardized 0-100 score using the formula (mean raw score-1)/range of response options×100. For the functional domains and the Global Health Status/Quality of Life domain, higher scores indicate better functioning or quality of life (positive scoring), whereas for the symptom domains, higher scores indicate greater symptom severity and poorer health status (negative scoring).