Novel MS vital sign: multi-sensor captures upper and lower limb dysfunction

Ann Clin Transl Neurol. 2020 Mar;7(3):288-295. doi: 10.1002/acn3.50988. Epub 2020 Feb 26.

Abstract

Objective: To create a novel neurological vital sign and reliably capture MS-related limb disability in less than 5 min.

Methods: Consecutive patients meeting the 2010 MS diagnostic criteria and healthy controls were offered enrollment. Participants completed finger and foot taps wearing the MYO-band© (accelerometer, gyroscope, and surface electromyogram sensors). Signal processing was performed to extract spatiotemporal features from raw sensor data. Intraclass correlation coefficients (ICC) assessed intertest reproducibility. Spearman correlation and multivariable regression methods compared extracted features to physician- and patient-reported disability outcomes. Partial least squares regression identified the most informative extracted textural features.

Results: Baseline data for 117 participants with MS (EDSS 1.0-7.0) and 30 healthy controls were analyzed. ICCs for final selected features ranged from 0.80 to 0.87. Time-based features distinguished cases from controls (P = 0.002). The most informative combination of extracted features from all three sensors strongly correlated with physician EDSS (finger taps rs = 0.77, P < 0.0001; foot taps rs = 0.82, P < 0.0001) and had equally strong associations with patient-reported outcomes (WHODAS, finger taps rs = 0.82, P < 0.0001; foot taps rs = 0.82, P < 0.0001). Associations remained with multivariable modeling adjusted for age and sex.

Conclusions: Extracted features from the multi-sensor demonstrate striking correlations with gold standard outcomes. Ideal for future generalizability, the assessments take only a few minutes, can be performed by nonclinical personnel, and wearing the band is nondisruptive to routine practice. This novel paradigm holds promise as a new neurological vital sign.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Accelerometry
  • Adult
  • Cross-Sectional Studies
  • Diagnostic Techniques, Neurological / instrumentation*
  • Diagnostic Techniques, Neurological / standards
  • Electromyography
  • Female
  • Fingers / physiopathology*
  • Foot / physiopathology*
  • Humans
  • Male
  • Middle Aged
  • Motor Activity
  • Multiple Sclerosis / diagnosis*
  • Multiple Sclerosis / physiopathology*
  • Severity of Illness Index
  • Signal Processing, Computer-Assisted
  • Treatment Outcome
  • Vital Signs / physiology*
  • Wearable Electronic Devices* / standards

Grants and funding

This work was funded by UCSF Pilot Program grant ; Investigator Initiated Award, Genentech grant .