• Gary Birnbaum, MD

Wearing one's MS

Paper #1: Wearable technologies to measure clinical outcomes in

multiple sclerosis: A scoping review

Alexander, S., Peryer, G., Gray, E., Barkhof, F. and Chataway, J.

Mult Scler

Epub Date: 2020/08/05

https://www.ncbi.nlm.nih.gov/pubmed/32749928


Paper #2: Biosensor vital sign detects multiple sclerosis progression

Krysko, K. M., Akhbardeh, A., Arjona, J., Nourbakhsh, B.,

Waubant, E., Antoine Gourraud, P. and Graves, J. S.

Ann Clin Transl Neurol; Epub Date: 2020/11/20

https://www.ncbi.nlm.nih.gov/pubmed/33211403


Paper #3: A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real‑world gait quality and physical activity

Shirley Shema‑Shiratzky, Inbar Hillel, Anat Mirelman et al

Journal of Neurology (2020) 267:1912–1921

https://www.ncbi.nlm.nih.gov/pubmed/32166481

Paper #4: Wearables and Deep Learning

Classify Fall Risk from Gait in Multiple Sclerosis

Brett M. Meyer, Student Member, IEEE, Lindsey J. Tulipani, Reed D. Gurchiek, Student Member, IEEE, et al

IEEE J Biomed Health Inform. 2020 Sep 18, 2020

https://www.ncbi.nlm.nih.gov/pubmed/32946403



Bottom Line:

The corona virus pandemic has dramatically changed neurologic care. The risks of providing “face-to-face” visits have forced health care providers to evaluate and treat persons with neurologic illness remotely with “telemedicine.” This is far from ideal, especially in evaluating the neurologic status of persons with MS. As a result, there has been a great impetus to the use of wearable devices such as smart-watches and movement monitors to utilize such devices to provide neurologic status information remotely and to provide unique aspects of neurologic function not routinely available during clinic visits. The above papers describe how information generated from such wearable devices can, and has been, used in exciting new ways to monitor neurologic status, aiding in treatment decisions and potentially expanding and improving the care of persons with MS in these challenging times.


Key Points:

1. Paper #1 provides an overview of current wearable devices and their potential use in monitoring the status of persons with MS. Thirty-five wearable devices were evaluated. Most measured lower limb function using activity monitors, accelerometers and gyroscopes to record step count, step length, and gait characteristics.

2. Applications allowing personal reporting of fatigue, quality of life, and cognition were also reviewed. Newer applications for devices such as smartphones and smartwatches could measure multiple functions, such as gait, cognition, and upper limb function.

3. The authors reviewed the benefits of wearable devices (ease of use, available for prolonged monitoring, ease of transmission of data to caregivers). There also are limitations (costs, upgrades of hardware and software which could change user and examiner interfaces, lack of adherence to wearing the devices, changing regulations regarding accessing data from the devices, and increasing difficulties with use of the devices due to progression of disability). All in all, the authors predicted progressively increasing use of wearable devices to assess neurologic status in persons with MS.

4. Paper #2 addresses a very important question. When do persons with relapsing forms of multiple sclerosis transition to a progressive form of MS, and possibly needing a different disease-modifying therapy?

5. The authors studied 53 individuals with relapsing forms of multiple sclerosis and 15 persons with progressive multiple sclerosis, both secondary progressive MS and primary progressive multiple sclerosis.

6. Participants were fitted with a device that monitored and recorded the acceleration of movements in arms and legs, the positions of the limbs with a gyroscope, and the contractions of limb muscles (EMG).

7. Participants then did standardized finger and foot tapping protocols. Detailed data were collected and analyzed for evidence of progression occurring over the relatively short time of a mean of 10 months.

8. Results were then correlated with changes in standard scales of neurologic function, the EDSS and FSS (Expanded Disability Status Scale and Functional Systems Scale).

9. The results showed that it was possible to separate persons with MS into different subtypes (stable relapsing disease, relapsing disease with progression, and progressive disease) based on whether there were changes over time in the limb movements detected with the electronic devices.

10. As expected, persons with progressive disease showed more worsening than persons with relapsing disease. However, most importantly, the electronic data were able to detect limb worsening in both progressive and relapsing persons even though there were no changes on neurologic exam (EDSS and FSS scores).

11. As noted in my previous posting, the transition from relapsing to progressive forms of MS can be very subtle, with worsening occurring in the absence of relapses. As medications for progressive forms of MS become available documenting such “silent” disease progression with electronic devices will become increasingly important.

12. Paper # 3 assessed the ability of wearable devices to monitor persons with MS while in their communities. The authors then compared these results to observations done in the clinic. Normal individuals were used as controls. Previous work with other neurologic illnesses showed there were substantive differences in how persons performed in their communities compared with how they tested in the clinic.

13. Forty-four persons with relapsing forms of multiple sclerosis were studied. Participants were first studied in the clinic for gait, including dual-task walking (walking and simultaneously performing a test of cognitive function), levels of fatigue, and cognitive functioning. They then wore an accelerometer on their backs for one week during their regular activities in their communities.

14. As expected, persons with MS walked less and more slowly than healthy controls, with more irregularity of their gait. Also as expected, increased disability and fatigue correlated with less time spent standing or sitting and more time resting. Gait speed observed in the clinic was faster than walking speed during community walking for persons with MS but was the same in clinic and the community for dual-task walking.

15. This interesting paper provides evidence that the data obtained during daily communal activities in many ways closely approximates data obtained in clinic but in some ways is even more detailed and informative regarding the daily functions of persons with MS.

16. Paper #4 addresses a major issue in the care of persons with MS, that of identifying individuals at risk for falling. Risks of falling increase with increasing disability. More than half of falls result in significant injury and efforts to reduce falls are usually only made after a fall has occurred.

17. The authors of Paper #4 noted that gait and trunk movements of persons with MS who fall are different from those of persons with MS who don’t fall. The authors then describe how the data derived from a wearable device that measures gait and truncal parameters could be analyzed by a “deep learning” artificial intelligence program (AI) to predict an individual’s risk of falling.

18. Thirty-seven persons with MS were studied, 18 of whom had previously fallen. Sensors were attached to chest and thighs and analyzed by their AI program.

19. Using data from just one minute of walking with as few as five strides the AI program was able to identify persons with MS that had fallen with greater accuracy than did results from a neurologic exam or from observations of these individuals. The rapid and accurate identification of persons at risk for falling should allow health care providers to promptly intervene with treatments and appropriate assistive devices prior to falling, hopefully reducing falls and the associated risks of injury.

Discussion:

The coronavirus pandemic has radically changed patient care. There are fewer clinic visits and health care providers are using “telemedicine” consultations as part of their routine practice. Such adaptations are successful for the management of many illnesses but are especially challenging in providing care for persons with MS where a “hands-on” neurologic exam is an important part of the clinical evaluation. As described in the above papers, data from wearable devices can be transmitted remotely to health care providers. Some of the information will be similar to that noted during clinic visits, but new techniques and applications, as noted above, can provide unique data on neurologic status, data that will allow health care providers to address issues not well-managed previously. Paper #2 describes how using data from changes in gait can allow health care providers to determine gradual, non-relapse related disease progression in persons with relapsing forms of multiple sclerosis. Identifying such “silent” progression long before there are changes on neurologic exam, should facilitate discussion on the need to change to another disease-modifying therapy if appropriate. Paper #3 discusses the successful use of a wearable device to determine daily activities of persons with MS in their communities. Such data can provide health care providers with a much more comprehensive picture of a person’s daily functioning in their community. Paper #4 is especially important in that it describes using “deep learning” artificial intelligence to analyze an individual’s gait and determine their risk of falling. This information will allow health care providers to pre-emptively advise and treat persons with MS prior to falling and possibly avoid sustaining injury.

The potential of wearable devices to aid in the management of persons with MS continues to grow. For example, devices, such as the Apple Watch® Series 6, in addition to having a fall detection monitor and a sleep monitor, has the capacity to monitor blood oxygen levels. This is especially important for persons with MS since impaired sleep, in particular sleep apnea, occurs in up to 50% of persons with MS. Sleep disorders are often not diagnosed or treated. Wearing such a device may allow users to detect not only the presence of frequent arousals from sleep, but low levels of oxygen during sleep, suggesting possible sleep apnea, and encouraging further evaluation.

While the coronavirus pandemic has caused major disruptions in the lives of millions of individuals, it also has greatly spurred the development of new techniques and approaches such as the use of wearable devices to provide information remotely, information often not routinely available during routine clinic visits, thus providing the potential to further improve the health of persons with MS.

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