Promoting Family Engagement With Early Childhood Developmental Screening via the Baby Steps Text Messaging and Web Portal System: Longitudinal Randomized Controlled Trial
Bibliographic record
Abstract
Background: Approximately 1 in 6 US children has a developmental disability. Early detection is crucial but often delayed, especially in families with limited access to resources. Current paper-based screening methods, such as the Ages and Stages Questionnaire, face challenges such as cultural barriers and timing issues. Digital tools can improve parent engagement and screening accuracy. This research explores new technologies to enhance long-term parent involvement in developmental screening. Objective: The study aims to understand whether features of a digital intervention specifically designed to engage parents in developmental screening are effective over a long-term period. Methods: Parents of children between 7 and 12 months old were recruited through flyers at clinics and libraries, mailing lists, and social media, and then they self-enrolled after eligibility screening. We conducted a randomized controlled trial with 139 families over 20 months, along with follow-up interviews and surveys. The intervention consisted of an interactive web portal that combined developmental and sentimental record-keeping, family-friendly visualizations, and the ability to answer screening questions via multiple modalities (eg, text messaging and web), without involvement of health care providers. The control condition consisted of a web-based portal with no specific engagement features, modeled after standard web-based developmental screening tools. Results: Overall, we enrolled 67 parents in the control group and 72 parents in the experimental group, for a total of 139 enrolled participants. Several parent engagement strategies we deployed in the experimental group were effective in increasing milestone questionnaire completion, with text messaging standing out as the most impactful and efficient, offering the highest return relative to the effort required for its development and implementation. Overall, the experimental group demonstrated a 44% higher average response rate compared to controls (t125=-3.32, P<.01). Participants in the experimental group submitted significantly more timely and valid responses, after text messaging was introduced (phase 2: 95% vs phase 1: 71%; t107=-4.44, P<.01), which is a critical factor for effective and timely tracking of child development. The experimental group participants responded to more questions on average (mean 127.60, SD 49.01) than those in the control group (t70=-7.23, P<.01) in phase 2 as well. In addition, study completion rates were significantly higher in the experimental group (83% vs 30%; t119=-8.40, P<.01), indicating greater long-term engagement. Sentimental record-keeping features showed promise but limited use, suggesting the need for integration with tools parents already use. Conclusions: This study demonstrates that a human-centered design approach for technology-based interventions can significantly enhance parent engagement and completion rates of developmental screening questionnaires. However, further research is needed with a larger sample to determine whether such features effectively prompt parents to seek early intervention services. Future studies should focus on engaging more diverse and underserved populations to validate these findings.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".