Co-Design and Development of the SmilesUp Text Messaging Intervention Using Behavioral Theory to Support Parents of Children With Early Childhood Caries: Mixed Methods Study
Bibliographic record
Abstract
Background: Early childhood caries (ECC) remains a common childhood condition that affects 600 million children worldwide. Providing parents with support for oral health behavior change can address ECC risk factors and complement preventive clinical care. Mobile health (mHealth) text message programs that are co-designed and evaluated by parents and health professionals using behavior theory have been shown to be effective in improving oral health outcomes. Objective: This study aimed to describe the co-design process, development, and content evaluation of a text message program designed to promote oral health behavior change among parents of children diagnosed with ECC using the Behavior Change Wheel (BCW) framework. Methods: The SmilesUp mHealth program was co-designed with parents in 2 stages using the BCW, a widely used theoretical framework to underpin mHealth programs, recommended by the World Health Organization. Through focus groups with parents in phase 1, the BCW was used to understand parental perspectives by identifying barriers and enablers and selecting target behaviors that could be feasibly delivered within a mHealth intervention. Barriers and enablers were mapped to the relevant theoretical domains and behavior change technique (BCT) of the BCW. Phase 2 evaluated content acceptability, measured by understandability, usefulness, and appropriateness of the program through questionnaires with parents and health professionals. Highly rated messages were finalized into an algorithm for the SMS text message program. Results: In phase 1, the overall target behavior was parental behavior change to support good oral health, including oral hygiene, reduced dietary sugar intake, and bedtime routines for their children. The 5 intervention functions focused on education, modeling, persuasion, environmental restructuring, and enablement, and 16 BCTs focused on addressing the motivational enablers and knowledge gap barriers identified by the parents. A total of 111 draft health messages were developed and mapped to the BCTs. In phase 2, a total of 2045 reviews of the 111 draft messages were completed by parents (14/31, 45.2%) and health professionals (17/31, 54.8%). Parents rated 77.4% (86/111) and health professionals rated 61.2% (68/111) of the messages as understandable, useful, and accepted. The messages that were considered understandable, useful, and appropriate by both groups were incorporated into the SmilesUp 12-week semipersonalized SMS message program. Conclusions: The SmilesUp mHealth program uses behavioral theory to address knowledge gaps in tooth brushing, diet, and bedtime routines identified by parents. It provides parents with convenient, bite-sized nudges of information to support oral health-promoting behaviors in the home context. Robust content development and evaluation are crucial initial steps before further investments are made to conduct a clinical trial to assess the effectiveness of the program.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".