Using cluster analysis to interpret the variability of gross motor scores of children with typical development.

BACKGROUND

Longitudinal research on gross motor percentile rank scores of children with typical development has documented intra-individual variability of scoring patterns. Clinically, interpreting these fluctuations presents a challenge for therapists.

OBJECTIVE

The aim of this study was to determine the utility of cluster analysis as a technique to organize the gross motor scoring patterns of children with typical development into clinically relevant groups.

DESIGN

This was a descriptive, exploratory study using data from 2 longitudinal studies.

PARTICIPANTS

Sixty-six children with typical development participated in the study.

METHODS

The children were assessed on the gross motor subscale of the Peabody Developmental Motor Scales at 9, 11, 13, 16, and 21 months of age and on the gross motor subscale of the Peabody Developmental Motor Scales, 2nd edition, at 4, 4.5, 5, and 5.5 years of age. Demographic and health data were collected. Parents were interviewed when the children were 8 years of age. Cluster analysis was conducted. Demographic and health data were compared across clusters.

RESULTS

Four distinct and clinically relevant clusters were identified. A significant difference was found among the clusters for total number of illnesses.

LIMITATIONS

The children in these analyses were at low risk for gross motor problems. Further research with a more high-risk sample is needed to validate the clinical utility of the identified clusters.

CONCLUSIONS

Cluster analysis techniques may offer a mechanism to explore longitudinal data in physical therapy research. The techniques provided a mechanism to group data without losing the richness of information provided by the intra-individual variability of scoring patterns. Clinically, examination of distinct scoring patterns may lead to improved accuracy in screening for gross motor concerns compared with the traditional use of single-assessment cutoff points.