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Record W2290339467 · doi:10.2196/resprot.5290

A Community-Engaged Approach to Developing a Mobile Cancer Prevention App: The mCPA Study Protocol

2016· article· en· W2290339467 on OpenAlexvenueno aff
Selina A. Smith, Mary Whitehead, Joyce Q. Sheats, Jeff Mastromonico, Wonsuk Yoo, Steven S. Coughlin

Bibliographic record

VenueJMIR Research Protocols · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsProtocol (science)MCPAComputer scienceMobile appsPsychologyMedicineWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.074
Threshold uncertainty score0.250

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.042
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0080.003
Scholarly communication0.0040.004
Open science0.0040.005
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0740.020

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.

Opus teacher head0.414
GPT teacher head0.593
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations29
Published2016
Admission routes1
Has abstractyes

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