An Evidence-Based Digital Prevention Program to Improve Oral Health Literacy of People With a Migration Background: Intervention Mapping Approach
Bibliographic record
Abstract
BACKGROUND: Studies in Germany have shown that susceptible groups, such as people with a migration background, have poorer oral health than the majority of the population. Limited oral health literacy (OHL) appears to be an important factor that affects the oral health of these groups. To increase OHL and to promote prevention-oriented oral health behavior, we developed an evidence-based prevention program in the form of an app for smartphones or tablets, the Förderung der Mundgesundheitskompetenz und Mundgesundheit von Menschen mit Migrationshintergrund (MuMi) app. OBJECTIVE: This study aims to describe the development process of the MuMi app. METHODS: For the description and analysis of the systematic development process of the MuMi app, we used the intervention mapping approach. The approach was implemented in 6 steps: needs assessment, formulation of intervention goals, selection of evidence-based methods and practical strategies for behavior change, planning and designing the intervention, planning the implementation and delivery of the intervention, and planning the evaluation. RESULTS: On the basis of our literature search, expert interviews, and a focus group with the target population, we identified limited knowledge of behavioral risk factors or proper oral hygiene procedures, limited proficiency of the German language, and differing health care socialization as the main barriers to good oral health. Afterward, we selected modifiable determinants of oral health behavior that were in line with behavior change theories. On this basis, performance objectives and change objectives for the relevant population at risk were formalized. Appropriate behavior change techniques to achieve the program objectives, such as the provision of health information, encouragement of self-control and self-monitoring, and sending reminders, were identified. Subsequently, these were translated into practical strategies, such as multiple-choice quizzes or videos. The resulting program, the MuMi app, is available in the Apple app store and Android app store. The effectiveness of the app was evaluated in the MuMi intervention study. The analyses showed that users of the MuMi app had a substantial increase in their OHL and improved oral hygiene (as measured by clinical parameters) after 6 months compared with the control group. CONCLUSIONS: The intervention mapping approach provided a transparent, structured, and evidence-based process for the development of our prevention program. It allowed us to identify the most appropriate and effective techniques to initiate behavior change in the target population. The MuMi app takes into account the cultural and specific determinants of people with a migration background in Germany. To our knowledge, it is the first evidence-based app that addresses OHL among people with a migration background.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".