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As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.
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This content will become publicly available on July 1, 2026
A matter of time: Developmental cascades for developmental science
The pace and breadth of infant development is remarkable—by their third birthday, infants acquire an impressive range of skills across multiple domains. Studying the complexities of cross-domain development, however, poses a challenge for a field of researchers with traditionally siloed expertise. The developmental cascades framework—the conceptual view that disparate domains are interconnected and reciprocally influential—offers researchers a flexible approach to identify and describe infant development. Over the past quarter century, cascades have surged in popularity among infancy researchers. In this review, we provide a history of developmental cascades research and highlight its contributions to the science of infant behavior and development. We discuss contemporary themes and challenges cascades researchers face (e.g., how to clear the high bar needed to establish causality among links in a cascading chain of events) and make recommendations for future research. Specifically, we propose that adopting a cascades approach encourages researchers to: (1) consider the ‘whole child’ by charting connections across different domains; (2) examine multiple timescales by linking moment-to-moment interactions to broader changes across development; (3) embrace complexity and foster interdisciplinary collaboration; and (4) gather evidence for causal pathways by combining the rigor of lab experiments with the richness of natural observations. Finally, we consider future directions for the next quarter century of cascades research—for developmental science, applied psychology, and clinical intervention.
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- Award ID(s):
- 2341398
- PAR ID:
- 10621602
- Editor(s):
- Rankin, Lela
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Infant Behavior and Development
- Volume:
- 80
- Issue:
- C
- ISSN:
- 0163-6383
- Page Range / eLocation ID:
- 102078
- Subject(s) / Keyword(s):
- Cascades infancy methods
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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