Promoting Family Engagement With Early Childhood Developmental Screening via the Baby Steps Text Messaging and Web Portal System: Longitudinal Randomized Controlled Trial
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».