Smart watch, smarter EDSS: Improving disability assessment in multiple sclerosis clinical practice

Costa, Gloria Dalla; Radaelli, Marta; Maida, Simona; Sangalli, Francesca; Colombo, Bruno; Moiola, Lucia; Comı, Gıancarlo; Martinelli, Vittorio

doi:10.1016/j.jns.2017.10.043

Cited by 29 publications

(17 citation statements)

References 9 publications

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“…standing, sitting, laying down, sedentary time, and hours of sleep), but cannot determine the quality of movement, joint kinematics, or motor impairments. 15 Studies have shown that the steps/day parameter is a reliable and valid measure of free-living walking behavior in pwMS. 16 The reliability and accuracy of accelerometer-based step counts have been validated showing an overall good accuracy compared to true step count, particularly in patients with mild disability.…”

Section: Digital Technology and Msmentioning

confidence: 99%

e-Health and multiple sclerosis: An update

Lavorgna

Brigo

Moccia³

et al. 2018

Mult Scler

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e-Health (or digital healthcare) is becoming increasingly relevant in multiple sclerosis (MS) clinical management. We aim to review and discuss current status and future perspective of e-health in people with multiple sclerosis (pwMS). The first part of this review describes how information on MS can be conveyed through the Web and digital media. The second part illustrates recent advances in digital technology that can improve clinical management and in motor and cognitive rehabilitation of pwMS. Finally, this review advocates future development of the "digital case manager" as a new figure to coordinate clinical management and care of pwMS. The digital revolution is changing the medical approach to MS in terms of information conveying and sharing, rehabilitation, and healthcare management.

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Section: Digital Technology and Msmentioning

confidence: 99%

e-Health and multiple sclerosis: An update

Lavorgna

Brigo

Moccia³

et al. 2018

Mult Scler

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“…The Extended Disability Status Scale (EDSS) is an accepted standard of disability measurement in MS and relies in its middle range mainly on walking abilities in the range between 20 and 500 m (10). However, the scale suffers for its increased variability for longer walking distances and other factors like fatigue, patient's mood, and the time the test was performed (11). EDSS also has limitations to measure small but clinically meaningful changes in ambulation, and it fails to capture the performance fluctuation over time in the natural environment (12).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Background: 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. Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls. Methods: 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. Results: 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 (r = 0.43, p < 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 (p < 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 p < 0.001). Zhai et al. Smartphone Accelerometry in Multiple Sclerosis Conclusions: 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.

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“…With dozens of mHealth apps available to manage MS on all major smartphone platforms, smartphone apps have recently emerged as a readily accessible alternative to non-invasively track symptoms of MS in the wild [37], [38]. Prior studies on the use mHealth in MS have, for example, evaluated telemedicine-enabled remote EDSS scoring [39], measurement devices for estimating walking ability [40] and fatigue [41], and machine learning for assessing gait impairment in MS [42]. Epidemiologically, demographic factors, such as age and sex, have been shown to be predictive of MS [43].…”

Section: A Monitoring and Diagnosis Of Msmentioning

confidence: 99%

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

Schwab

Karlen

2021

IEEE J. Biomed. Health Inform.

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Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.

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Smart watch, smarter EDSS: Improving disability assessment in multiple sclerosis clinical practice

Cited by 29 publications

References 9 publications

e-Health and multiple sclerosis: An update

e-Health and multiple sclerosis: An update

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

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

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