Related Experiment Video
Updated: Oct 6, 2025
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
Published on: May 17, 2024
Development of a standards-based phenotype model for gross motor function to support learning health systems in
Nikolas Koscielniak1, Gretchen Piatt2, Charles Friedman2
1Clinical and Translational Science Institute Wake Forest University School of Medicine Winston-Salem North Carolina USA.
Insights
This study developed a data model to capture gross motor function (GMF) for children with Cerebral Palsy (CP), enabling research and quality improvement in pediatric rehabilitation. The model standardizes data for better care and treatment impact measurement.
Area of Science:
- Pediatric Rehabilitation
- Biomedical Informatics
- Clinical Data Standards
Background:
- Standardized data and systematic approaches are crucial for research and quality improvement in pediatric rehabilitation.
- Existing data infrastructure often lacks the specificity needed for detailed clinical research.
Purpose of the Study:
- To determine the capacity for capturing Gross Motor Function (GMF) data in children with Cerebral Palsy (CP).
- To develop a data infrastructure model for research and quality improvement activities.
- To demonstrate a systematic approach for leveraging existing data for pediatric care enhancement.
Main Methods:
- Systematic examination of pediatric data concepts within a learning network.
- Iterative construction of GMF phenotype models using standardized data elements and GMFCS case definitions.
- Theory and expert-informed selection of data concepts, organized into five domains.
Main Results:
- Identified 65 data element concepts for the overall GMF phenotype model.
- Developed 20 variables and logic statements to classify GMF into three clinically meaningful classes.
- Organized data elements into Neurologic Function, Mobility Performance, Activity Performance, Motor Performance, and Device Use domains.
Conclusions:
- The developed approach enables organizations to utilize existing data for care improvement and research.
- This represents the first consensus-based, theory-driven specification for GMF data elements and logic.
- Further research is needed to validate the phenotype model and its utility in differentiating GMF classes for various healthcare system activities.
Introduction:
Research and continuous quality improvement in pediatric rehabilitation settings require standardized data and a systematic approach to use these data.
Methods:
We systematically examined pediatric data concepts from a pediatric learning network to determine capacity for capturing gross motor function (GMF) for children with Cerebral Palsy (CP) as a demonstration for enabling infrastructure for research and quality improvement activities of an LHS. We used an iterative approach to construct phenotype models of GMF from standardized data element concepts based on case definitions from the Gross Motor Function Classification System (GMFCS). Data concepts were selected using a theory and expert-informed process and resulted in the construction of four phenotype models of GMF: an overall model and three classes corresponding to deviations in GMF for CP populations.
Results:
Sixty five data element concepts were identified for the overall GMF phenotype model. The 65 data elements correspond to 20 variables and logic statements that instantiate membership into one of three clinically meaningful classes of GMF. Data element concepts and variables are organized into five domains relevant to modeling GMF: Neurologic Function, Mobility Performance, Activity Performance, Motor Performance, and Device Use.
Conclusion:
Our experience provides an approach for organizations to leverage existing data for care improvement and research in other conditions. This is the first consensus-based and theory-driven specification of data elements and logic to support identification and labeling of GMF in patients for measuring improvements in care or the impact of new treatments. More research is needed to validate this phenotype model and the extent that these data differentiate between classes of GMF to support various LHS activities.
