Mobile App Delivery of the EORTC QLQ-C30 Questionnaire to Assess Health-Related Quality of Life in Oncological Patients: Usability Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps are evolving in the medical field. However, ongoing discussions have questioned whether such apps are really valuable and whether patients will accept their use in day-to-day clinical life. Therefore, we initiated a usability study in our department. OBJECTIVE: We present our results of the first app prototype and patient testing of health-related quality of life (HRQoL) assessment in oncological patients. METHODS: We developed an app prototype for the iOS operating system within eight months in three phases: conception, initial development, and pilot testing. For the HRQoL assessment, we chose to implement only the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30; German version 3). Usability testing was conducted for three months. Participation was voluntary and pseudonymized. After completion of the QLQ-C30 questionnaire using iPads provided by our department, we performed a short survey with 10 questions. This survey inquired about patients' opinions regarding general aspects, including technical advances in medicine, mobile and app assistance during cancer treatment, and the app-specific functions (eg, interface and navigation). RESULTS: After logging into the app, the user can choose between starting a questionnaire, reviewing answers (administrators only), and logging out. The questionnaire is displayed with the same information, questions, and answers as on the original QLQ-C30 sheet. No alterations in wording were made. Usability was tested with 81 patients; median age was 55 years. The median time for completing the HRQoL questionnaire on the iPad was 4.0 minutes. Of all participants, 84% (68/81) owned a mobile device. Similarly, 84% (68/81) of participants would prefer a mobile version of the HRQoL questionnaire instead of a paper-based version. Using the app in daily life during and after cancer treatment would be supported by 83% (67/81) of participants. In the prototype version of the app, data were stored on the device; in the future, 79% (64/81) of the patients would agree to transfer data via the Internet. CONCLUSIONS: Our usability test showed good results regarding attractiveness, operability, and understandability. Moreover, our results demonstrate a high overall acceptance of mobile apps and telemedicine in oncology. The HRQoL assessment via the app was accepted thoroughly by patients, and individuals are keen to use it in clinical routines, while data privacy and security must be ensured.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".