USER-CENTERED DESIGN AND USABILITY EVALUATION OF A CANCER PREVENTION WEB APPLICATION: AN ITERATIVE APPROACH IN GERMANY
Bibliographic record
Abstract
Abstract Objectives Limited public awareness of cancer risk factors necessitates effective dissemination of cancer prevention information. Digital technologies offer an opportunity to address this gap, yet there is scant information on tools for communicating cancer prevention evidence. This article describes the user-centered design, development and usability evaluation of a web application for personalized cancer prevention tailored to the German population. Materials and Methods Prototypes of the web-app were developed through early and continuous formative evaluations. These prototypes integrated validated cancer risk prediction models and recommendations using an evidence-based risk communication approach. In a graphical user interface (GUI) test usability was assessed using the system usability scale (SUS), deriving scores for overall usability, usefulness, and learnability. Qualitative data on user experience (UX) and user interface (UI) issues were also collected through think-aloud protocols, interviews, and questionnaires. Findings The GUI test showed a SUS score of 69.7/100 and a usefulness score of 75.8, indicating acceptable usability, while the learnability score was 48.4. Eight categories of UX/UI problems were identified, including one severe and three moderate issues related to data input, user guidance and risk visualization. Qualitative feedback highlighted strengths in navigation, information presentation, and interactive features such as the risk simulation tool. Discussion The iterative development and early user testing yielded valuable feedback, identifying key usability concerns during prototyping. The usability score was within an acceptable range, and the usefulness score was above average. However, the lower learnability score indicated potential challenges in user understanding and satisfaction. Identified usability issues highlight areas for improvement while positive feedback supports the design choices, particularly the use of visual aids, numerical data, and personalized feedback to improve risk comprehension and motivate behavior change. Conclusion The NCPC cancer prevention web application represents a significant step towards effective digital health promotion in Germany. Addressing identified usability concerns through continued iterative refinement and user involvement is crucial for enhancing the tool’s effectiveness. The integration of evidence-based risk communication strategies shows promise in improving risk comprehension and motivating behavior change among users.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".