Uncovering distinct motor development trajectories in infants during the first half year of life.

Infants undergo significant developmental changes in the first few months of life. While some risk factors increase the risk of developmental disability, such as preterm birth, the developmental trajectories of infants born pre-term (PT) and full-term (FT) present with individual variability. This study aims to investigate whether the utilization of data-driven unsupervised machine learning can identify patterns within groups of infants and categorize infants into specific developmental trajectories. Thirty-four infants, 19 FT and 15 PT, were assessed with the gross and fine motor subscales of the Bayley Scales of Infant and Toddler Development, version III (BSID-III) monthly for 2-5 visits between the ages of 1 and 6 months. Latent class growth analysis (LCGA) models were adopted to identify clusters of motor developmental trajectories during this critical time. Based on statistical significance, the linear, 2-class trend was selected as the best-fitting model for both gross and fine motor trajectories. Within this, LCGA reveals 2 developmental trends with varying beginning scores and developmental rates, including the low-baseline slow-growth (LBSG) subgroup, and the high-baseline fast-growth (HBFG) subgroup, with age (adjusted for prematurity) being equally distributed across both subgroups. Both subgroups, HBFG and LBSG, had a combination of infants born FT and PT (55% FT in HBFG, 56% FT in LBSG), supporting that preterm birth alone may not sufficiently categorize an infant's developmental trajectory. The later BSID-III gross motor score showed marginal difference between groups (p = 0.062). Similarly, the fine motor model displayed a mixture of both infants born FT and PT (68% FT in HBFG, 40% FT in LBSG). In this case, the late motor composite BSID score was different between groups (p = 0.04). Our study uses a novel approach of LCGA to elucidate heterogeneous trajectories of motor development for gross and fine motor skills during the first half of life and offers potential for early identification of subgroup membership. Furthermore, these findings underscore the necessity for individualized risk assessments and intervention strategies tailored to individual needs. Ultimately, further validation of these models may provide usefulness in uncovering distinct motor development trajectories in infants.