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

Abstract

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.

Keywords: Accelerometry; Machine learning; Neuromotor disorders; Pediatrics; Physical activity.

Fig. 1

Objective measures of physical activity. Objective measures can use closed-source devices (Apple Watch, Fitbit, etc.) or open-source accelerometers (Actigraph, Axivity, etc.). Raw data is combined into a vector sum and then activity is categorized by intensity, using cut points for activity counts (a clinical measure of the sum of activity per period of time) or by machine learning to categorize activity type or intensity using select features of the accelerometer data