Modeling a gross motor curve of typically developing Dutch infants from 3.5 to 15.5 months based on the Alberta Infant Motor Scale. (June 2021)

• Record Type: Journal Article
• Authors: Boonzaaijer, Marike; Oudgenoeg-Paz, Ora; Suir, Imke; Westers, Paul; Nuysink, Jacqueline; Volman, Michiel; Jongmans, Marian
• Is Part Of: Early human development. Volume 157(2021)
• Publication Date: 2021-06
• Subjects: AIMS Albert Infant Motor Scale -- PDMS Peabody Developmental Motor Scales -- IMP Infant Motor Profile -- ASQ-II Ages and Stages Questionnaire, second edition -- MSEL Mullen Scales of Early Learning -- PPT pediatric physical therapist -- LGM Latent Growth Model -- LMM Linear Mixed Models -- ICC Intraclass Correlation Coefficient -- CP Cerebral Palsy; Gross motor development -- AIMS -- Infants -- Growth curve -- Longitudinal design; Fetus -- Periodicals; Neonatology -- Periodicals; Prenatal influences -- Periodicals; 612.65
• DOI: 10.1016/j.earlhumdev.2021.105366
• Languages: English
• ISSNs: 0378-3782

Abstract

Abstract: Background: Interindividual variability in gross motor development of infants is substantial and challenges the interpretation of motor assessments. Longitudinal research can provide insight into variability in individual gross motor trajectories. Purpose: To model a gross motor growth curve of healthy term-born infants from 3.5 to 15.5 months with the Alberta Infant Motor Scale (AIMS) and to explore groups of infants with different patterns of development. Methods: A prospective longitudinal study including six assessments with the AIMS. A Linear Mixed Model analysis (LMM) was applied to model motor growth, controlled for covariates. Cluster analysis was used to explore groups with different pathways. Growth curves for the subgroups were modelled and differences in the covariates between the groups were described and tested. Results: In total, data of 103 infants was included in the LMM which showed that a cubic function (F(1, 571) = 89.68, p < 0.001) fitted the data best. None of the covariates remained in the model. Cluster analysis delineated three clinically relevant groups: 1) Early developers (32%), 2) Gradual developers (46%), and 3) Late bloomers (22%). Significant differences in covariates between the groups were found for birth order, maternal education and maternal employment. Conclusion: The current study contributes to knowledge about gross motor trajectories of healthy term born infants. Cluster analysis identified three groups with different gross motor trajectories. The motor growth curve provides a starting point for future research on motor trajectories of infants at risk and can contribute to accurate screening. Highlights: A non-linear cubic function best describes the AIMS motor growth curve of infants aged 3.5–15.5 months. None of the child- and environmental factors had a significant effect on the motor growth curve. Cluster analysis identified three groups: Early developers, Gradual developers and Late bloomers.