ABSTRACT
The prevalence of childhood overweight and obesity is increasing in the last decades, also in children with Cerebral Palsy (CP). Even though it has been established that an increase in weight can have important negative effects on gait in healthy adults and children, it has not been investigated what the effect is of an increase in body weight on the characteristics of gait in children with CP. In CP, pre and post three-dimensional gait analyses are performed to assess the effectiveness of an intervention. As a considerable amount of time can elapse between these measurements, and the effect of an alteration in the body weight is not taken into consideration, this effect of increased body weight is of specific importance. Thirty children with the predominantly spastic type of CP and 15 typically developing (TD) children were enrolled (age 3-15 years). All children underwent three-dimensional gait analysis with weight-free (baseline) and weighted (10% of the body weight added around their waist) trials. Numerous gait parameters showed a different response to the added weight for TD and CP children. TD children increased walking velocity, step- and stride length, and decreased double support duration with a slightly earlier timing of foot-off, while the opposite was found in CP. Similarly, increased ranges of motion at the pelvis (coronal plane) and hip (all planes), higher joint angular velocities at the hip and ankle, as well as increased moments and powers at the hip, knee and ankle were observed for TD children, while CP children did not change or even showed decreases in the respective measures in response to walking with added weight. Further, while TD children increased their gastrocnemius EMG amplitude during weighted walking, CP children slightly decreased their gastrocnemius EMG amplitude. As such, an increase in weight has a significant effect on the gait pattern in CP children. Clinical gait analysts should therefore take into account the negative effects of increased weight during pre-post measurements to avoid misinterpretation of treatment results. Overweight and obesity in CP should be counteracted or prevented as the increased weight has detrimental effects on the gait pattern.
ABSTRACT
Three-dimensional gait analysis (3DGA) generates a wealth of highly variable data. Gait classifications help to reduce, simplify and interpret this vast amount of 3DGA data and thereby assist and facilitate clinical decision making in the treatment of CP. CP gait is often a mix of several clinically accepted distinct gait patterns. Therefore, there is a need for a classification which characterizes each CP gait by different degrees of membership for several gait patterns, which are considered by clinical experts to be highly relevant. In this respect, this paper introduces Bayesian networks (BN) as a new approach for classification of 3DGA data of the ankle and knee in children with CP. A BN is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. Furthermore, they provide an explicit way of introducing clinical expertise as prior knowledge to guide the BN in its analysis of the data and the underlying clinically relevant relationships. BNs also enable to classify gait on a continuum of patterns, as their outcome consists of a set of probabilistic membership values for different clinically accepted patterns. A group of 139 patients with CP was recruited and divided into a training- (n=80% of all patients) and a validation-dataset (n=20% of all patients). An average classification accuracy of 88.4% was reached. The BN of this study achieved promising accuracy rates and was found to be successful for classifying ankle and knee joint motion on a continuum of different clinically relevant gait patterns.
