Using real-world accelerometry-derived diurnal patterns of physical activity to evaluate disability in multiple sclerosis.

Multiple sclerosis (MS) is a leading cause of disability affecting people typically between the ages of 20 and 50 years, with negative impacts on their quality of life. Although medications may reduce the risk of relapses for those with relapsing remitting MS (RRMS), there are limited treatments to slow disability accrued from the progressive subtypes of MS. Measuring disability accrual relies on self-report or the use of clinical measures. Clinical measures for MS suffer from limitations in their ability to detect changes in function that relate to disease progression or intervention responsiveness. For example, the Expanded Disability Status Scale is a widely accepted raterbased categoricalmeasure that provides an overview of disability in people with MS; however, it has limited reliability and sensitivity for detecting small meaningful changes in motor function. 3,4 Walk tests such as the Timed 25-foot Walk and Timed-Up and Go are objective and reliable but only provide quantitative information about a moment in time, limiting the capture of daily (or even hourly) performance fluctuations that may provide an early indication of progression. Self-reported outcomes offer valuable personal perspectives but rely on memory recall, which could be confounded with cognitive changes or depression and anxiety. The heterogeneity of impairments in MS makes it challenging to find an objective outcome measure that reflects a person’s overall disability including daily fluctuations, that can be implemented in a standard way and demonstrates ecological validity. New biophysical markers that can be tailored to a person’s disability, applied in a person’s natural environment, and are simple to apply are greatly needed. Recent studies have turned to the use of motion sensors, such as accelerometers, aiming to develop a new gold standard for quantifying walking mobility. The wearable accelerometer is a non-invasive, objective, and inexpensive technology that records human movement in realtime in a real-world context. Accelerometry data are simple to acquire, making it possible to objectively study physical activity in awide range of individuals at an unprecedented temporal level (i.e., at minute level) in a person’s free-living environment. However, the methods used to analyze accelerometry data often fall well short of the richness of the accelerometry data. Current analysis methods largely rely on aggregated data summaries of either activity intensity or duration of active times defined for activity counts above a certain threshold; the data are often summarized in daily totals which leads to a loss of detail about diurnal distribution of physical activity over 24-h. Use of aggregated data removes the ability to tailor the accelerometry data to a person’s diurnal profile or to use it as a guide for interventions, ultimately expanding its clinical usefulness. To better understand how disease progression affects physical activity, it is possible to evaluate data variations through a 24-h period at refined resolutions rather than summing activity counts over a whole day. Use of functional data analysis tools, allows for study of the entire activity profile, capturing unique information from each accelerometry dataset. Over the past decade,