Exercise and Recovery

The goal of this observational study is to develop and evaluate a large language model (LLM)-based decision support system for exercise prescription in breast cancer patients, aiming to provide personalized decision-making support for postoperative breast cancer rehabilitation. The main questions it aims to answer are: How accurate, personalized, and safe are the exercise prescriptions generated by the fine-tuned LLM? How does the model's performance compare with other mainstream or non-fine-tuned models across different stages and subtypes of breast cancer? Participants are postoperative breast cancer rehabilitation patients treated at Sun Yat-sen Memorial Hospital of Sun Yat-sen University. They will have demographic, tumor, treatment, and physical fitness data collected; receive personalized exercise prescriptions automatically generated by the LLM-based system; and provide subjective evaluations on the feasibility and executability of the prescriptions.

The investigators are conducting research on factors related to the self-regulation of mood and arousal states across a range of everyday activities as well as different levels of stress. Behavioral interventions-such as meditation, listening to music, or visualizing art or nature-offer important alternatives and/or adjunctive strategies to pharmaceutical tools or other mechanisms supporting physical and emotional well-being. This research will expand on the knowledge base regarding the impact of biophysical stimulation and/or frame of mind on an individual's self-directed management of physical and emotional health. Motivation, confidence, and composure are critical aspects of self-efficacy, framing a person's mindset to make healthy choices across daily activities. The research in this protocol is carried out across multiple sites and studies by means of digital tools and a consistent participant experience workflow. Together, these resources are intended to support individuals across multiple self-directed experiences utilized in the maintenance of their basic health. Before, during, and after session experiences, the participant provides objective (physiological) responses and/or subjective (self-report) responses and reflections; some studies also include interviews or focus groups. The combined set of responses can be summarized, connected to other measures, and input to machine learning models to improve personalization of stimuli. By studying the effects of soundBrilliance experiences across a variety of contexts and participant samples, the investigators can better refine each element of the stimulus to improve satisfaction, tolerance, and targeted outcomes.