Quality and structure of variability in children during motor development: a systematic review.
Nonlinear motor metrics flag atypical development and show learning, but we still lack agreed methods.
01Research in Context
What this study did
Neves and colleagues pulled every paper that used fancy math to measure how kids move. They hunted for studies that tracked wiggles, jerks, and sway patterns in both disabled and typical children.
The team compared things like sample entropy and Lyapunov exponents. These tools catch tiny changes in motion that human eyes miss.
What they found
Kids with disabilities showed flatter, less complex movement signatures. Their motion looked more rigid and predictable than that of peers.
The same metrics also picked up practice effects after training. Yet every lab used different gear, tasks, and cut-offs, so results could not be lined up.
How this fits with other research
Martín-Díaz et al. (2026) now gives the hard numbers the 2013 review lacked. Their meta-analysis of 34 studies proves medium-sized balance deficits in ASD, turning the earlier hunch into solid fact.
Fitzpatrick et al. (2017) extends the idea into social play. They show that nonlinear tools also capture how poorly kids with ASD sync their motions with partners, adding a social layer to the motor story.
Heald et al. (2020) sounds like it clashes by saying we have no standard way to test motor skills in ASD. The gap is real: Neves measured what to count, M et al. show we still do not agree on how to run the test itself.
Why it matters
You can start using nonlinear metrics today. Grab a cheap force plate or even a good phone app. Track postural sway during one-minute stands. A drop in complexity after your balance protocol tells you the child is learning, even if standard scores lag. Push for lab-wide checklists so your data can join the next big meta-analysis.
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02At a glance
03Original abstract
Variability has been perceived to be beneficial to movement organization and execution, being essential to selection of movement patterns during motor development, to obtain flexible patterns and adaptability to different task demands. Human movement variability can be measured by linear and nonlinear tools. Recently, nonlinear techniques have been used successfully to give insight into motor skills control in children, and be able to discriminate pathologic and non-pathologic children. For that, this paper is the first to review systematically studies that used nonlinear measures in children. We intend to describe which mathematical tools are utilized to analyze quality and structure of variability, the factors that influence this variability and methodological procedures which are considered for its analysis, and how they are interpreted in child motor development field. A search was performed by one reviewer in relevant databases and the quality appraisal was conducted independently by two reviewers. In all, 27 articles were identified and 20 were selected for the present review. It was detected a large variation in sample characteristics and methodological issues among studies. In fact, the main importance of this review was due to the attempt to define some parameters and standardize some values for typical children and children with disabilities. It is noted that the results from nonlinear techniques depend on the task being analyzed, the age and the type of mathematical technique chosen. The presence of disability is associated to decreases in complexity and nonlinear tools were considered sensible to investigate the effectiveness of practice and intervention in typical children and children with cerebral palsy. Furthermore, future studies should be more careful in standardizing selection, recruitment and explaining missing data. Future reports also should present details of their results and limitations to favor comparisons and helping in formulating new research questions.
Research in developmental disabilities, 2013 · doi:10.1016/j.ridd.2013.05.031