Physical Activity Energy Expenditure Predicts Quality of Life in Ambulatory School-Age Children with Cerebral Palsy

Proxy- and self-report evaluation of quality of life in cerebral palsy: Using Spanish version of CPQOL for Children and adolescents

BACKGROUND

Promoting quality of life (QoL) is one of the main goals in interventions carried out with children and adolescents with cerebral palsy (CP).

AIMS

The aim of this study was to analyze the determinants of QoL in children with CP, including evaluations by the children themselves and their parents, and to identify discrepancies between evaluators.

METHODS AND PROCEDURES

The adapted Spanish version of the Cerebral Palsy Quality of Life (CP-QOL) for children and adolescents (self-report and primary caregiver-reports versions) was applied to a sample of 74 children with CP and their respective parents (totaling 222 participants), as well as instruments to measure functioning (i.e., GMFCS, MACS, CFCS and EDACS). The average age of the children was 12.50 (SD=4.07), with a higher number of boys (55.7 %).

OUTCOMES AND RESULTS

The lowest QoL levels were found in the Functional dimension in both assessments (Mchildren/adolescents=70.21, Mparents=58.14). For children, the highest rated dimension was Social Well-being (M=74.54), while for parents it was School (M=71.03). The degree of agreement between evaluators was low in almost all dimensions (ICC≤.40). More satisfactory predictive models were constructed from the evaluations carried out by parents, except in the case of the Access to Services dimension, with functioning measures being the main predictors of QoL levels.

CONCLUSIONS AND IMPLICATIONS

The CP-QOL in its two available versions is a useful and specific instrument for assessing QoL in children with CP in both research and professional fields.

Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review

BACKGROUND

Children and adolescents with neuromotor disorders need regular physical activity to maintain optimal health and functional independence throughout their development. To this end, reliable measures of physical activity are integral to both assessing habitual physical activity and testing the efficacy of the many interventions designed to increase physical activity in these children. Wearable accelerometers have been used for children with neuromotor disorders for decades; however, studies most often use disorder-specific cut points to categorize physical activity intensity, which lack generalizability to a free-living environment. No reviews of accelerometer data processing methods have discussed the novel use of machine learning techniques for monitoring physical activity in children with neuromotor disorders.

METHODS

In this narrative review, we discuss traditional measures of physical activity (including questionnaires and objective accelerometry measures), the limitations of standard analysis for accelerometry in this unique population, and the potential benefits of applying machine learning approaches. We also provide recommendations for using machine learning approaches to monitor physical activity.

CONCLUSIONS

While wearable accelerometers provided a much-needed method to quantify physical activity, standard cut point analyses have limitations in children with neuromotor disorders. Machine learning models are a more robust method of analyzing accelerometer data in pediatric neuromotor disorders and using these methods over disorder-specific cut points is likely to improve accuracy of classifying both type and intensity of physical activity. Notably, there remains a critical need for further development of classifiers for children with more severe motor impairments, preschool aged children, and children in hospital settings.