ArticleJMIR cancer2025
Exploring Feature Preferences for a Treatment-Accompanying App in Patients Undergoing Radiation Therapy: Cross-Sectional Study.
Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Protocol of a Randomized Trial Investigating the Value of a Reminder App to Increase Physical Activity During Radiotherapy or Radio-Chemotherapy for Locally Advanced Lung Cancer.Journal of clinical medicine · 2026Article
- Assessment of Physical Activity During Radiation Therapy for Lung Cancer: Study Protocol of the APART-LUNG Study.Clinics and practice · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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