Metastatic Breast Cancer mHealth App to Promote Patient-Provider Communication: Protocol for a Usability and Satisfaction Study
Bibliographic record
Abstract
BACKGROUND: With the availability of more advanced and effective treatments, life expectancy has improved among patients with metastatic breast cancer (MBC), but this makes communication with their medical oncologist more complex. Some patients struggle to learn about their therapeutic options and to understand and articulate their preferences. Mobile health (mHealth) apps can enhance patient-provider communication, playing a crucial role in the diagnosis, treatment, quality of life, and outcomes for patients living with MBC. Our team developed an app called My MBC Journey to focus on the collection of important information for patients with MBC between clinical encounters. OBJECTIVE: This study will evaluate the usability and satisfaction of My MBC Journey, a mobile app designed for MBC, to inform future modifications. METHODS: This mixed methods study will assess patient use and satisfaction with the My MBC Journey app to inform future app modifications and identify the barriers and facilitators to the app's use for enhancing patient-provider communication. We will recruit a prospective, cross-sectional convenience sample of 25 patients with MBC and a sample of 15 members of the care team (ie, caregivers, nurse navigators, and medical oncologists). Applying iterative, convergent mixed methods, we will conduct qualitative, semistructured interviews with the patients and care team members. We also will collect quantitative data on usability through app analytics and standardized questionnaires (ie, the Mobile Application Rating Scale, the Mobile Application Rating Scale user version, and the System Usability Scale). Qualitative interviews will be audio recorded and analyzed using NVivo software to identify mHealth implementation themes. RESULTS: The study's results will inform future app design modifications and gauge preliminary effect size in support of future evaluations of the app's efficacy in improving patient-provider communication. CONCLUSIONS: Our long-term goal is to improve patient-provider communication by developing mHealth apps that empower patients to collect and share clinically relevant, patient-reported information in a timely manner. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/66050.
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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 | 0.042 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".