An interventional study of AI-Based Mobile Application for Personalized Chemotherapy Support and Usual Care in Breast Cancer, Breast Neoplasm and Chemotherapy-Related Toxicities, sponsored by Dena h. Al-Tameemi. Recruiting at 1 site in Iraq. Open to female participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-06-12.
Sponsored by Dena h. Al-Tameemi · Not applicable, Interventional, and Supportive care
The goal of this clinical trial is to learn if an Arabic-language mobile application that uses artificial intelligence (AI) can help women with breast cancer during chemotherapy. The app is designed to give personalized support by reminding participants about their medications, teaching them how to manage treatment side effects, and alerting their healthcare team about serious symptoms.
The main questions this study aims to answer are:
Researchers will compare two groups:
Group 1: Participants who use the AI-based mobile app plus usual oncology care. Group 2: Participants who receive usual care only.
Participants will:
Despite advances in oncology care, breast cancer patients in Iraq face significant challenges regarding medication adherence and symptom management during the inter-cycle chemotherapy periods. This randomized controlled trial aims to bridge this gap by evaluating the efficacy, safety, and feasibility of a specialized, Arabic-language Artificial Intelligence (AI) mobile application.
Current standard care in the local setting often relies on episodic clinic visits, leaving patients without real-time support for side effects experienced at home. This study hypothesizes that a continuous, AI-driven digital intervention can reduce symptom burden and improve adherence to chemotherapy and supportive care medications (e.g., antiemetics) compared to standard care alone. The application utilizes Natural Language Processing (NLP) to provide conversational support tailored specifically to the cultural and linguistic context of Iraqi patients.
The intervention integrates a "Human-in-the-loop" safety model to ensure clinical accuracy. The AI algorithms are trained on clinical practice guidelines adapted for the local formulary.
Symptom Triage Logic: The app utilizes an algorithm based on the CTCAE grading system. Low-grade symptoms trigger self-care advice (e.g., hydration, dietary changes), while high-grade symptoms trigger immediate alerts to the patient to seek care and a notification to the study investigators.
Adherence Algorithms: Unlike static alarms, the notification system adapts to the specific chemotherapy cycle (e.g., AC or Taxane-based regimens) to remind patients of specific supportive medications required on specific days.
Control Group Specification (Standard of Care) Participants randomized to the control arm will receive the institutional standard of care. This includes routine oncologist consultations, standard written or verbal discharge instructions regarding chemotherapy side effects, and pharmacy dispensing counseling. They will not have access to the interactive AI features but will undergo the same schedule of outcome assessments to ensure rigorous comparison.
This study represents the first empirical effort to integrate AI-driven digital health tools into the public oncology sector in Iraq. It aims to validate whether automated, algorithmic triage is a feasible addition to the healthcare infrastructure in low-resource settings.
14,825 studies on the registry are indexed under Breast Neoplasms; 3,319 are open to participants now.
This study's planned enrollment of 130 is above the median of 72 across 10,896 interventional studies indexed under Breast Neoplasms.
Browse Breast Neoplasms studies →This is the only study on the registry with Dena h. Al-Tameemi as lead sponsor.
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Exclusion Criteria:
Patients with cognitive impairment or severe psychiatric disorders that would preclude effective interaction with the mobile application or questionnaire completion.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Metastases
Cancer that has spread from where it began to other parts of the body. “Metastatic” cancer is cancer that has spread this way.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Participants in this group will receive the AI-based mobile application in addition to usual oncology care. The Arabic-language mobile app uses artificial intelligence (AI) to provide personalized chemotherapy support, including symptom monitoring, medication adherence reminders, and educational guidance. Participants will use the app daily for 12 weeks during their chemotherapy cycles. All AI-generated advice is reviewed by oncologists and pharmacists to ensure clinical safety. They will also receive standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Behavioral: AI-Based Mobile Application for Personalized Chemotherapy Support · Other: Usual Care
Participants in this group will receive standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols. They will not have access to the AI-based mobile application.
Other: Usual Care
The intervention is an Arabic-language mobile application powered by artificial intelligence (AI) designed to provide personalized chemotherapy support for women with breast cancer. The app assists participants by monitoring symptoms, sending medication adherence reminders, and offering educational content on managing side effects and improving treatment understanding. It uses a conversational interface based on natural language processing (NLP) to communicate with users. Participants are asked to use the app daily for 12 weeks while receiving chemotherapy. A human-in-the-loop system ensures oncologists and pharmacists review AI-generated advice for accuracy and safety.
Also known as: AI Breast Cancer Support App, AI-ChemoApp
Standard oncology care provided by the hospital team, including chemotherapy administration, routine follow-up, and patient education according to local protocols.
Also known as: ordinary care, standard care
Change in Symptom Burden and Chemotherapy-Related Toxicities (Arabic PRO-CTCAE)
Symptom burden and treatment-related toxicities will be assessed using the validated Arabic version of the Patient-Reported Outcomes-Common Terminology Criteria for Adverse Events (PRO-CTCAE). Participants will report frequency, severity, and interference of common chemotherapy-related symptoms such as fatigue, nausea, vomiting, pain, and neuropathy. Mean score changes between baseline and 12 weeks will be compared between the AI app group and the usual care group to evaluate whether the intervention lowers symptom burden and improves self-management.Data will be collected at Baseline and weekly thereafter.
Time frame: Up to 12 weeks
Medication Adherence Score (Arabic MMAS-8
Medication adherence will be measured using the 8-item Morisky Medication Adherence Scale (MMAS-8) in its validated Arabic version. Participants' self-reported responses generate a score from 0-8, with higher scores indicating better adherence. The mean change in adherence scores from baseline to 12 weeks will be compared between groups to determine the app's effectiveness in promoting medication adherence during chemotherapy.
Time frame: Baseline and at weeks 6,12.
Change in Patient Knowledge and Information Satisfaction (EORTC QLQ-INFO25)
The Arabic version of EORTC QLQ-INFO25 questionnaire will assess participants' perception of the adequacy, clarity, and usefulness of information provided about their disease, treatment, and care. The total score ranges from 0 to 100, with higher scores reflecting greater satisfaction with information. The AI-based mobile app is expected to increase knowledge and satisfaction through personalized, accessible education.
Time frame: Baseline and at weeks 6,12.
Change in General Quality of Life (EORTC QLQ-C30)
Quality of life (QoL) will be measured using the validated Arabic version of the EORTC QLQ-C30. The EORTC QLQ-C30 is a 30-item questionnaire used to assess the quality of life of cancer patients. It incorporates five functional scales (physical, role, cognitive, emotional, and social), three symptom scales (fatigue, pain, and nausea/vomiting), and a global health status/QoL scale. Raw scores are transformed to a linear scale ranging from 0 to 100. Functional Scales \& Global Health Status: Higher scores represent a higher/better level of functioning and quality of life. Symptom Scales: Higher scores represent a higher/worse level of symptomatology.
Time frame: Baseline and at weeks 6,12.
Change in Breast Cancer-Specific Quality of Life (EORTC QLQ-BR23)
The Arabic version of EORTC QLQ-BR23 will be used to assess the change in Breast Cancer-Specific Quality of Life.The EORTC QLQ-BR23 is a supplementary module specifically for breast cancer patients. It consists of 23 items covering functional scales (body image, sexual functioning, future perspective) and symptom scales (side effects of systemic therapy, breast symptoms, arm symptoms, hair loss). Raw scores are transformed to a linear scale ranging from 0 to 100. Functional Scales: Higher scores represent better functioning. Symptom Scales: Higher scores represent worse symptoms.
Time frame: Baseline and at weeks 6,12.
Accuracy of AI-Generated Advice Compared with Oncologist Assessment
The accuracy and clinical appropriateness of the AI-generated recommendations will be evaluated by comparing the app's advice logs to oncologist judgments on the same patient-reported scenarios. Agreement will be assessed using Cohen's kappa coefficient to determine the reliability of the AI-based symptom triage.
Time frame: Through study completion, an average of 12 weeks.
User Satisfaction and App Usability Score
Participants will evaluate the application using a structured questionnaire assessing multiple domains including ease of use, perceived usefulness, information clarity, and trust in AI recommendations. Responses are measured on a 5-point Likert scale: 1 = Strongly Disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly Agree The final outcome is reported as an aggregate average score across all items. Scale Range: 1 to 5. Interpretation: Higher scores indicate higher user satisfaction
Time frame: At 12 weeks (end of intervention)
App Usage Frequency
Engagement is assessed by monitoring the backend usage logs to determine the average number of distinct application sessions (log-ins) per participant. Unit of Measure: Sessions per week
Time frame: Continuously during the 12-week intervention.
Identification of Technical, Ethical, and Implementation Barriers
Qualitative data will be collected through semi-structured interviews to identify challenges related to app usability, privacy concerns, data reliability, and clinical integration. Thematic analysis will be conducted to inform strategies for safe and effective implementation of AI-based supportive care tools in Iraqi oncology settings.
Time frame: After 12 weeks of intervention use and upon study completion.
Plan to share: Yes — Deidentified individual participant data (IPD) that underlie the results reported in published articles will be made available to qualified academic researchers upon reasonable request. Data sharing will occur after publication of the main results and subject to approval by the Research Ethics Committee, College of Pharmacy, University of Baghdad. Data will include coded variables without any identifiers to ensure participant confidentiality.
Supporting information: Study protocol, Statistical analysis plan, Informed consent form
Time frame: Deidentified individual participant data and related documents will be available beginning 6 months after publication of the main study results and for up to 3 years thereafter. Requests beyond this period will be considered on a case-by-case basis depending on data availability and ethical approval.
Access criteria: Access to the deidentified data will be granted to qualified investigators for non-commercial academic research. Requests must include a clear research proposal and data-use agreement ensuring data protection. Data will be shared electronically through secure university-approved transfer methods after approval by the principal investigator and the Research Ethics Committee, College of Pharmacy, University of Baghdad.
Started Jun 2026 (actual). Primary completion is estimated for Aug 2026, 2 months after the start.
Of 2,661 non-industry interventional studies with no phase under Breast Neoplasms, half reached primary completion within 33 months of starting (the median), counting those still running; a quarter within 16 and three quarters within 66. This study's planned 2 months is below that.
Counting every sponsor, half of 2,771 studies reached it within 33 months; a quarter within 15 and three quarters within 66.
How these figures are worked out, and which studies are left out of them.
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