Description

Breast cancer is the most commonly diagnosed cancer worldwide and remains the leading cause of cancer-related mortality among women. Despite advances in diagnosis and treatment, patients undergoing chemotherapy frequently experience a wide range of physical and psychological symptoms, including fatigue, nausea, vomiting, pain, sleep disturbances, anxiety, depression, and impaired body image. These symptoms may negatively affect treatment adherence, self-management abilities, and health-related quality of life. Inadequately managed symptoms can also lead to treatment delays, dose reductions, emergency department visits, and hospitalizations. Effective symptom management is therefore a critical component of breast cancer care. Self-efficacy, defined as an individual's belief in their ability to successfully perform behaviors required to achieve desired outcomes, plays an important role in symptom management and adaptation to cancer treatment. Higher levels of self-efficacy have been associated with better symptom control, improved psychological well-being, enhanced patient-provider communication, and greater treatment adherence. In addition, maintaining and improving quality of life has become a major goal of contemporary cancer care. Mobile health technologies have emerged as promising tools for supporting symptom monitoring and self-management among cancer patients. Previous studies have demonstrated that electronic patient-reported outcome systems can facilitate timely symptom reporting, reduce symptom burden, improve quality of life, and potentially enhance survival outcomes. However, many existing mobile applications for breast cancer patients focus on limited aspects of care and do not comprehensively address the broad range of symptoms experienced during chemotherapy. Artificial intelligence has the potential to enhance mobile health interventions by providing personalized, real-time recommendations based on patient-reported data. Artificial intelligence-supported systems can analyze symptom reports, identify symptom severity, and deliver tailored guidance to support symptom self-management. Such personalized interventions may increase patient engagement, improve symptom control, strengthen self-efficacy, and ultimately enhance quality of life. The purpose of this randomized controlled trial is to evaluate the effects of an artificial intelligence-supported mobile application on symptom management, self-efficacy, and quality of life among breast cancer patients receiving chemotherapy for the first time. The study will be conducted between June 2026 and December 2026 at the Berna Türek Chemotherapy Center of Ege University Hospital and the Chemotherapy Center of İzmir City Hospital. A total of 70 breast cancer patients who meet the eligibility criteria will be enrolled and randomly assigned to either an intervention group (n=35) or a control group (n=35). Participants in the intervention group will download and use an artificial intelligence-supported mobile application on their Android smartphones on the first day of chemotherapy. Following chemotherapy initiation, participants will report their symptoms daily through the application. Based on symptom severity and patient-reported information, the artificial intelligence component of the application will provide individualized symptom-management recommendations developed according to evidence-based practices and supervised by the research team. Participants will also have access to educational content regarding breast cancer and its treatment through the application. Participants in the control group will receive standard care routinely provided by the treatment centers. Outcome assessments will be conducted at baseline (first chemotherapy day), at the end of the first chemotherapy cycle (cycle length varies by chemotherapy protocol; all cycles are defined according to each patient's prescribed treatment regimen), and at the end of the fourth chemotherapy cycle. Data will be collected using the patient identification form, the memorial symptom assessment scale (MSAS), the chemotherapy symptom management self-efficacy scale for breast cancer patients, the european organisation for research and treatment of cancer quality of life questionnaire core 30 (EORTC QLQ-C30 version 3.0), and the breast cancer-specific module (EORTC QLQ-BR23). All data will be collected by the same researcher. The primary hypothesis is that patients using the artificial intelligence-supported mobile application will demonstrate better symptom management, higher self-efficacy levels, and improved quality of life compared with patients receiving standard care alone. Findings from this study may contribute to the growing evidence regarding the integration of artificial intelligence and mobile health technologies into supportive cancer care and provide guidance for future digital health interventions targeting symptom management in breast cancer patients.