Evaluating the Effectiveness of an Intelligent mHealth Intervention for Child Unintentional Injury Prevention: Protocol for a Cluster Randomized Controlled Trial
Bibliographic record
Abstract
Background: Unintentional injury is a leading cause of childhood morbidity and mortality worldwide. In China, real-world implementation of child injury prevention efforts remains inadequate due to constrained workforce capacity and a lack of operational frameworks. Objective: This study aims to assess the effectiveness of a mobile health (mHealth) intervention, the Intelligent Child Unintentional-Injury Reduction & Education (iCURE) project, embedded within China's National Basic Public Health Service Program. The intervention relies on a WeChat (Tencent) service account for caregivers and a web-based platform for health care providers to deliver standardized unintentional injury prevention strategies for young children. Key features of the program include interactive questions and answers, injury risk assessment with instant feedback, a tailored injury prevention knowledge disseminator, and regular reminders to caregivers. Methods: A double-blind, 12-month follow-up, cluster randomized controlled trial will be implemented in Changsha, Hunan Province, China. Caregivers of children aged ≤5 years will be recruited. Randomization will be conducted at the street or town level. The control group will receive routine safety education, while the intervention group will receive both routine safety education and the iCURE mHealth intervention focused on unintentional injury prevention and delivered via WeChat. Data will be collected at baseline and every 3 months during the study period. The primary outcome is 12-month incidence of unintentional injuries among children, including minor injuries and as reported by caregivers. Secondary outcomes include children's injury risk level and caregiver supervision behaviors assessed using a standard questionnaire. Data analysis will be conducted using generalized linear mixed models with a Poisson link function and generalized estimating equations to assess the effectiveness of the iCURE intervention, following intention-to-treat principles. Sensitivity analyses will be conducted with per-protocol principles and excluding participants with missing primary outcomes. Results: As of May 2025, a total of 6701 participants have been successfully enrolled and baseline data were collected for all participants. Of those enrolled, 87.2% (5842/6,701) completed the first follow-up assessment. Conclusions: This trial will examine the effectiveness of an intelligent mHealth intervention for child unintentional injury prevention building on China's National Basic Public Health Service Program. If successful, the iCURE intervention may provide a cost-effective strategy for child injury prevention in low- and middle-income countries.
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 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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.009 |
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".