Web-Based Patient Self-Reported Outcome After Radiotherapy in Adolescents and Young Adults With Cancer: Survey on Acceptance of Digital Tools
Bibliographic record
Abstract
BACKGROUND: eHealth and mobile health (mHealth) are an evolving trend in the medical field. The acceptance of digital tools is high, and the need is growing. OBJECTIVE: Young adults (18-40 years) confronted with a cancer diagnosis present unique needs and require special care. They often have a strong affinity and are familiar with modern technology. On that account, we implemented a web-based symptom and quality of life (QoL) assessment to address patients' attitudes and willingness to use mHealth tools. The study also aims to evaluate sociodemographic parameters that could influence patients' opinions. METHODS: A total of 380 young patients aged 18-40 treated with radiotherapy between 2002 and 2017 were included in the trial. We assessed QoL via the European Organization for Research and Treatment of Cancer-Core 30 (EORTC C30) questionnaire and added general questions about mHealth technology. The added questions inquired patients' opinions regarding general aspects, including technical advances in medicine, mobile and app assistance during cancer treatment, data transfer, and app-specific features. The survey was conducted for 12 months. Participation was voluntary and pseudonymized; prior written consent was obtained. RESULTS: We achieved a participation rate of 57.6% (219/380) and a completion rate of 50.2% (110/219). The median age was 33 years (range 18-40). Of all participants, 89.1% (98/110) considered new technologies in medicine as positive; 10.9% (12/110) answered with neutral. Nearly all patients (96.4%, 106/110) stated that they would send further data via a web-based platform. Of all, 96.4% (106/110) considered the provided pseudonymization of their data as safe. We further asked the patients if they would use a mobile app for symptom and QoL assessment similar to the present web-based system: 74.5% (82/110) answered with yes and 25.5% (28/110) said they would not use a mobile app in the future. We tested the willingness to use an app on several sociodemographic parameters, such as age, gender, education, health insurance status, and cancer-related parameters: tumor stage, time since radiation treatment, and treatment intention. None of these parameters correlated with app use in this group of young adults. Patients who were generally positive regarding using an app rated several possible functions of a future app. The 3 most requested features were appointment reminders (89.0%, 73/82), contact overview of all involved clinics and physicians (87%, 71/82), and making an appointment via app (78%, 64/82). CONCLUSIONS: eHealth and mHealth tools should be available as an integrated part of a comprehensive cancer care approach. It provides automated, thorough documentation of health parameters during therapy and follow-up for doctors, medical staff, and tumor patients to optimize treatment. With this study, we could show that young adults are the ideal patient population to use eHealth/mHealth tools. Such tools offer further digital support and improve the patients' need for constant QoL during cancer care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".