Gross motor skills in toddlers: Prevalence and socio-demographic differences
Purpose: To examine the relationships between gross motor skills, age, gender, and socio-economic status in toddlers. The hope is to gain information which may be used in future research to actively develop gross motor skills in populations/areas of concern.
Study Population: 335 children (age: 19.80 ± 4.08 months, BMI: 17.84 ± 1.69) from New South Wales, Australia.
Methods: Cross sectional study was performed on a population of children who were recruited from Early Childhood Education and Care programs in New South Wales, Australia.
The Peabody Developmental Motor Scales Second Edition (PDMS-2) was used to assess gross motor skills. Since the focus on this study is on gross motor skills, only the Gross Motor Skills (GMS) subset of the PDMS-2 was used. Locomotion, stability, and object manipulation subtests comprise the GMS subset. Items are scored from 0-2 based on child’s performance. A score of 2 indicates mastery of skill, while a score of 1 indicates performance does not meet all criteria for mastery of skill, and a score of 0 indicates inability or refusal to perform skill. The entry point of the PDMS-2 is determined by the child’s age. The test is administered in reverse until the child scores 2 on three consecutive tasks (basal level). The tester progresses forward until the child scores 0 on three consecutive tasks (ceiling level).
Using examiner’s manual, the researcher is able to convert raw scores to standard scores, and standard scores to gross motor quotient (GMQ). Children’s scores were grouped as above or below average for analysis.
Parent questionnaires and the Australian Socio-Economic Index for Areas (SEIFA) were used to determine socio-economic status of families. SEIFA ranks areas based on socioeconomic disadvantage with lower score being worse than higher scores (1-10). Information on family income was also used in analysis. Multiple statistical analyses were performed including: chi-square tests for categorical data, two tailed t-tests for sex differences, and one-way ANOVA for girls and boys above 20 months, and girls and boys below 20 months. While standard scores were used for analysis between age and gender, GMQ was used in a linear regression with socio-economic variables as recommended by the manual.
Outcome Measures: A one-time score of the GMS subset of the PDMS-2.
Intervention: There was no intervention, as this is a cross-sectional study.
Results: Of 335 children, 23.3% scored below average and 6.9% scored above average on GMQ. Regarding subtests, 34.3% scored below average for locomotion, 10.1% for object manipulation, and 0.3% (1 child) for stability. Boys scored significantly better than girls in object manipulation regardless of age. In locomotion, girls younger than 20 months scored significantly better than boys older than 20 months. Furthermore, boys younger than 20 months scored significantly better than both boys and girls older than 12 months. In general, GMS scores worsened with age. GMS scores also worsened with better socio-economic status (higher SEIFA index).
Strengths: Large sample size, provides rationale for further investigation (possibly intervention studies) of populations lacking in gross motor skills.
Limitations: Comparing Australian toddlers to US norms, selected variables to analyze with GMS, Using one-way ANOVA instead of two-way ANOVA for analysis, no use of MDC or MDIC of PDMS-2.
Conclusion: This study has novel findings which suggest that a greater percentage of children have below average gross motor skills compared to previously established norms. It finds that as toddlers age, they perform worse on gross motor skills assessments. This study also suggests that boys are better at object manipulation (catching and throwing) than girls regardless of age. From these findings, it seems that children older than 20 months, especially girls are at risk of undeveloped gross motor skills. The authors hypothesize that children of lower income families may have more free play which improves performance on gross motor skills assessments. Perhaps this hypothesis applies generationally as well. Children in the new generation may have more access to technology such as smart phones, and tablets which increase screen time and limit free play. Children with access to these technologies may independently operate them as they age which further increases their screen time and limits free play. With continued research we can identify factors that are associated with worse GMS in children and provide interventions to mitigate those negative effects.
Veldman SLC, Jones RA, Santos R, Sousa-Sá E, Okely AD. Gross motor skills in toddlers: Prevalence and socio-demographic differences. J Sci Med Sport. 2018;21(12):1226-1231. doi:10.1016/j.jsams.2018.05.001