This randomized controlled clinical trial aims to evaluate the effects of an artificial intelligence-supported mobile application on symptom management, self-efficacy, and quality of life in breast cancer patients receiving chemotherapy. The study will be conducted between June 2026 and December 2026 at the Ege University Hospital Berna Türek Chemotherapy Center and the İzmir City Hospital Chemotherapy Center. Seventy breast cancer patients who will receive chemotherapy for the first time will be randomly assigned to either an intervention group using the mobile application or a control group receiving standard care. The study seeks to answer the following key questions: Does the artificial intelligence-supported mobile application improve symptom management? Does use of the application increase patients' self-efficacy? Does the application improve quality of life compared with standard care? Participants in the intervention group will use the mobile application during chemotherapy, report daily symptoms, and receive personalized symptom-management recommendations generated by an artificial intelligence system trained by the research team. Participants in both groups will complete symptom, self-efficacy, and quality-of-life assessments at baseline, after the first chemotherapy cycle (cycle length varies by chemotherapy protocol; all cycles are defined according to each patient's prescribed treatment regimen), and after the fourth cycle. The control group will receive standard care and complete the same assessments through face-to-face visits. This study is designed to assess the effectiveness of artificial intelligence-supported mobile health interventions in breast cancer care and their potential to improve symptom management, self-efficacy, and quality of life.
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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
NONE
Enrollment
70
Participants will use an AI-supported mobile application starting on the first day of chemotherapy. The application allows daily symptom reporting, provides AI-based personalized symptom management recommendations, and includes educational information about breast cancer and its treatment.
Ege University Hospital, Berna Türek Chemotherapy Center
Izmir, İzmir, Turkey (Türkiye)
RECRUITINGSymptom burden
Symptom burden will be assessed using the Memorial Symptom Assessment Scale (MSAS). The scale comprises a total of 32 symptoms, which are evaluated in terms of frequency, severity, and distress. On the Memorial Symptom Assessment Scale, each symptom is examined for presence or absence. If a symptom is present, it is rated as follows: for frequency: 1 (rarely), 2 (occasionally), 3 (frequently), 4 (almost constantly); for severity: 1 (mild), 2 (moderate), 3 (severe), 4 (very severe); for distress: 0 (not at all), 1 (a little bit), 2 (somewhat), 3 (quite a bit), 4 (very much). Higher scores on the Memorial Symptom Assessment Scale indicate greater frequency, severity, and distress of the experienced symptoms.
Time frame: Baseline, after the end of Cycle 1 (each cycle is 21 days), and after the end of Cycle 4 (approximately up to 12 weeks)
Chemotherapy symptom management self-efficacy
Self-efficacy will be measured using the Chemotherapy Symptom Management Self-Efficacy Scale for Breast Cancer Patients. The scale consists of 27 items and three subdimensions. Each item is rated on a 0-10 Likert scale (0 = not confident at all, 10 = very confident). The total score ranges from 0 to 270. Higher scores indicate better self-efficacy (greater confidence in managing chemotherapy-related symptoms).
Time frame: Baseline, after the end of Cycle 1 (each cycle is 21 days), and after the end of Cycle 4 (approximately up to 12 weeks)
Quality of Life (EORTC QLQ-C30)
Quality of life will be evaluated using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30 Version 3.0). The scale contains 30 items assessing functional scales (physical, role, cognitive, emotional, social), symptom scales (fatigue, pain, nausea/vomiting, dyspnea, insomnia, appetite loss, constipation, diarrhea, financial difficulties), and global health status. All scores are transformed to a 0-100 range. Higher scores on global health status and functional scales indicate better quality of life, while higher scores on symptom scales indicate worse quality of life.
Time frame: Baseline, after the end of Cycle 1 (each cycle is 21 days), and after the end of Cycle 4 (approximately up to 12 weeks)
Quality of Life (EORTC QLQ-BR23)
Quality of life will be evaluated using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Breast Cancer Module 23 (EORTC QLQ-BR23). The scale contains 23 items assessing breast cancer-specific functional domains (body image, sexual functioning, sexual enjoyment, future perspective) and symptom domains (systemic therapy side effects, breast symptoms, arm symptoms, hair loss). All scores are transformed to a 0-100 range. Higher scores on functional scales indicate better quality of life, while higher scores on symptom scales indicate worse quality of life.
Time frame: Baseline, after the end of Cycle 1 (each cycle is 21 days), and after the end of Cycle 4 (approximately up to 12 weeks)
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.