Designing a Cancer Prevention Collaborative Goal-Setting Mobile App for Non-Hispanic Black Primary Care Patients: An Iterative, Qualitative Patient-Led Process
Bibliographic record
Abstract
BACKGROUND: There remains a need to engage at-risk primary care populations in cancer prevention behaviors, yet primary care physicians often lack the time or resources to discuss these behaviors with their patients. OBJECTIVE: The objective of this study is to evaluate the content, usability, and acceptability of a mobile app that leverages insights from goal-setting and social network literature to facilitate cancer prevention goal setting, tracking, and sharing between non-Hispanic Black primary care patients and their social ties. METHODS: We recruited eligible non-Hispanic Black primary care patients (aged ≥18 years) from 2 practice sites in West Philadelphia, using nonprobabilistic purposive sampling. We conducted semistructured interviews with 5 to 7 participants over 3 weeks to solicit feedback on paper mock-ups of the app, iteratively adapting these mock-ups after each set of interviews. Thereafter, and informed by initial feedback, we created an electronic beta version of the app and sought acceptability and usability feedback from a different set of participants. Then, we conducted content analysis of all user responses to search for unifying themes on acceptability and usability of both the initial mock-ups and beta version of the app. We further assessed app usability using questions derived from the System Usability Scale. RESULTS: A total of 33 non-Hispanic Black primary care patients participated in this study. The mean age was 49 (SD 13) years, and 26 (79%) out of 33 participants identified as female. Semistructured interviews revealed three primary generalizable insights from our target population: the framing of each goal and its relevance to cancer impacted the likelihood that the goal would be chosen, participants thought that sharing health goals with others facilitates health behaviors, and most participants found it motivating to see other users' goal progress, while still collaborating with these users on their health goals. An overarching insight that permeated across each theme was the participants' desire to customize and personalize the app. Usability testing revealed that 100% (33/33) of participants found the app easy to use, and 76% (25/33) of participants reported that they would like to use this app frequently. CONCLUSIONS: Cancer prevention in the modern era must include options that are accessible to all, but this does not mean that all options must be universal. This study's iterative process led to the development of a cancer prevention mobile app that non-Hispanic Black primary care patients deemed usable and acceptable and yielded noteworthy insights about what intended end users value in setting and accomplishing health goals.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".