Collaborative Design and Development of a Patient-Centered Digital Health App for Supportive Cancer Care: Participatory Study
Bibliographic record
Abstract
BACKGROUND: Digital health tools such as smartphone apps have the potential to improve supportive cancer care. Although numerous smartphone apps for supportive care are available, few are designed using a user-centered approach. Such an approach is crucial for successful implementation, as it may improve user engagement, usability, and adoption in clinical settings. OBJECTIVE: This study aimed to co-design and develop a digital health app for supportive cancer care in collaboration with patients with cancer and health care professionals and to explore factors influencing its future acceptance. METHODS: We conducted a participatory study with the major stakeholders at the University Hospital Zurich. Workshops, individual qualitative interviews, and focus groups were held with health care professionals, survivors of cancer, and patients with cancer. The co-design process was divided into 3 phases: predesign, generative phase, and prototyping. User-centered design methods included scoring cards and think-aloud protocols to co-create design ideas, identify important functionalities, and test usability. Qualitative data were analyzed using thematic analysis. RESULTS: Patients and health care professionals emphasized the need for a digital health app to improve patient-healthcare professional communication, digitalize supportive care screening and processes, and enhance self-efficacy. The resulting app, OncoSupport+, was co-designed and integrated into the clinical workflow for supportive cancer care. It consists of (1) a patient dashboard to record patient-reported outcome measures and to provide access to personalized supportive care information and contact details, and (2) a nurse dashboard to visualize patient data, which can be used during nursing consultations. Potential facilitators for adoption included ease of use, workflow integration, introduction by health care professionals, and technical support, whereas internet anxiety may be a potential barrier. CONCLUSIONS: Collaborative development with patients and health care professionals is crucial for creating digital health tools that can be implemented successfully. Future research should evaluate the feasibility of long-term implementation and the real-world usability and effectiveness of OncoSupport+ for improving communication, self-efficacy, and quality of life.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".