BACKGROUND AND PURPOSE: Development of gross motor function in children with cerebral palsy (CP) has not been documented. The purposes of this study were to examine a model of gross motor function in children with CP and to apply the model to construct gross motor function curves for each of the 5 levels of the Gross Motor Function Classification System (GMFCS). SUBJECTS: A stratified sample of 586 children with CP, 1 to 12 years of age, who reside in Ontario, Canada, and are known to rehabilitation centers participated. METHODS: Subjects were classified using the GMFCS, and gross motor function was measured with the Gross Motor Function Measure (GMFM). Four models were examined to construct curves that described the nonlinear relationship between age and gross motor function. RESULTS: The model in which both the limit parameter (maximum GMFM score) and the rate parameter (rate at which the maximum GMFM score is approached) vary for each GMFCS level explained 83% of the variation in GMFM scores. The predicted maximum GMFM scores differed among the 5 curves (level I=96.8, level II=89.3, level III=61.3, level IV=36.1, and level V=12.9). The rate at which children at level II approached their maximum GMFM score was slower than the rates for levels I and III. The correlation between GMFCS levels and GMFM scores was (.91. Logistic regression, used to estimate the probability that children with CP are able to achieve gross motor milestones based on their GMFM total scores, suggests that distinctions between GMFCS levels are clinically meaningful. CONCLUSION AND DISCUSSION: Classification of children with CP based on functional abilities and limitations is predictive of gross motor function, whereas age alone is a poor predictor. Evaluation of gross motor function of children with CP by comparison with children of the same age and GMFCS level has implications for decision making and interpretation of intervention outcomes.
BACKGROUND AND PURPOSE: Development of gross motor function in children with cerebral palsy (CP) has not been documented. The purposes of this study were to examine a model of gross motor function in children with CP and to apply the model to construct gross motor function curves for each of the 5 levels of the Gross Motor Function Classification System (GMFCS). SUBJECTS: A stratified sample of 586 children with CP, 1 to 12 years of age, who reside in Ontario, Canada, and are known to rehabilitation centers participated. METHODS: Subjects were classified using the GMFCS, and gross motor function was measured with the Gross Motor Function Measure (GMFM). Four models were examined to construct curves that described the nonlinear relationship between age and gross motor function. RESULTS: The model in which both the limit parameter (maximum GMFM score) and the rate parameter (rate at which the maximum GMFM score is approached) vary for each GMFCS level explained 83% of the variation in GMFM scores. The predicted maximum GMFM scores differed among the 5 curves (level I=96.8, level II=89.3, level III=61.3, level IV=36.1, and level V=12.9). The rate at which children at level II approached their maximum GMFM score was slower than the rates for levels I and III. The correlation between GMFCS levels and GMFM scores was (.91. Logistic regression, used to estimate the probability that children with CP are able to achieve gross motor milestones based on their GMFM total scores, suggests that distinctions between GMFCS levels are clinically meaningful. CONCLUSION AND DISCUSSION: Classification of children with CP based on functional abilities and limitations is predictive of gross motor function, whereas age alone is a poor predictor. Evaluation of gross motor function of children with CP by comparison with children of the same age and GMFCS level has implications for decision making and interpretation of intervention outcomes.
Authors: Sylke J Steggerda; Francisca T De Bruïne; Annette A van den Berg-Huysmans; Monique Rijken; Lara M Leijser; Frans J Walther; Gerda van Wezel-Meijler Journal: Cerebellum Date: 2013-12 Impact factor: 3.847
Authors: Jason R Wingert; Harold Burton; Robert J Sinclair; Janice E Brunstrom; Diane L Damiano Journal: Dev Med Child Neurol Date: 2008-09-20 Impact factor: 5.449
Authors: Kristie L Bell; Roslyn N Boyd; Sean M Tweedy; Kelly A Weir; Richard D Stevenson; Peter S W Davies Journal: BMC Public Health Date: 2010-04-06 Impact factor: 3.295
Authors: Mary Rutherford; Luca A Ramenghi; A David Edwards; Peter Brocklehurst; Henry Halliday; Malcolm Levene; Brenda Strohm; Marianne Thoresen; Andrew Whitelaw; Denis Azzopardi Journal: Lancet Neurol Date: 2009-11-05 Impact factor: 44.182