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Unsupervised Dynamic Electronic Patient-Reported Outcome Reporting of Immunotherapy-Related Symptoms in Cancer
Anna Kristina Stauffacher1,2, Louise von Stockar3, Hans-Friedrich Witschel4
1Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Oncology
|July 10, 2025
Summary
Unsupervised cancer patients using a mobile app demonstrated high adherence to electronic patient-reported outcomes (ePRO), effectively tracking symptoms and wellbeing during immunotherapy. This dynamic ePRO approach supports patient care in various cancer treatment settings.
Area of Science:
- Oncology
- Digital Health
- Patient-Reported Outcomes
Background:
- Electronic patient-reported outcomes (ePRO) enhance patient care and symptom management in cancer treatment.
- This study investigates ePRO for wellbeing, symptoms, and cognition in unsupervised patients with solid tumors (bladder, breast, lung) undergoing various therapies.
Purpose of the Study:
- To assess the feasibility and adherence of unsupervised ePRO data collection in cancer patients.
- To explore the relationship between reported symptoms, wellbeing, and cognitive function during cancer therapy.
Main Methods:
- A mobile app was used by 11 patients undergoing immune checkpoint inhibitor treatment for structured reporting of symptoms (CTCAE criteria), wellbeing, cognition, and vital signs.
- Descriptive analysis was performed on the collected data.
Main Results:
- Patients submitted 9,624 symptom ratings and 2,983 wellbeing ratings, with high adherence (median 453 symptom entries/patient).
- Commonly reported symptoms included fatigue, diarrhea, limb/muscle pain, sleep issues, and dyspnea, with clinically relevant associations to immunotherapy.
- Higher symptom severity correlated with lower wellbeing, but no clear link to cognitive performance was observed.
Conclusions:
- Unsupervised cancer patients exhibit high adherence and frequency of data entry using ePRO mobile applications.
- ePRO systems are valuable tools for monitoring patients undergoing immunotherapy, comparable to supervised methods.
