Evidence map›Paper›PMID 42241920›Full record

ArticleInfant behavior & development2026

Characterizing infant leg movements using 72-h wearable sensor data: Descriptive analysis from a large, heterogenous sample of infants 0-2 months of age from the HEALthy Brain and Child Development Study.

Jinseok Oh, Nicolò Pini, Camille Nebeker, Beth A Smith, Novel Technology/Wearable Sensors Working Group

Abstract read
In one paragraph

Article in Infant behavior & development, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jinseok OhDivision of Developmental-Behavioral Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Nicolò PiniDepartment of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, USA.
Camille NebekerHerbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, CA, USA; The Qualcomm Institute, UC San Diego, La Jolla, CA, USA.
Beth A SmithDivision of Developmental-Behavioral Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA; Developmental Neuroscience and Neurogenetics Program, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, USA; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: bsmith@chla.usc.edu.
Novel Technology/Wearable Sensors Working Group

Funding

Healthy Brain and Child Development National Consortium Data Coordinating CenterU24DA055330 · NIDA · WASHINGTON UNIVERSITY · PI ANDERS M DALE, Damien A Fair · 2021 to 2026
$34.6M
2/24 Healthy Brain and Child Development National ConsortiumU01DA055362 · NIDA · CHILDREN'S HOSPITAL OF LOS ANGELES · PI PAT LEVITT, Beth A Smith · 2021 to 2026
$9.6M
TR&D3 - Rapid Translation of AI-powered Temporally Precise mHealth Interventions via Efficient and Embeddable Trustworthy Biomarker ImplementationsP41EB028242 · NIBIB · UNIVERSITY OF MEMPHIS · PI Santosh Kumar · 2020 to 2026
$9.5M
NIBIB NIH HHS P41 EB028242NIDA NIH HHS U01 DA055362NIDA NIH HHS U24 DA055330
6 · The paper itself

Abstract

Spontaneous limb movements provide the foundation for motor development, yet knowledge of their normative characteristics has been limited by small homogenous samples and short observation windows. Leveraging the HEALthy Brain and Child Development (HBCD) study, we present descriptive characteristics of the first large-scale dataset of leg movements from 421 infants aged 0-2 months, captured with wearable sensors worn continuously for 72 h in naturalistic settings across multiple research sites in the United States. Our analysis focused on three domains: movement characteristics, variability of movement acceleration time-series, and physical activity intensity. Movement characteristics included leg movements per hour awake, peak acceleration per movement, average acceleration per movement, and movement duration. These characteristics aligned with earlier smaller-scale studies and showed highly consistent patterns between right and left legs. Sample entropy analysis revealed left-skewed distributions with median values near 1.3. Physical activity intensity estimations showed that infants spent the majority of time in sedentary activity, followed by light activity, with only brief periods of moderate-to-vigorous activity. Together, these findings provide initial characterization of multiple dimensions of infant leg movements, hence significantly contributing to derive reference distributions in early life. By establishing scalable, ecologically valid, and computationally tractable measures of infant motor behavior, this study lays the groundwork for integrating wearable sensing with longitudinal developmental science and for identifying early indicators of atypical trajectories. Ultimately, these findings contribute to the broader goals of the HBCD study: to understand how the brain develops and is shaped by environmental, social, and biological factors during pregnancy and after birth.

Indexed as

Child DevelopmentLegMotor ActivityWearable Electronic DevicesFemaleHumansInfantInfant, NewbornMaleMovementHBCD StudyInfant motor developmentLeg movementsPhysical activitySample entropyWearable sensors

Identifiers

PMID42241920
PMCPMC13284690

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.