Early childhood tracking application: Correspondence between crowd-based developmental percentiles and clinical tools

Area of Science:

• Pediatric Development
• Digital Health
• Developmental Screening

Background:

• Child developmental screening is crucial for early diagnosis and intervention.
• Barriers often lead to delayed identification of developmental delays.
• Mobile applications offer innovative approaches to developmental tracking.

Purpose of the Study:

• To evaluate the correspondence between crowd-based percentiles from the babyTRACKS app and traditional developmental measures.
• To assess the utility of babyTRACKS in reflecting child development compared to established tools.

Main Methods:

• Analysis of babyTRACKS diaries from 1951 children, recording milestone attainment ages.
• Comparison of app-derived percentiles with Centers for Disease Control (CDC) norms, Ages and Stages Questionnaire (ASQ-3), and Mullen Scales of Early Learning (MSEL).
• Statistical analysis to determine correlations and differences between babyTRACKS data and traditional assessments.

Main Results:

• babyTRACKS percentiles showed correlation with unmet CDC milestones and higher ASQ-3 and MSEL scores.
• Children not meeting CDC thresholds had significantly lower babyTRACKS percentiles.
• App percentiles corresponded well with traditional measures, especially in fine motor and language domains.

Conclusions:

• The babyTRACKS mobile application demonstrates correspondence with traditional developmental assessment measures.
• Crowd-based percentiles from babyTRACKS can potentially aid in monitoring child development.
• Further research is required to establish optimal referral thresholds within the app to minimize false alarms.