A Community-Engaged Approach to Developing a Mobile Cancer Prevention App: The mCPA Study Protocol
Bibliographic record
Abstract
BACKGROUND: Rapid growth of mobile technologies has resulted in a proliferation of lifestyle-oriented mobile phone apps. However, most do not have a theoretical framework and few have been developed using a community-based participatory research approach. A community academic team will develop a theory-based, culturally tailored, mobile-enabled, Web-based app-the Mobile Cancer Prevention App (mCPA)-to promote adherence to dietary and physical activity guidelines. OBJECTIVE: The aim of this study is to develop mCPA content with input from breast cancer survivors. METHODS: Members of SISTAAH (Survivors Involving Supporters to Take Action in Advancing Health) Talk (N=12), treated for Stages I-IIIc breast cancer for less than 1 year, 75 years of age or younger, and English-speaking and writing, will be recruited to participate in the study. To develop the app content, breast cancer survivors will engage with researchers in videotaped and audiotaped sessions, including (1) didactic instructions with goals for, benefits of, and strategies to enhance dietary intake and physical activity, (2) guided discussions for setting individualized goals, monitoring progress, and providing or receiving feedback, (3) experiential nutrition education through cooking demonstrations, and (4) interactive physical activity focused on walking, yoga, and strength training. Qualitative (focus group discussions and key informant interviews) and quantitative (sensory evaluation) methods will be used to evaluate the participatory process and outcomes. RESULTS: Investigators and participants anticipate development of an acceptable (frequency and duration of usage) feasible (structure, ease of use, features), and accessible mobile app available for intervention testing in early 2017. CONCLUSIONS: Depending on the availability of research funding, mCPA testing, which will be initiated in Miami, will be extended to Chicago, Houston, Philadelphia, and Los Angeles.
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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 | 0.047 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.074 | 0.020 |
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".