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Biosensing in multiple sclerosis

Authors: Soren Jonzzon; Jennifer Graves; Jennifer Arjona; Andrew Yousef; Leena Suleiman;

Biosensing in multiple sclerosis

Abstract

The goal of using wearable biosensors in multiple sclerosis (MS) is to provide outcome metrics with higher sensitivity to deficits and better inter-test and inter-rater reliability than standard neurological exam bedside maneuvers. A wearable biosensor not only has the potential to enhance physical exams, but also offers the promise of remote evaluations of the patient either at home or with local non-specialist providers. Areas covered: We performed a structured literature review on the use of wearable biosensors in studies of multiple sclerosis. This included accelerometers, gyroscopes, eye-trackers, grip sensors, and multi-sensors. Expert commentary: Wearable sensors that are sensitive to change in function over time have great potential to serve as outcome metrics in clinical trials. Key features of generalizability are simplicity in the application of the device and delivery of data to the provider. Another important feature to establish is best sampling rate. Having too high of a sampling rate can lead to over-interpretation of noisy data On the other hand, a low sampling rate can result in an insensitive test thus missing subtle changes of clinical interest. Of most importance is to establish metrics derived from wearable devices that provide meaningful data in longitudinal studies.

Related Organizations
Keywords

Wearable Electronic Devices, Multiple Sclerosis, Accelerometry, Disease Progression, Humans, Monitoring, Ambulatory, Reproducibility of Results, Biosensing Techniques, Fitness Trackers

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    21
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Top 10%
Top 10%
Top 10%
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