Exploring Feature Preferences for a Treatment-Accompanying App in Patients Undergoing Radiation Therapy: Cross-Sectional Study
Bibliographic 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".