Abstract
This project is an exploratory study of levels of physical activity in boys with Duchenne Muscular Dystrophy collected in the natural environment using actigraphy. Data collected by the Actigraph GT9x is used to develop algorithms to categorize the distribution of energy expenditure throughout the day. This study presents inaccuracies with using typical pediatric actigraphy algorithms in children with disability and the need to develop custom algorithms that correlate to our population.
Primary Author and Speaker: Natalie Little
Additional Authors and Speakers: Margaret Feltman
Contributing Authors: Amy Hartman, Annmarie Kelleher, Roxanna Bendixen
Valid objective measurement is integral to increasing our understanding of physical activity and sedentary behaviors in children with disability [1]. Our study investigated the use of pediatric cut point algorithms in boys with Duchenne muscular dystrophy (DMD). Specific activity algorithms for children have been developed and are used to define levels of activity for unaffected pediatric populations [2]. Using pediatric cut point algorithms for children with disability, such as boys with DMD, is problematic because of their muscle weakness and inability to participate in most moderate to vigorous activities. Currently, there are no specific algorithms that appropriately categorize levels of daily activity for children with a progressive disorder, like DMD. Comparing activity in children with disability to typically developing children may not provide the information necessary for understanding changes based on treatment and intervention [3].
This study is a longitudinal descriptive study, and exploratory in nature. Boys with DMD between the ages of 4-17participated. Age-matched controls were used for comparisons. Subjects were recruited through the Muscular Dystrophy Association (MDA) and Parent Project Muscular Dystrophy (PPMD).
Community based activity data was collected through the ActiGraph GT9x monitor. Participants were instructed to wear the device 10 hours a day for 30 days. Actigraphy data was collected in 60-second counts, and translated to vector magnitudes (VM). VM is used to categorize movement into levels of activity intensity on a scale from sedentary to light to moderate to moderate/vigorous to very vigorous. Statistical data analyses were completed through SPSS 24.0.
Seven days of daily activity data for three boys (age 5, 7, and 12) with DMD and their age match controls’ data were analyzed. Using VM values from our control subjects, four categories of activity were created (sedentary, light, moderate, and moderate/vigorous). This gave us a picture of what typical daily activity looks like for age-matched children without physical disabilities. With this algorithm, control subjects demonstrated 24% of their daily activity in the sedentary range. In comparison, when data from the boys with DMD used the same algorithm, 51% of their daily activity was in the sedentary range with no activity in the moderate/vigorous range. Using the VM data of the boys with DMD, we created levels of activity that specifically correlate with this population. Using this specific DMD algorithm, boys with DMD demonstrated 28% of their daily activity in the sedentary range, with a wider range in all intensity levels throughout the day. These findings support the assertion that current pediatric algorithms based on unaffected children may not be appropriate to use for interpreting activity levels in children with disability, such as DMD.
Findings from this study assert that there is a need for pediatric algorithms that specifically correlate to children with physical disabilities to better represent their community based engagement. Our modified algorithm accounts for variances in movement and physical activity that boys with DMD experience, creating population appropriate ranges of activity levels. Comparing children with significant disabilities to unaffected children only tells us what we already know – they are significantly different in their activity levels. Better understanding of changes within and among the population may provide us with knowledge necessary to determine treatment and intervention effects. This study demonstrates the use of actigraphy data to create algorithms that better suit children who have physical disabilities and more accurately track their disease progression and treatment progress.
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