ArticleInternational journal of environmental research and public health2019
Hip and Wrist-Worn Accelerometer Data Analysis for Toddler Activities.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old).The international journal of behavioral nutrition and physical activity · 2022Pooled it
- Assessment of 24-hour physical behaviour in children and adolescents via wearables: a systematic review of free-living validation studies.BMJ open sport & exercise medicine · 2022Pooled it
- Impact of cut-point methods on classification of physical activity and sedentary behaviour of toddlers.BMC public health · 2025Trial
- Compliance With the 24-h Movement Behaviours Guidelines Among Brazilian Toddlers.Child: care, health and development · 2025Article
- Physical Activity in Pre-Ambulatory Children with Cerebral Palsy: An Exploratory Validation Study to Distinguish Active vs. Sedentary Time Using Wearable Sensors.Sensors (Basel, Switzerland) · 2025Article
- Preschoolers' Moderate-to-Vigorous Physical Activity Measured by a Tri-Axial Accelerometer: Compliance with International Guidelines and Different Cut-Points.Children (Basel, Switzerland) · 2024Article
- Long-form recording of infant body position in the home using wearable inertial sensors.Behavior research methods · 2024Article
- Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants.Communications medicine · 2022Article
- Sensing everyday activity: Parent perceptions and feasibility.Infant behavior & development · 2021Article
- A Contactless Method for Measuring Full-Day, Naturalistic Motor Behavior Using Wearable Inertial Sensors.Frontiers in psychology · 2021Article
- Calibrating Wrist-Worn Accelerometers for Physical Activity Assessment in Preschoolers: Machine Learning Approaches.JMIR formative research · 2020Article
- Daily Physical Activity Among Toddlers: Hip and Wrist Accelerometer Assessments.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants' performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was "carried" by an adult (median = 144 counts/5 s;
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