Education and Symptom Reporting in an mHealth App for Patients With Cancer: Mixed Methods Development and Validation Study
Bibliographic record
Abstract
Background: The widespread prevalence of cancer across the globe demands cutting-edge solutions for its treatment. Current cancer therapies, notably chemotherapy, pose challenges due to their side effects. The early detection and management of the side effects are vital but complex. This study introduces a mobile health app designed to bridge the communication gaps between patients with cancer and health care providers. Hence, it allows patients to report symptoms immediately and also enables proactive symptom management by health care providers. Objective: This study has 2 objectives: first, to design a cancer-focused mobile health app that integrates educational content and real-time symptom reporting for chemotherapy patients. Second, to validate and evaluate the app quality using the Mobile App Rating Scale (MARS). The app seeks to foster health care communication, reduce hospital readmissions, and optimize symptom management, contributing to a more impactful patient experience. Methods: This mixed-methods study details the development and validation of mobile health applications. The app was designed by a multidisciplinary team, including nurses, medical professionals, pharmaceutical chemists, computer engineers, and software developers, using agile methodologies. For validation, the app was assessed by 13 evaluators, including clinical professionals (nurses and physicians) and engineers. The evaluation included technical performance analysis using Google tools and quality assessment using the MARS, which measures engagement, functionality, aesthetics, and information quality. Results: Performance metrics highlighted areas for improvement, with loading times showing delays in displaying content. Meanwhile, the response time of the app was moderate, and visual stability remained excellent. The app achieved an overall MARS score of 3.75 (SD 0.42), indicating consistent quality, with functionality scoring the highest (4.35; SD 0.52) and engagement the lowest (3.31; SD 0.61). The reliability of the MARS was confirmed (interclass correlation coefficient: 0.84; 95% CI: 0.72-0.92). Evaluators unanimously praised the app's potential benefits for patients and clinical professionals while identifying areas for improvement such as customization, onboarding guidance, and navigation. Conclusions: The CONTIGO app showed strengths in functionality, usability, and information quality, supported by robust security measures. However, areas such as user interactivity and engagement require improvement. Future refinements will integrate insights from patients with cancer to address user-specific needs and enhance the oncology care experience.
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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 | 0.053 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".