Co-Designing the MOSAIC mHealth App With Breast Cancer Survivors: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the world's most prevalent cancer. Although the 5-year survival rate for breast cancer in the United States is 91%, the stress and uncertainty of survivorship can often lead to symptoms of depression and anxiety. With nearly half of breast cancer survivors living with stress and symptoms of depression and anxiety, there are a significant number of unmet supportive care needs. New and potentially scalable approaches to meeting these supportive care needs are warranted. OBJECTIVE: This study aimed to engage breast cancer survivors and acceptance and commitment therapy (ACT) content experts in user-centered design (UCD) to develop a mobile health app (MOSAIC [Mobile Acceptance and Commitment Therapy Stress Intervention]) using stress intervention strategies. METHODS: We held 5 UCD sessions with 5 breast cancer survivors, 3 ACT content experts, 2 user experience design experts, and 1 stress expert facilitator over the course of 10 weeks. The sessions were developed to lead the 10 co-designers through the 5-step UCD process (eg, problem identification, solution generation, convergence, prototyping, and debriefing and evaluation). Following the fifth session, a prototype was generated and evaluated by the 5 breast cancer survivors and 3 ACT experts using the System Usability Scale, Acceptability E-scale, and a brief set of semistructured interview questions. RESULTS: The 10 co-designers were present for each of the 5 co-design sessions. Co-designers identified 5 design characteristics: simple entry with use reminders (behavioral nudges), a manageable number of intervention choices, highly visual content, skill-building exercises, and social support. A total of 4 features were also identified as critical to the use of the tool: an ACT and breast cancer-specific onboarding process, clean navigation tools, clear organization of the interventions, and once-per-week behavioral nudges. These requirements created the foundation for the app prototype. The 5 breast cancer survivors and 3 ACT co-designers evaluated the app prototype for 1 week, using an Android smartphone. They rated the app as usable (mean 79.29, SD 19.83) on the System Usability Scale (a priori mean cutoff score=68) and acceptable (mean 24.28, SD 2.77) on the Acceptability E-scale (a priori mean cutoff score=24). CONCLUSIONS: Through the UCD process, we created an ACT app prototype with 5 breast cancer survivors, 3 ACT experts, and 2 UCD designers. The next step in our research is to continue the assessment and refining of the prototype with additional breast cancer survivors. Future work will pilot-test the app to examine the feasibility of a large-scale, randomized control trial. Studies will enroll increasingly diverse breast cancer survivors to broaden the generalizability of findings.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".