AUTHOR=Zhai Yuyang , Nasseri Navina , Pöttgen Jana , Gezhelbash Eghbal , Heesen Christoph , Stellmann Jan-Patrick 

TITLE=Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

JOURNAL=Frontiers in Neurology

VOLUME=11

YEAR=2020

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.00688

DOI=10.3389/fneur.2020.00688

ISSN=1664-2295

ABSTRACT=<p><bold>Background:</bold> Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative.</p><p><bold>Objective:</bold> To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls.</p><p><bold>Methods:</bold> Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups.</p><p><bold>Results:</bold> Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (<italic>r</italic> = 0.43, <italic>p</italic> &lt; 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = −0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (<italic>p</italic> &lt; 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all <italic>p</italic> &lt; 0.001).</p><p><bold>Conclusions:</bold> Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.</p>