Advancing Wrist‐Worn Accelerometry for Measurement of Infant Motor Behavior
Bibliographic Data
Wearable sensor technology, particularly accelerometry, is an emerging tool to measure infant motor behavior. We evaluated (1) differences and relationships between accelerometry‐derived variables in structured versus unstructured contexts and (2) the convergent validity of accelerometer‐derived variables with coded motor behavior during structured motor tasks and with caregiver reports of infant motor behavior. Typically developing infants ( n = 176, 6 months adjusted age) were evaluated via bilateral wrist‐worn accelerometers during structured, laboratory toy play tasks and at home for 2 days, unstructured. Eleven accelerometer variables quantifying duration, intensity, and variability of movement were extracted from each context and analyzed with respect to five variables from the video‐coded structured toy play task and caregiver report (Ages and Stages Questionnaire motor subscales). Infants and parents tolerated the devices as indicated by a usable data rate equivalent to the questionnaire response rate. Accelerometry values from the structured context were generally larger than those from the unstructured context, except for the variance variables. Values across the contexts were moderately and consistently related. Accelerometry variables were related to video‐coded variables but were only minimally related to caregiver reports of fine motor behavior. Upper limb accelerometry is a feasible, valid addition to the infant motor measurement toolbox
Accelerometer · Context (archaeology) · Convergent validity · Motor activity · Motor behavior · Motor skill · Wearable computer · Wearable technology · Cerebral Palsy and Movement Disorders · Infant Development and Preterm Care · Infant Health and Development
Activity-Based Sleep-Wake Identification
A multivariate hierarchical model for studying psychological change within married couples.
Testing the significance of a correlation with nonnormal data
Research electronic data capture (Redcap)—A metadata-driven methodology and workflow process for providing translational research informatics support
Welcome to the Tidyverse
The Redcap consortium
Fitting Linear Mixed-Effects Models Using lme4
LmerTest Package
| Highly cited | No |
|---|