Developing a Digital Solution for Remote Assessment in Multiple Sclerosis: From Concept to Software as a Medical Device

Walt, Anneke van der; Butzkueven, Helmut; Shin, Robert K.; Midaglia, Luciana; Capezzuto, Luca; Lindemann, Michael; Davies, Geraint; Butler, Lesley M.; Costantino, Cristina; Montalbán, Xavier

doi:10.3390/brainsci11091247

Cited by 21 publications

(19 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Floodlight is a smartphone app-based system for monitoring MS disease management and progression. This tool includes a collection of tasks designed to assess mood, information processing, hand motor function, gait, and balance [120,121]. Both the pinching test and the draw a shape test included in Floodlight correlate significantly with the 9HPT [122].…”

Section: Electronic and Home-based Monitoring Systemsmentioning

confidence: 99%

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

et al. 2022

Self Cite

View full text Add to dashboard Cite

Multiple sclerosis (MS) is a progressive disease that often affects the cerebellum. It is characterised by demyelination, inflammation, and neurodegeneration within the central nervous system. Damage to the cerebellum in MS is associated with increased disability and decreased quality of life. Symptoms include gait and balance problems, motor speech disorder, upper limb dysfunction, and oculomotor difficulties. Monitoring symptoms is crucial for effective management of MS. A combination of clinical, neuroimaging, and task-based measures is generally used to diagnose and monitor MS. This paper reviews the present and new tools used by clinicians and researchers to assess cerebellar impairment in people with MS (pwMS). It also describes recent advances in digital and home-based monitoring for people with MS.

show abstract

Section: Electronic and Home-based Monitoring Systemsmentioning

confidence: 99%

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…We might learn what symptoms MS subjects are likely to develop, why they are experiencing the ones they have, and what treatments are more suited for their current disease burden. A few recent studies have demonstrated that mobile-based multiple sclerosis apps can reliably measure functional impairments of MS 11,28 .…”

Section: Introductionmentioning

confidence: 99%

Performance of Machine Learning Models for Predicting High-Severity Symptoms in Multiple Sclerosis

Roy

Mincu

Proleev

et al. 2023

Preprint

View full text Add to dashboard Cite

Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging (MRI), clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile technology enables continual collection of data and can pave the path for predicting complex aspects of MS such as symptoms and disease courses. To this end, we conducted a first-of-its-kind observational study called MS Mosaic. First, we developed and publicly launched a mobile app for collecting longitudinal data from MS subjects in the United States. Second, we ran the study across 3 years in order to capture complex patterns for this slow progressing disease. Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps) three months in advance.

show abstract

“…Digital tools have been investigated for remote monitoring, but are expensive and can only be used for research purposes. [5][6][7][8] Thus, we have investigated the feasibility of monitoring gait in people with MS (PwMS) using Google Maps Timeline (GMT), an open-access digital tool embedded within the Google Maps smartphone application providing information about walking distance and time, thus being a promising proxy for walking abilities in real-life settings.…”

Section: Introductionmentioning

confidence: 99%

Google Maps Timeline: An open-access digital tool to monitor walking abilities in people with multiple sclerosis

Caliendo,

Puca,

Lavorgna

et al. 2023

Multiple Sclerosis Journal - Experimental, Translational and Cl

View full text Add to dashboard Cite

Introduction Gait impairment is common in multiple sclerosis (MS), but difficult to evaluate in clinical practice. In this proof-of-concept observational study, we compared walking ability recorded by Google Maps Timeline to conventional clinical measures in people with MS. Methods We used open-access Google Maps Timeline to record the total number of days with walking activity, walking distance, walking time, and walking speed. Each Google Maps Timeline variable was included in a different stepwise linear regression model including all conventional clinical variables. Results We included nine people with MS (age 43.1 ± 6.6 years; females 55.6%; disease duration 12.7 ± 3.1 years; median Expanded Disability Status Scale 3.0 (range 1.0–5.5)). Higher percentage of days with recorded walking was associated with lower Fatigue Severity Scale ( p = 0.01), and higher MS Walking Scale ( p = 0.04). Longer average daily walking distance was associated with shorter Timed-25 Foot Walking Test ( p = 0.02), lower Expanded Disability Status Scale ( p = 0.01), and higher Euro-Quality of Life ( p = 0.04). Longer average daily walking time was associated with shorter Timed-25 Foot Walking Test ( p = 0.03). Higher walking speed was associated with lower Fatigue Severity Scale ( p = 0.04). Conclusion Google Maps Timeline parameters provide actual estimates of daily walking activities in MS.

show abstract

Developing a Digital Solution for Remote Assessment in Multiple Sclerosis: From Concept to Software as a Medical Device

Cited by 21 publications

References 50 publications

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

An Update on the Measurement of Motor Cerebellar Dysfunction in Multiple Sclerosis

Performance of Machine Learning Models for Predicting High-Severity Symptoms in Multiple Sclerosis

Google Maps Timeline: An open-access digital tool to monitor walking abilities in people with multiple sclerosis

Contact Info

Product

Resources

About