Biosensing in multiple sclerosis

Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol

(1) Objective: We explore the predictive power of a novel stream of patient data, combining wearable devices and patient reported outcomes (PROs), using an AI-first approach to classify the health status of Parkinson's disease (PD), multiple sclerosis (MS) and stroke patients (collectively named PMSS). (2) Background: Recent studies acknowledge the burden of neurological disorders on patients and on the healthcare systems managing them. To address this, effort is invested in the digital transformation of health provisioning for PMSS patients. (3) Methods: We introduce the data collection journey within the ALAMEDA project, which continuously collects PRO data for a year through mobile applications and supplements them with data from minimally intrusive wearable devices (accelerometer bracelet, IMU sensor belt, ground force measuring insoles, and sleep mattress) worn for 1-2 weeks at each milestone. We present the data collection schedule and its feasibility, the mapping of medical predictor variables to wearable device capabilities and mobile application functionality. (4) Results: A novel combination of wearable devices and smartphone applications required for the desired analysis of motor, sleep, emotional and quality-of-life outcomes is introduced. AI-first analysis methods are presented that aim to uncover the prediction capability of diverse longitudinal and cross-sectional setups (in terms of standard medical test targets). Mobile application development and usage schedule facilitates the retention of patient engagement and compliance with the study protocol.

A Connected World: System-Level Support Through Biosensors

The goal of protecting the health of future generations is a blueprint for future biosensor design. Systems-level decision support requires that biosensors provide meaningful service to society. In this review, we summarize recent developments in cyber physical systems and biosensors connected with decision support. We identify key processes and practices that may guide the establishment of connections between user needs and biosensor engineering using an informatics approach. We call for data science and decision science to be formally connected with sensor science for understanding system complexity and realizing the ambition of biosensors-as-a-service. This review calls for a focus on quality of service early in the design process as a means to improve the meaningful value of a given biosensor. We close by noting that technology development, including biosensors and decision support systems, is a cautionary tale. The economics of scale govern the success, or failure, of any biosensor system.

Feasibility of a smartphone app to monitor patient reported outcomes in multiple sclerosis: The haMSter interventional trial

Background

Monitoring of patient outcomes in multiple sclerosis (MS) is fundamental for individualized treatment decisions. So far, these decisions have been motivated by conventional outcomes, i.e., relapses or clinical disability supported by radiological disease activity. Complementing this concept, patient reported outcomes (PROs) assess individual health-related quality of life, among other constructs. Their inclusion in clinical routine, however, has been challenging as assessing them requires resources of time and personnel.

Objective

This interventional feasibility study investigated the haMSter app, a mobile health solution for remote and longitudinal monitoring of PROs in a sample of people with MS (pwMS).

Methods

The core feature of haMSter is the provision of three PRO questionnaires relevant to MS (anxiety/depression, MS-related quality of life, and fatigue) that patients can fill out once a month. For this feasibility trial, we offered 50 volunteers to use the haMSter app over six months and to take part in a haMSter study visit. This consultation concluded the study and participants had the opportunity to discuss their graphically plotted PRO results with their treating physician.

Results

The main outcome was overall patient adherence to monthly completion of the PRO questionnaires, which remained high up to 4 months (98%) and dropped over time (months 5: 83% and 6: 66%). Exploratory outcomes included patient satisfaction as estimated on the Telemedicine Perception Questionnaire (TMPQ, 17–85 points). The mean TMPQ score was 64 (95%CI: 62–66) points, indicating a high degree of approval. Ancillary tests included subgroup analyses of participants with particularly high or low satisfaction and upper extremity disability as a potential obstacle to utility or acceptance. We found no distinct characteristics separating participants with high or low satisfaction.

Conclusions

In this first feasibility trial, the haMSter app for longitudinal PRO monitoring was well received in terms of adherence and satisfaction. ClinicalTrials.gov identifier: NCT04555863.

Smartphone-derived keystroke dynamics are sensitive to relevant changes in multiple sclerosis

Background

To investigate smartphone keystroke dynamics (KD), derived from regular typing, on sensitivity to relevant change in disease activity, fatigue, and clinical disability in multiple sclerosis (MS).

Methods

Preplanned interim analysis of a cohort study with 102 MS patients assessed at baseline and 3‐month follow‐up for gadolinium‐enhancing lesions on magnetic resonance imaging, relapses, fatigue and clinical disability outcomes. Keyboard interactions were unobtrusively collected during typing using the Neurokeys App. From these interactions 15 keystroke features were derived and aggregated using 16 summary and time series statistics. Responsiveness of KD to clinical anchor‐based change was assessed by calculating the area under the receiver operating characteristic curve (AUC). The optimal cut‐point was used to determine the minimal clinically important difference (MCID) and compared to the smallest real change (SRC). Commonly used clinical measures were analyzed for comparison.

Results

A total of 94 patients completed the follow‐up. The five best performing keystroke features had AUC‐values in the range 0.72–0.78 for change in gadolinium‐enhancing lesions, 0.67–0.70 for the Checklist Individual Strength Fatigue subscale, 0.66–0.79 for the Expanded Disability Status Scale, 0.69–0.73 for the Ambulation Functional System, and 0.72–0.75 for Arm function in MS Questionnaire. The MCID of these features exceeded the SRC on group level. KD had higher AUC‐values than comparative clinical measures for the study outcomes, aside from ambulatory function.

Conclusions

Keystroke dynamics demonstrated good responsiveness to changes in disease activity, fatigue, and clinical disability in MS, and detected important change beyond measurement error on group level. Responsiveness of KD was better than commonly used clinical measures.

Telehealth in Multiple Sclerosis Clinical Care and Research

Purpose of Review

The COVID-19 pandemic has provided us with a unique opportunity to experiment with telehealth and evaluate its benefits and limitations. This review discusses the impact of telehealth on multiple sclerosis (MS) care and research in adults and children.

Recent Findings

Telehealth visits for MS patients have been shown to reduce missed workdays and costs for patients. Brief telephone-based counseling may be associated with better adherence to disease-modifying therapy, although results of multiple home-based tele-rehabilitation for people with MS have been equivocal. Overall, patients and providers have reported high levels of satisfactions with telehealth. Several remote disability measures and numerous other technological tools have emerged for use in remote MS research and care. Major challenges of telehealth include limitations to performing a complete neurologic exam and disparities in access to telehealth amongst vulnerable populations with limited access to virtual platforms.

Summary

Following the rapid expansion of telehealth during the pandemic, it is highly likely that we will continue to embrace the benefits of this valuable tool. Future directions for improving telehealth should include more evidence-based research on the diagnostic accuracy in neuroimmunology and reducing disparities in the access to telehealth.

What the Tech? The Management of Neurological Dysfunction Through the Use of Digital Technology

Worldwide, it is estimated that millions of individuals suffer from a neurological disorder which can be the result of head injuries, ischaemic events such as a stroke, or neurodegenerative disorders such as Parkinson's disease (PD) and multiple sclerosis (MS). Problems with mobility and hemiparesis are common for these patients, making daily life, social factors and independence heavily affected. Current therapies aimed at improving such conditions are often tedious in nature, with patients often losing vital motivation and positive outlook towards their rehabilitation. The interest in the use of digital technology in neuro-rehabilitation has skyrocketed in the past decade. To gain insight, a systematic review of the literature in the field was conducting following the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines for three categories: stroke, Parkinson's disease and multiple sclerosis. It was found that the majority of the literature (84%) was in favour of the use of digital technologies in the management of neurological dysfunction; with some papers taking a "neutral" or "against" standpoint. It was found that the use of technologies such as virtual reality (VR), robotics, wearable sensors and telehealth was highly accepted by patients, helped to improve function, reduced anxiety and make therapy more accessible to patients living in more remote areas. The most successful therapies were those that used a combination of conventional therapies and new digital technologies.

Biosensor vital sign detects multiple sclerosis progression

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.

Smartphone Accelerometry: A Smart and Reliable Measurement of Real-Life Physical Activity in Multiple Sclerosis and Healthy Individuals

Background: Mobility impairment is common in persons with multiple sclerosis (pwMS) and can be assessed with clinical tests and surveys that have restricted ecological validity. Commercial research-based accelerometers are considered to be more valuable as they measure real-life mobility. Smartphone accelerometry might be an easily accessible alternative.

Objective: To explore smartphone accelerometry in comparison to clinical tests, surveys, and a wrist-worn ActiGraph in pwMS and controls.

Methods: Sixty-seven pwMS and 70 matched controls underwent mobility tests and surveys. Real-life data were collected with a smartphone and an ActiGraph over 7 days. We explored different smartphone metrics in a technical validation course and computed afterward correlation between ActiGraph (steps per minute), smartphone accelerometry (variance of vector magnitude), clinical tests, and surveys. We also determined the ability to separate between patients and controls as well as between different disability groups.

Results: Based on the technical validation, we found the variance of the vector magnitude as a reliable estimate to discriminate wear time and no wear-time of the smartphone. Due to a further association with different activity levels, it was selected for real-life analyses. In the cross-sectional study, ActiGraph correlated moderately (r = 0.43, p < 0.05) with the smartphone but less with clinical tests (rho between |0.211| and |0.337|). Smartphone data showed stronger correlations with age (rho = −0.487) and clinical tests (rho between |0.565| and |0.605|). ActiGraph only differed between pwMS and controls (p < 0.001) but not between disability groups. At the same time, the smartphone showed differences between pwMS and controls, between RRMS and PP-/SPMS, and between participants with/without ambulatory impairment (all p < 0.001).

Conclusions: Smartphone accelerometry provides better estimates of mobility and disability than a wrist-worn standard accelerometer in a free-living context for both controls and pwMS. Given the fact that no additional device is needed, smartphone accelerometry might be a convenient outcome of real-life ambulation in healthy individuals and chronic diseases such as MS.

Beyond center-based testing: Understanding and improving functioning with wearable technology in MS

Wearable sensors are designed to be worn on the body or embedded into portable devices (e.g. smartphones and smartwatches), allowing continuous patient-based monitoring, objective outcomes measuring, and feedback delivering on daily-life activities. Within the medicine domain, there has been a rapid increase in the development, testing, and use of wearable technologies especially in the context of neurological diseases. Although wearables represent promising tools also in multiple sclerosis (MS), the research on their application in MS is still ongoing, and further studies are required to assess their reliability and accuracy to monitor body functions and disability in people with MS (pwMS). Here, we provided a comprehensive overview of the opportunities, potential challenges, and limitations of the wearable technology use in MS. In particular, we classified previous findings within this field into macro-categories, considered crucial for disease management: assessment, monitoring, intervention, advice, and education. Given the increasing pivotal role played by wearables, current literature suggests that for pwMS, the time is right to shift from a center-based traditional therapeutic paradigm toward a personalized patient-based disease self-management. On this way, we present two ongoing initiatives aimed at implementing a continuous monitoring of pwMS and, consequently, providing timely and appropriate care interventions.

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

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 r_s = 0.77, P < 0.0001; foot taps r_s = 0.82, P < 0.0001) and had equally strong associations with patient‐reported outcomes (WHODAS, finger taps r_s = 0.82, P < 0.0001; foot taps r_s = 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.

Biosensors to monitor MS activity

Advances in wearable and wireless biosensing technology pave the way for a brave new world of novel multiple sclerosis (MS) outcome measures. Our current tools for examining patients date back to the 19th century and while invaluable to the neurologist invite accompaniment from these new technologies and artificial intelligence (AI) analytical methods. While the most common biosensor tool used in MS publications to date is the accelerometer, the landscape is changing quickly with multi-sensor applications, electrodermal sensors, and wireless radiofrequency waves. Some caution is warranted to ensure novel outcomes have clear clinical relevance and stand-up to the rigors of reliability, reproducibility, and precision, but the ultimate implementation of biosensing in the MS clinical setting is inevitable.

The virtual physical exam in the 21st century

The modern Western medical encounter follows a strict framework that weaves subjective and objective components into a unifying diagnosis. As health care changes to incorporate new technology, such as virtual health care, the components that lead to diagnosis must likewise evolve. The virtual physical exam has limitations compared with the traditional exam. Despite this limitation, every year more patients are seen virtually with high satisfaction. Data have shown that supplementary real-time patient-provider video telemedicine increases access and extends established patient-physician relationships which will likely fuel increased telemedicine adoption even further. However, to date, there are limited data regarding the validity of the virtual examination compared with the traditional physical exam. In this paper, we review the use of developing technology related to the virtual physical exam.

The Role of Wearable Devices in Multiple Sclerosis

Multiple sclerosis (MS) is the most common neurological disorder in young adults. The prevalence of walking impairment in people with MS (pwMS) is estimated between 41% and 75%. To evaluate the walking capacity in pwMS, the patient reported outcomes (PROs) and performance-based tests (i.e., the 2-minute walk test, the 6-minute walk test, the Timed 25-Foot Walk Test, the Timed Up and Go Test, and the Six Spot Step Test) could be used. However, some studies point out that the results of both performance-based tests and objective measures (i.e., by accelerometer) could not reflect patient reports of walking performance and impact of MS on daily life. This review analyses different motion sensors embedded in smartphones and motion wearable device (MWD) that can be useful to measure free-living walking behavior, to evaluate falls, fatigue, sedentary lifestyle, exercise, and quality of sleep in everyday life of pwMS. Caveats and limitations of MWD such as variable accuracy, user adherence, power consumption and recharging, noise susceptibility, and data management are discussed as well.

e-Health and multiple sclerosis: An update

e-Health (or digital healthcare) is becoming increasingly relevant in multiple sclerosis (MS) clinical management. We aim to review and discuss current status and future perspective of e-health in people with multiple sclerosis (pwMS). The first part of this review describes how information on MS can be conveyed through the Web and digital media. The second part illustrates recent advances in digital technology that can improve clinical management and in motor and cognitive rehabilitation of pwMS. Finally, this review advocates future development of the "digital case manager" as a new figure to coordinate clinical management and care of pwMS. The digital revolution is changing the medical approach to MS in terms of information conveying and sharing, rehabilitation, and healthcare management.

Free-Living Physical Activity Monitoring in Adult US Patients with Multiple Sclerosis Using a Consumer Wearable Device

Introduction

Wearable devices have been used to characterize physical activity in multiple sclerosis (MS). The objectives of this study were to advance the literature on the utility of free-living physical activity tracking from secondary analyses of a pilot study in MS patients.

Method

The original observational study was conducted in participants with MS at PatientsLikeMe (PatientsLikeMe (www.PatientsLikeMe.com), an online network of patients with chronic diseases. Participants completed a baseline self-assessment, and received a Fitbit One^TM wearable device with instructions to upload data. Eligible participants (1) self-reported MS, (2) logged on to the PatientsLikeMe website 90 days prior to enrollment, and (3) consented to participate electronically. Participants (1) < 18 years, (2) living outside the United States, and (3) requiring wheelchair assistance for most daily activities were excluded. The secondary analyses were limited to participants with complete data on MS type, disease duration, and Multiple Sclerosis Rating Scale (MSRS) and at least 7 days of wearable data. Step count was used as a measure of physical activity.

Results

The analysis cohort of 114 participants uploaded a mean of 20.1 days of wearable data over the 23-day study (87% adherence); participants averaged 4,393 steps per day. The mean age of participants was 52 years, predominantly female (75%), relapsing-remitting type (79%), with mean disease duration of 16 years. Mean MSRS score within 30-day of baseline was 32; 72% reported mild-moderate walking disability. The reliability of step count measured by intraclass correlation was 0.55 for a single day, ≥0.7 for 2-day average, and ≥0.9 for 7-day average. After controlling for covariates, self-reported disease severity (MSRS quartile) was an independent predictor of step count (p < 0.001). Least square means (LS means) for participants that were least disabled (lowest quartile) was 5,937 steps, which was significantly higher than participants in the second, third, and fourth quartiles (4,570, 3,490, and 3,272, respectively). Similarly, LS means of participants with no ambulatory disability (measured by MSRS walk component) was 6,931 steps, significantly higher than participants with greater disability (4,743, 4,394, 2,727 steps for symptomatic, mild, and moderate disability, respectively, p < 0.001).

Discussion

Using an interactive platform, this study captured free-living mobility data in MS patients. Important metrics such as the use of a minimum of 2-day estimates and self-reported disability were found to be robust indicators and correlates, respectively, of participant activity levels. Further triangulation of such metrics may reduce the burden on patients, clinicians, and researchers when monitoring clinical status.