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.

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.

Objective To determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data. Methods Patients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO‐band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within‐patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics’ discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation. Results Participants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow‐up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow‐up, several had evidence of progression by the multisensor metrics. Interpretation Within a short follow‐up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases.