Related Experiment Video
Updated: May 7, 2026
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
Published on: April 11, 2018
Exploring Feature Preferences for a Treatment-Accompanying App in Patients Undergoing Radiation Therapy:
Rieka von der Warth1, Nils Henrik Nicolay2,3, Harald Binder2
1Section of Health Care Research and Rehabilitation Research, Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 49, Freiburg, 79106, Germany, 49 761 270 74470.
Background:
Mobile health (mHealth) apps are playing an increasingly important role in health care, including in radiotherapy. However, adherence remains low. One way to increase adherence is to tailor app features to the patients' preferences.
Objective:
This study aimed to explore the importance of patient preferences regarding the features of a therapy-supporting app in radiotherapy. In addition, we examined factors associated with the perceived importance of these features.
Methods:
A cross-sectional questionnaire study was conducted with patients undergoing radiotherapy between summer 2021 and winter 2022. The subjective importance of 18 features of a treatment-accompanying app was explored using a 5-point Likert scale from 1=not so important to 5=extremely important. Descriptive analyses were used to show the rated importance of app functions. Associations with possible predictors were examined using multiple hierarchical regressions, with age (interval-scaled), gender (dichotomous), previous experience with mHealth apps (dichotomous), education (3-level nominal), and supportive care needs (interval-scaled) as predictors.
Results:
A total of 84 radiotherapy patients participated. The average age was 62 (SD 12.5) years. The feature with the highest importance was security against hacking (46/77, 60% extremely important). Explained variances in the regression analyses ranged between R2=0.25 (The app should give me tips on suitable sporting activities that are possible with my illness) and R2=-.07 (The app should provide me with information about suitable self-help offers). Previous mHealth usage predicted the importance of 6 features, such as managing appointments (β=.275; P<.05). Decreasing age was related to 6 features, for example, showing test results and laboratory values (β=-.358; P<.05). Other predictors were an increasing age and greater supportive care needs.
Conclusions:
Patients undergoing radiotherapy rated app features as having varying levels of importance. The findings may help to tailor mHealth apps in radiotherapy, potentially improving adherence to app usage.
