Mobile Phone Multilevel and Multimedia Messaging Intervention for Breast Cancer Screening: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Despite the increasing breast cancer incidence and mortality rates, Korean American immigrant women have one of the lowest rates of breast cancer screening across racial groups in the United States. Mobile health (mHealth), defined as the delivery of health care information or services through mobile communication devices, has been utilized to successfully improve a variety of health outcomes. OBJECTIVE: This study adapted the principles of mHealth to advance breast cancer prevention efforts among Korean American immigrant women, an underserved community. METHODS: Using a randomized controlled trial design, 120 Korean American women aged 40 to 77 years were recruited and randomly assigned to either the mMammogram intervention group (n=60) to receive culturally and personally tailored multilevel and multimedia messages through a mobile phone app along with health navigator services or the usual care control group (n=60) to receive a printed brochure. Outcome measures included knowledge, attitudes, and beliefs about breast cancer screening, readiness for mammography, and mammogram receipt. The feasibility and acceptability of the mMammogram intervention was also assessed. RESULTS: The intervention group showed significantly greater change on scores of knowledge of breast cancer and screening guidelines (P=.01). The intervention group also showed significantly greater readiness for mammography use after the intervention compared with the control group. A significantly higher proportion of women who received the mMammogram intervention (75%, 45/60) completed mammograms by the 6-month follow-up compared with the control group (30%, 18/60; P<.001). In addition, the intervention group rated satisfaction with the intervention (P=.003), effectiveness of the intervention (P<.001), and increase of knowledge on breast cancer and screenings (P=.001) significantly higher than the control group. CONCLUSIONS: A mobile phone app-based intervention combined with health navigator service was a feasible, acceptable, and effective intervention mechanism to promote breast cancer screening in Korean American immigrant women. A flexible, easily tailored approach that relies on recent technological advancements can reach underserved and hard-to-recruit populations that bear disproportionate cancer burdens. TRIAL REGISTRATION: Clinicaltrials.gov NCT01972048; https://clinicaltrials.gov/show/NCT01972048 (Archived by WebCite at https://clinicaltrials.gov/archive/NCT01972048/2013_10_29).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".