Promoting Collaborative Goal Setting for Cancer Prevention Among Primary Care Patients Through mHealth: Mixed Methods Evaluation of a New App
Bibliographic record
Abstract
BACKGROUND: Many newly diagnosed cancers are associated with modifiable lifestyle behaviors, such as diet, exercise, smoking cessation, and maintaining a healthy weight. However, primary care providers rarely discuss cancer prevention behaviors with their patients. OBJECTIVE: This study aims to assess the usability, acceptability, and user engagement of the Healthier Together mobile app, which is designed to promote cancer prevention behaviors among non-Hispanic Black primary care patients, by using social networks and goal-setting theories of behavior change. METHODS: In an 8-week pilot study, we enrolled primary care patients (N=41) and provided them with a cancer prevention mobile app that allowed them to select, track, and share progress on cancer prevention goals with other users. App usability was assessed using the System Usability Scale. We assessed the app's acceptability by qualitatively analyzing open-ended responses regarding participants' overall experience with the app. We assessed participants' engagement by analyzing the built-in data capture device, which included the number of times participants checked in (out of a maximum of 8) during the study. RESULTS: The mean age of the 41 participants was 51 years (SD 12), and 76% (31/41) were women. App use data were captured from all participants, and 83% (34/41) completed the exit survey and interview. The mean System Usability Scale score was 87 (SD 12; median 90; IQR 78-95). The analysis of open-ended responses revealed several key themes, and participants complemented the app's ease of use and health behavior-promoting features while also commenting on the need for more feedback and social interactions through the app. On average, participants checked in 5.7 times (SD 2.7) out of 8 possible opportunities. Of the 41 participants, 76% (31/41) checked in during at least 4 of the 8 weeks. Secondary analyses revealed that participants often accomplished their set goals (mean 5.1, SD 2.7) for each week. The qualitative analysis of comments given by participants within the app after each weekly check-in revealed several themes on how the app assisted participants in behavioral change, highlighting that some participants created exercise programs, ate healthier foods, lost a significant amount of weight, and stopped smoking during this study. CONCLUSIONS: The implementation of a mobile cancer prevention goal-setting app in a primary care setting was feasible, and the app achieved high usability, acceptability, and engagement among participants. User feedback revealed an influence on health behaviors. These findings suggest the promise of the Healthier Together app in facilitating behavioral change to reduce cancer risk among non-Hispanic Black primary care patients.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".