Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis

Pragmatic phase II clinical trial to improve depression care in a real-world diverse MS cohort from an academic MS centre in Northern California: MS CATCH study protocol

INTRODUCTION

Depression occurs in over 50% of individuals living with multiple sclerosis (MS) and can be treated using many modalities. Yet, it remains: under-reported by patients, under-ascertained by clinicians and under-treated. To enhance these three behaviours likely to promote evidence-based depression care, we engaged multiple stakeholders to iteratively design a first-in-kind digital health tool. The tool, MS CATCH (Care technology to Ascertain, Treat, and engage the Community to Heal depression in patients with MS), closes the communication loop between patients and clinicians. Between clinical visits, the tool queries patients monthly about mood symptoms, supports patient self-management and alerts clinicians to worsening mood via their electronic health record in-basket. Clinicians can also access an MS CATCH dashboard displaying patients' mood scores over the course of their disease, and providing comprehensive management tools (contributing factors, antidepressant pathway, resources in patient's neighbourhood). The goal of the current trial is to evaluate the clinical effect and usability of MS CATCH in a real-world clinical setting.

METHODS AND ANALYSIS

MS CATCH is a single-site, phase II randomised, delayed start, trial enrolling 125 adults with MS and mild to moderately severe depression. Arm 1 will receive MS CATCH for 12 months, and arm 2 will receive usual care for 6 months, then MS CATCH for 6 months. Clinicians will be randomised to avoid practice effects. The effectiveness analysis is superiority intent-to-treat comparing MS CATCH to usual care over 6 months (primary outcome: evidence of screening and treatment; secondary outcome: Hospital Anxiety Depression Scale-Depression scores). The usability of the intervention will also be evaluated (primary outcome: adoption; secondary outcomes: adherence, engagement, satisfaction).

ETHICS AND DISSEMINATION

University of California, San Francisco Institutional Review Board (22-36620). The findings of the study are planned to be shared through conferences and publishments in a peer-reviewed journal. The deidentified dataset will be shared with qualified collaborators on request, provision of CITI and other certifications, and data sharing agreement. We will share the results, once the data are complete and analysed, with the scientific community and patient/clinician participants through abstracts, presentations and manuscripts.

TRIAL REGISTRATION NUMBER

NCT05865405.

Use of smartphone-based remote assessments of multiple sclerosis in Floodlight Open, a global, prospective, open-access study

Floodlight Open was a global, open-access, digital-only study designed to understand the drivers and barriers in deployment and use of a smartphone app in a naturalistic setting and broad study population of people with and without multiple sclerosis (MS). The study utilised the Floodlight Open app: a 'bring-your-own-device' solution that remotely measures a user's mood, cognition, hand motor function, and gait and postural stability via smartphone sensor-based tests requiring active user input ('active tests'). Levels of mobility of study participants ('life-space measurement') were passively measured. Study data from these tests were made available via an open-access platform. Data from 1350 participants with self-declared MS and 1133 participants with self-declared non-MS from 17 countries across four continents were included in this report. Overall, MS participants provided active test data for a mean duration of 5.6 weeks or a mean duration of 19 non-consecutive days. This duration increased among MS participants who persisted beyond the first week to a mean of 10.3 weeks or 36.5 non-consecutive days. Passively collected life-space measurement data were generated by MS participants for a mean duration of 9.8 weeks or 50.6 non-consecutive days. This duration increased to 16.3 weeks/85.1 non-consecutive days among MS participants who persisted beyond the first week. Older age, self-declared MS disease status, and clinical supervision as part of concomitant clinical research were all significantly associated with higher persistence of the use of the Floodlight Open app. MS participants performed significantly worse than non-MS participants on four out of seven active tests. The findings from this multinational study inform future research to improve the dynamics of persistence of use of digital monitoring tools and further highlight challenges and opportunities in applying them to support MS clinical care.

"Self-care selfies": Patient-uploaded videos capture meaningful changes in dexterity over 6 months

OBJECTIVE

Upper extremity function reflects disease progression in multiple sclerosis (MS). This study evaluated the feasibility, validity, and sensitivity to change of remote dexterity assessments applying human pose estimation to patient-uploaded videos.

METHODS

A discovery cohort of 50 adults with MS recorded "selfie" videos of self-care tasks at home: buttoning, brushing teeth, and eating. Kinematic data were extracted using MediaPipe Hand pose estimation software. Clinical comparison tests were grip and pinch strength, 9 hole peg test (9HPT), and vibration, and patient-reported dexterity assessments (ABILHAND). Feasibility and acceptability were evaluated (Health-ITUES framework). A validation cohort (N = 35) completed 9HPT and videos.

RESULTS

The modality was feasible: 88% of the 50 enrolled participants uploaded ≥3 videos, and 74% completed the study. It was also usable: assessments easy to access (95%), platform easy to use (97%), and tasks representative of daily activities (86%). The buttoning task revealed four metrics with strong correlations with 9HPT (nondominant: r = 0.60-0.69, dominant: r = 0.51-0.57, P < 0.05) and ABILHAND (r = -0.48, P = 0.05). Retest validity at 1 week was stable (r > 0.8). Cross-sectional correlations between video metrics and 9HPT were similar at 6 months, and in the validation cohort (nondominant: r = 0.46, dominant: r = 0.45, P < 0.05). Over 6 months, pinch strength (5.8-5.0 kg/cm2 , P = 0.05) and self-reported pinch (ABILHAND) decreased marginally. While only 15% of participants worsened by 20% on 9HPT, 70% worsened in key buttoning video metrics.

INTERPRETATION

Patient-uploaded videos represent a novel, patient-centered modality for capturing dexterity that appears valid and sensitive to change, enhancing its potential to be disseminated for neurological disease monitoring and treatment.

Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis

BACKGROUND

Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study.

METHODS

Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed.

RESULTS

After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count.

SIGNIFICANCE

Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.

Wearable Sensor Technologies to Assess Motor Functions in People With Multiple Sclerosis: Systematic Scoping Review and Perspective

BACKGROUND

Wearable sensor technologies have the potential to improve monitoring in people with multiple sclerosis (MS) and inform timely disease management decisions. Evidence of the utility of wearable sensor technologies in people with MS is accumulating but is generally limited to specific subgroups of patients, clinical or laboratory settings, and functional domains.

OBJECTIVE

This review aims to provide a comprehensive overview of all studies that have used wearable sensors to assess, monitor, and quantify motor function in people with MS during daily activities or in a controlled laboratory setting and to shed light on the technological advances over the past decades.

METHODS

We systematically reviewed studies on wearable sensors to assess the motor performance of people with MS. We scanned PubMed, Scopus, Embase, and Web of Science databases until December 31, 2022, considering search terms "multiple sclerosis" and those associated with wearable technologies and included all studies assessing motor functions. The types of results from relevant studies were systematically mapped into 9 predefined categories (association with clinical scores or other measures; test-retest reliability; group differences, 3 types; responsiveness to change or intervention; and acceptability to study participants), and the reporting quality was determined through 9 questions. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines.

RESULTS

Of the 1251 identified publications, 308 were included: 176 (57.1%) in a real-world context, 107 (34.7%) in a laboratory context, and 25 (8.1%) in a mixed context. Most publications studied physical activity (196/308, 63.6%), followed by gait (81/308, 26.3%), dexterity or tremor (38/308, 12.3%), and balance (34/308, 11%). In the laboratory setting, outcome measures included (in addition to clinical severity scores) 2- and 6-minute walking tests, timed 25-foot walking test, timed up and go, stair climbing, balance tests, and finger-to-nose test, among others. The most popular anatomical landmarks for wearable placement were the waist, wrist, and lower back. Triaxial accelerometers were most commonly used (229/308, 74.4%). A surge in the number of sensors embedded in smartphones and smartwatches has been observed. Overall, the reporting quality was good.

CONCLUSIONS

Continuous monitoring with wearable sensors could optimize the management of people with MS, but some hurdles still exist to full clinical adoption of digital monitoring. Despite a possible publication bias and vast heterogeneity in the outcomes reported, our review provides an overview of the current literature on wearable sensor technologies used for people with MS and highlights shortcomings, such as the lack of harmonization, transparency in reporting methods and results, and limited data availability for the research community. These limitations need to be addressed for the growing implementation of wearable sensor technologies in clinical routine and clinical trials, which is of utmost importance for further progress in clinical research and daily management of people with MS.

TRIAL REGISTRATION

PROSPERO CRD42021243249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=243249.

Real-life ankle submovements and computer mouse use reflect patient-reported function in adult ataxias

Novel disease-modifying therapies are being evaluated in spinocerebellar ataxias and multiple system atrophy. Clinician-performed disease rating scales are relatively insensitive for measuring disease change over time, resulting in large and long clinical trials. We tested the hypothesis that sensors worn continuously at home during natural behaviour and a web-based computer mouse task performed at home could produce interpretable, meaningful and reliable motor measures for potential use in clinical trials. Thirty-four individuals with degenerative ataxias (spinocerebellar ataxia types 1, 2, 3 and 6 and multiple system atrophy of the cerebellar type) and eight age-matched controls completed the cross-sectional study. Participants wore an ankle and wrist sensor continuously at home for 1 week and completed the Hevelius computer mouse task eight times over 4 weeks. We examined properties of motor primitives called 'submovements' derived from the continuous wearable sensors and properties of computer mouse clicks and trajectories in relationship to patient-reported measures of function (Patient-Reported Outcome Measure of Ataxia) and ataxia rating scales (Scale for the Assessment and Rating of Ataxia and the Brief Ataxia Rating Scale). The test-retest reliability of digital measures and differences between ataxia and control participants were evaluated. Individuals with ataxia had smaller, slower and less powerful ankle submovements during natural behaviour at home. A composite measure based on ankle submovements strongly correlated with ataxia rating scale scores (Pearson's r = 0.82-0.88), strongly correlated with self-reported function (r = 0.81), had high test-retest reliability (intraclass correlation coefficient = 0.95) and distinguished ataxia and control participants, including preataxic individuals (n = 4) from controls. A composite measure based on computer mouse movements and clicks strongly correlated with ataxia rating scale total (r = 0.86-0.88) and arm scores (r = 0.65-0.75), correlated well with self-reported function (r = 0.72-0.73) and had high test-retest reliability (intraclass correlation coefficient = 0.99). These data indicate that interpretable, meaningful and highly reliable motor measures can be obtained from continuous measurement of natural movement, particularly at the ankle location, and from computer mouse movements during a simple point-and-click task performed at home. This study supports the use of these two inexpensive and easy-to-use technologies in longitudinal natural history studies in spinocerebellar ataxias and multiple system atrophy of the cerebellar type and shows promise as potential motor outcome measures in interventional trials.

Decentralised clinical trials in multiple sclerosis research

Randomised controlled trials (RCTs) play an important role in multiple sclerosis (MS) research, ensuring that new interventions are safe and efficacious before their introduction into clinical practice. Trials have been evolving to improve the robustness of their designs and the efficiency of their conduct. Advances in digital and mobile technologies in recent years have facilitated this process and the first RCTs with decentralised elements became possible. Decentralised clinical trials (DCTs) are conducted remotely, enabling participation of a more heterogeneous population who can participate in research activities from different locations and at their convenience. DCTs also rely on digital and mobile technologies which allows for more flexible and frequent assessments. While hospitals quickly adapted to e-health and telehealth assessments during the COVID-19 pandemic, the conduct of conventional RCTs was profoundly disrupted. In this paper, we review the existing evidence and gaps in knowledge in the design and conduct of DCTs in MS.

Reliability and acceptance of dreaMS, a software application for people with multiple sclerosis: a feasibility study

BACKGROUND

There is an unmet need for reliable and sensitive measures for better monitoring people with multiple sclerosis (PwMS) to detect disease progression early and adapt therapeutic measures accordingly.

OBJECTIVE

To assess reliability of extracted features and meaningfulness of 11 tests applied through a smartphone application ("dreaMS").

METHODS

PwMS (age 18-70 and EDSS ≤ 6.5) and matched healthy volunteers (HV) were asked to perform tests installed on their smartphone once or twice weekly for 5 weeks. Primary outcomes were test-retest reliability of test features (target: intraclass correlation [ICC] ≥ 0.6 or median coefficient of variation [mCV] < 0.2) and reported meaningfulness of the tests by PwMS. Meaningfulness was self-assessed for each test on a 5-point Likert scale (target: mean score of > 3) and by a structured interview.

CLINICALTRIALS

gov Identifier: NCT04413032.

RESULTS

We included 31 PwMS (21 [68%] female, mean age 43.4 ± 12.0 years, median EDSS 3.0 [range 1.0-6.0]) and 31 age- and sex-matched healthy volunteers. Out of 133 features extracted from 11 tests, 89 met the preset reliability criteria. All 11 tests were perceived as highly meaningful to PwMS.

CONCLUSION

The dreaMS app reliably assessed features reflecting key functional domains meaningful to PwMS. More studies with longer follow-up are needed to prove validity of these measures as digital biomarkers in PwMS.

Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones

Goal: Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. Methods: Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. Results: This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score ([Formula: see text]: 0.56,[Formula: see text]0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. Conclusion: Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.

Impact of Covid-19 emergency on rehabilitation services for Multiple Sclerosis: An international RIMS survey

Background

Covid-19 pandemic greatly impacted on the healthcare systems worldwide with negative consequences on several aspects of clinical populations. For neurological chronic conditions such as Multiple Sclerosis (MS), rehabilitation activities have been suspended or postponed during the pandemic. Rehabilitation is crucial for people with MS (PwMS) because it promotes recovery from relapses and maximizes opportunities for social participation. To better understand the impact of Covid-19 emergency on rehabilitation services for MS, the European network for rehabilitation in MS (RIMS) disseminated a survey to healthcare professionals (HPs) and representatives of the MS rehabilitation services (RSs), to explore the two different perspectives on the delivery of rehabilitation in usual circumstances and during the Covid-19 emergency.

Methods

The online survey was distributed from July 9th to September 20th, 2020. Besides general information on the responders (e.g. location of center, and memebership to RIMS), information was collected on usual service delivery (e.g. settings, specialities, and types of treatment), the impact of Covid-19 circumstances (e.g. restrictions, use of personal protective equipment, and impact on work), and the use of technologiesin rehabilitation.

Results

Twenty-two representatives of MS rehabilitation services (RSs)and 143 health care professionals (HPs) responded. Most of RSs and HPs worked in services specialized for MS including a mixture of all usual rehabilitation settings (i.e. inpatient, outpatient and community setting). The majority of services adopted a multidisciplinary framework, including physical therapy, occupational therapy, social service, speech and language therapy, psychological support, dietary interventions, medical management, vocational rehabilitation and cognitive rehabilitaton. Overall, most of responders indicated they did not use technologies in their practice (e.g. for treatment or assessment). However, depending on the type of technology a low-to-medium percentage of responders declared to use some technologies before Covid-19 crisis (5-55% for RSs and 12-53% for HPs) and a low percentage planned the use after pandemic (0-14% for RSs and 1-10% for HPs). Moreover, for the responders the most feasible interventions deliverable through tele-rehabilitation were psychological support and dietary interventions, with psychological support considered the most necessary intervention to be remotely implemented. Moderate feasibility (30-60%) was reported for hands-off interventions (e.g. aerobic exercise and cognitive rehabilitation) whereas low feasibility (<30%) was reported for hands-on interventions. Feasibility was especially low when tools were used that are not adaptable at-home (e.g. hyperbaric oxygen therapy).

Conclusion

The Covid-19 pandemic has stimulated the MS healthcare professionals to find new solutions to deliver alternative interventions to PwMS. In this context, the role of telemedicine is crucial to continue rehabilitation services at home, and limit exposure to infection. However, most of healthcare professionals have not incorporated the use of technologies. Therefore, the implementation of digital health solutions in the clinical practice needs more attention towards education on the potentials of technologies for rehabilitation and simplification of the national healthcare system reimbursement procedures for the rehabilitation technologies use.

A systematic review of engagement reporting in remote measurement studies for health symptom tracking

Remote Measurement Technologies (RMTs) could revolutionise management of chronic health conditions by providing real-time symptom tracking. However, the promise of RMTs relies on user engagement, which at present is variably reported in the field. This review aimed to synthesise the RMT literature to identify how and to what extent engagement is defined, measured, and reported, and to present recommendations for the standardisation of future work. Seven databases (Embase, MEDLINE and PsycINFO (via Ovid), PubMed, IEEE Xplore, Web of Science, and Cochrane Central Register of Controlled Trials) were searched in July 2020 for papers using RMT apps for symptom monitoring in adults with a health condition, prompting users to track at least three times during the study period. Data were synthesised using critical interpretive synthesis. A total of 76 papers met the inclusion criteria. Sixty five percent of papers did not include a definition of engagement. Thirty five percent included both a definition and measurement of engagement. Four synthetic constructs were developed for measuring engagement: (i) engagement with the research protocol, (ii) objective RMT engagement, (iii) subjective RMT engagement, and (iv) interactions between objective and subjective RMT engagement. The field is currently impeded by incoherent measures and a lack of consideration for engagement definitions. A process for implementing the reporting of engagement in study design is presented, alongside a framework for definition and measurement options available. Future work should consider engagement with RMTs as distinct from the wider eHealth literature, and measure objective versus subjective RMT engagement.Registration: This review has been registered on PROSPERO [CRD42020192652].

Remote Assessments of Hand Function in Neurological Disorders: Systematic Review

BACKGROUND

Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted.

OBJECTIVE

Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction.

METHODS

PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development.

RESULTS

In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based.

CONCLUSIONS

The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.

Automatic Classification and Severity Estimation of Ataxia From Finger Tapping Videos

Digital assessments enable objective measurements of ataxia severity and provide informative features that expand upon the information obtained during a clinical examination. In this study, we demonstrate the feasibility of using finger tapping videos to distinguish participants with Ataxia (N = 169) from participants with parkinsonism (N = 78) and from controls (N = 58), and predict their upper extremity and overall disease severity. Features were extracted from the time series representing the distance between the index and thumb and its derivatives. Classification models in ataxia archived areas under the receiver-operating curve of around 0.91, and regression models estimating disease severity obtained correlation coefficients around r = 0.64. Classification and prediction model coefficients were examined and they not only were in accordance, but were in line with clinical observations of ataxia phenotypes where rate and rhythm are altered during upper extremity motor movement.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the "Spiral tracing." We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.

Practice Effects of Mobile Tests of Cognition, Dexterity, and Mobility on Patients With Multiple Sclerosis: Data Analysis of a Smartphone-Based Observational Study

Background

Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration.

Objective

The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing.

Methods

We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility.

Results

Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed.

Conclusions

Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered.

Wearable technologies to measure clinical outcomes in multiple sclerosis: A scoping review

Wearable technology refers to any sensor worn on the person, making continuous and remote monitoring available to many people with chronic disease, including multiple sclerosis (MS). Daily monitoring seems an ideal solution either as an outcome measure or as an adjunct to support rater-based monitoring in both clinical and research settings. There has been an increase in solutions that are available, yet there is little consensus on the most appropriate solution to use in either MS research or clinical practice. We completed a scoping review (using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines) to summarise the wearable solutions available in MS, to identify those approaches that could potentially be utilised in clinical trials, by evaluating the following: scalability, cost, patient adaptability and accuracy. We identified 35 unique products that measure gait, cognition, upper limb function, activity, mood and fatigue, with most of these solutions being phone applications.

Developing a Digital Solution for Remote Assessment in Multiple Sclerosis: From Concept to Software as a Medical Device

There is increasing interest in the development and deployment of digital solutions to improve patient care and facilitate monitoring in medical practice, e.g., by remote observation of disease symptoms in the patients’ home environment. Digital health solutions today range from non-regulated wellness applications and research-grade exploratory instruments to regulated software as a medical device (SaMD). This paper discusses the considerations and complexities in developing innovative, effective, and validated SaMD for multiple sclerosis (MS). The development of SaMD requires a formalised approach (design control), inclusive of technical verification and analytical validation to ensure reliability. SaMD must be clinically evaluated, characterised for benefit and risk, and must conform to regulatory requirements associated with device classification. Cybersecurity and data privacy are also critical. Careful consideration of patient and provider needs throughout the design and testing process help developers overcome challenges of adoption in medical practice. Here, we explore the development pathway for SaMD in MS, leveraging experiences from the development of Floodlight™ MS, a continually evolving bundled solution of SaMD for remote functional assessment of MS. The development process will be charted while reflecting on common challenges in the digital space, with a view to providing insights for future developers.

Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia

With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.

A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1 week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.

Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p < 0.01; Cohen’s d = 1.48) and fewer high intensity movements (p < 0.001; Cohen’s d = 1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p < 0.0001; Cohen’s d = 2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71–0.87; p < 0.0001), correlated with specific computer task motor features (r: 0.67–0.92; p < 0.01), demonstrated high reliability (r: 0.86–0.93; p < 0.00001), and were not significantly influenced by age in the healthy control group.

Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.

Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8-15%. A lack of transparency of "black-box" deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

Mobile health (mHealth) usage, barriers, and technological considerations in persons with multiple sclerosis: a literature review

OBJECTIVES

Persons with multiple sclerosis (MS) can face a number of potential healthcare-related barriers, for which mobile health (mHealth) technology can be potentially beneficial. This review aimed to understand the frequency, current uses, and potential barriers with mHealth usage among persons with MS.

METHODS

A query string was used to identify articles on PubMed, MEDLINE, CINAHL, and IEEE Xplore that were published in English between January 2010 and December 2019. Abstracts were reviewed and selected based on a priori inclusion and exclusion criteria. Fifty-nine peer-reviewed research studies related to the study questions are summarized.

RESULTS

The majority of persons with MS were reported as using smartphones, although rates of mHealth utilization varied widely. mHealth usage was grouped into 3 broad categories: (1) disability and symptom measurement; (2) interventions and symptom management; and (3) tracking and promoting adherence. While there have been an increasing number of mHealth options, certain limitations associated with MS (eg, poor dexterity, memory problems) may affect usage, although including persons with MS in the design process can address some of these issues.

DISCUSSION

Given the increased attention to mHealth in this population and the current need for telehealth and at home devices, it is important that persons with MS and healthcare providers are involved in the development of new mHealth tools to ensure that the end product meets their needs. Considerations for addressing the potential mHealth use barriers in persons with MS are discussed.

Mobile-phone-based e-diary derived patient reported outcomes: Association with clinical disease activity, psychological status and quality of life of patients with multiple sclerosis

BACKGROUND

The applicability of mobile digital technology to promote clinical care of people with multiple sclerosis (pwMS) is gaining increased interest as part of the implementation of patient-centered approaches. We aimed at assessing adherence to a smartphone-based e-diary, which was designed to collect patient-reported outcomes (PROs). Secondary objectives were to evaluate the construct and predictive validity of e-diary derived PROs and to explore the various factors that were associated with changes in PROs over time.

MATERIALS AND METHODS

In this observational cohort study patients downloaded an MS tailored e-diary into their personal smartphones. Report of PROs was enquired once monthly for a period of one year through a smartphone-based application, using previously validated tools. An e-diary derived bodily function summary score (eBF) was defined as the sum of scores depicting vision, limbs function, pain, bowl/ bladder dysfunction, pseudobulbar affect and spasticity. Multiple linear regression and analysis of covariance were used to determine the association between PROs, clinician-reported outcomes (ClinROs) of disease activity and quality of life (QoL). Regression coefficient analysis was used to compare the slope of change in eBF before and after a relapse.

RESULTS

97 pwMS downloaded the e-diary [Female: 64 (66%), EDSS 3.4±2.1]. 76 patients (78%) completed the 12-month study period. 53 patients (55%) submitted ≥75% of requested surveys. Anxiety was negatively associated with adherence to periodic PROs assessments by the e-diary. E-diary derived PROs were significantly correlated with corresponding functional system scores (0.38< r <0.8, P<0.001). eBF score significantly predicted QoL (β = -0.36, P = 0.001) while EDSS did not. Change in eBF score over time was independently associated with the occurrence of an MS relapse (F = 4.4, P = 0.04), anxiety (F = 6.4, P = 0.01) and depression (F = 5.1, P = 0.03). Individual regression slopes of eBF scores were significantly higher pre-relapse than post-relapse (3.0±3.3 vs. -0.8±2.0, P = 0.007).

CONCLUSION

Adherence of pwMS to recording in an e-diary collecting PROs was high. Changes in e-diary derived PROs over time predict clinical MS relapses on the group level and thus carry the potential of usage in clinical research as well as for improved MS care in real world setting.

Usability of a Mobile App for Real-Time Assessment of Fatigue and Related Symptoms in Patients With Multiple Sclerosis: Observational Study

Background

Although fatigue is one of the most debilitating symptoms in patients with multiple sclerosis (MS), its pathogenesis is not well understood. Neurogenic, inflammatory, endocrine, and metabolic mechanisms have been proposed. Taking into account the temporal dynamics and comorbid mood symptoms of fatigue may help differentiate fatigue phenotypes. These phenotypes may reflect different pathogeneses and may respond to different mechanism-specific treatments. Although several tools have been developed to assess various symptoms (including fatigue), monitor clinical status, or improve the perceived level of fatigue in patients with MS, options for a detailed, real-time assessment of MS-related fatigue and relevant comorbidities are still limited.

Objective

This study aims to present a novel mobile app specifically designed to differentiate fatigue phenotypes using circadian symptom monitoring and state-of-the-art characterization of MS-related fatigue and its related symptoms. We also aim to report the first findings regarding patient compliance and the relationship between compliance and patient characteristics, including MS disease severity.

Methods

After developing the app, we used it in a prospective study designed to investigate the brain magnetic resonance imaging correlates of MS-related fatigue. In total, 64 patients with MS were recruited into this study and asked to use the app over a 2-week period. The app features the following modules: Visual Analogue Scales (VASs) to assess circadian changes in fatigue, depression, anxiety, and pain; daily sleep diaries (SLDs) to assess sleep habits and quality; and 10 one-time questionnaires to assess fatigue, depression, anxiety, sleepiness, physical activity, and motivation, as well as several other one-time questionnaires that were created to assess those relevant aspects of fatigue that were not captured by existing fatigue questionnaires. The app prompts subjects to assess their symptoms multiple times a day and enables real-time symptom monitoring through a web-accessible portal.

Results

Of 64 patients, 56 (88%) used the app, of which 51 (91%) completed all one-time questionnaires and 47 (84%) completed all one-time questionnaires, VASs, and SLDs. Patients reported no issues with the usage of the app, and there were no technical issues with our web-based data collection system. The relapsing-remitting MS to secondary-progressive MS ratio was significantly higher in patients who completed all one-time questionnaires, VASs, and SLDs than in those who completed all one-time questionnaires but not all VASs and SLDs (P=.01). No other significant differences in demographics, fatigue, or disease severity were observed between the degrees of compliance.

Conclusions

The app can be used with reasonable compliance across patients with relapsing-remitting and secondary-progressive MS irrespective of demographics, fatigue, or disease severity.

Home video prediction of epileptic vs. nonepileptic seizures in US veterans

OBJECTIVE

Using video-EEG (v-EEG) diagnosis as a gold standard, we assessed the predictive diagnostic value of home videos of spells with or without additional limited demographic data in US veterans referred for evaluation of epilepsy. Veterans, in particular, stand to benefit from improved diagnostic tools given higher rates of PNES and limited accessibility to care.

METHODS

This was a prospective, blinded diagnostic accuracy study in adults conducted at the Houston VA Medical Center from 12/2015-06/2019. Patients with a definitive diagnosis of epileptic seizures (ES), psychogenic nonepileptic seizures (PNES), or physiologic nonepileptic events (PhysNEE) from v-EEG monitoring were asked to submit home videos. Four board-certified epileptologists blinded to the original diagnosis formulated a diagnostic impression based upon the home video review alone and video plus limited demographic data.

RESULTS

Fifty patients (30 males; mean age 47.7 years) submitted home videos. Of these, 14 had ES, 33 had PNES, and three had PhysNEE diagnosed by v-EEG. The diagnostic accuracy by video alone was 88.0%, with a sensitivity of 83.9% and specificity of 89.6%. Providing raters with basic patient demographic information in addition to the home videos did not significantly improve diagnostic accuracy when comparing to reviewing the videos alone. Inter-rater agreement between four raters based on video was moderate with both videos alone (kappa = 0.59) and video plus limited demographic data (kappa = 0.60).

SIGNIFICANCE

This study demonstrated that home videos of paroxysmal events could be an important tool in reliably diagnosing ES vs. PNES in veterans referred for evaluation of epilepsy when interpreted by experts. A moderate inter-rater reliability was observed in this study.

Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test

Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility of assessing PwMS and Healthy Control's (HC) physical function by characterising gait-related features, which can be modelled using machine learning (ML) techniques to correctly distinguish subgroups of PwMS from healthy controls. A total of 97 subjects (24 HC subjects, 52 mildly disabled (PwMSmild, EDSS [0-3]) and 21 moderately disabled (PwMSmod, EDSS [3.5-5.5]) contributed data which was recorded from a Two-Minute Walk Test (2MWT) performed out-of-clinic and daily over a 24-week period. Signal-based features relating to movement were extracted from sensors in smartphone and smartwatch devices. A large number of features (n = 156) showed fair-to-strong (R 0.3) correlations with clinical outcomes. LASSO feature selection was applied to select and rank subsets of features used for dichotomous classification between subject groups, which were compared using Logistic Regression (LR), Support Vector Machines (SVM) and Random Forest (RF) models. Classifications of subject types were compared using data obtained from smartphone, smartwatch and the fusion of features from both devices. Models built on smartphone features alone achieved the highest classification performance, indicating that accurate and remote measurement of the ambulatory characteristics of HC and PwMS can be achieved with only one device. It was observed however that smartphone-based performance was affected by inconsistent placement location (running belt versus pocket). Results show that PwMSmod could be distinguished from HC subjects (Acc. 82.2 ± 2.9%, Sen. 80.1 ± 3.9%, Spec. 87.2 ± 4.2%, F ₁ 84.3 ± 3.8), and PwMSmild (Acc. 82.3 ± 1.9%, Sen. 71.6 ± 4.2%, Spec. 87.0 ± 3.2%, F 1 75.1 ± 2.2) using an SVM classifier with a Radial Basis Function (RBF). PwMSmild were shown to exhibit HC-like behaviour and were thus less distinguishable from HC (Acc. 66.4 ± 4.5%, Sen. 67.5 ± 5.7%, Spec. 60.3 ± 6.7%, F ₁ 58.6 ± 5.8). Finally, it was observed that subjects in this study demonstrated low intra- and high inter-subject variability which was representative of subject-specific gait characteristics.

Assessment of the Predictive Value of Outpatient Smartphone Videos for Diagnosis of Epileptic Seizures

Importance

Misdiagnosis of epilepsy is common. Video electroencephalogram provides a definitive diagnosis but is impractical for many patients referred for evaluation of epilepsy.

Objective

To evaluate the accuracy of outpatient smartphone videos in epilepsy.

Design, Setting, and Participants

This prospective, masked, diagnostic accuracy study (the OSmartViE study) took place between August 31, 2015, and August 31, 2018, at 8 academic epilepsy centers in the United States and included a convenience sample of 44 nonconsecutive outpatients who volunteered a smartphone video during evaluation and subsequently underwent video electroencephalogram monitoring. Three epileptologists uploaded videos for physicians from the 8 epilepsy centers to review.

Main Outcomes and Measures

Measures of performance (accuracy, sensitivity, specificity, positive predictive value, and negative predictive value) for smartphone video-based diagnosis by experts and trainees (the index test) were compared with those for history and physical examination and video electroencephalogram monitoring (the reference standard).

Results

Forty-four eligible epilepsy clinic outpatients (31 women [70.5%]; mean [range] age, 45.1 [20-82] years) submitted smartphone videos (530 total physician reviews). Final video electroencephalogram diagnoses included 11 epileptic seizures, 30 psychogenic nonepileptic attacks, and 3 physiologic nonepileptic events. Expert interpretation of a smartphone video was accurate in predicting a video electroencephalogram monitoring diagnosis of epileptic seizures 89.1% (95% CI, 84.2%-92.9%) of the time, with a specificity of 93.3% (95% CI, 88.3%-96.6%). Resident responses were less accurate for all metrics involving epileptic seizures and psychogenic nonepileptic attacks, despite greater confidence. Motor signs during events increased accuracy. One-fourth of the smartphone videos were correctly diagnosed by 100% of the reviewing physicians, composed solely of psychogenic attacks. When histories and physical examination results were combined with smartphone videos, correct diagnoses rose from 78.6% to 95.2%. The odds of receiving a correct diagnosis were 5.45 times greater using smartphone video alongside patient history and physical examination results than with history and physical examination alone (95% CI, 1.01-54.3; P = .02).

Conclusions and Relevance

Outpatient smartphone video review by experts has predictive and additive value for diagnosing epileptic seizures. Smartphone videos may reliably aid psychogenic nonepileptic attacks diagnosis for some people.

Imaging Mechanisms of Disease Progression in Multiple Sclerosis: Beyond Brain Atrophy

Clinicians involved with different aspects of the care of persons with multiple sclerosis (MS) and scientists with expertise on clinical and imaging techniques convened in Dallas, TX, USA on February 27, 2019 at a North American Imaging in Multiple Sclerosis Cooperative workshop meeting. The aim of the workshop was to discuss cardinal pathobiological mechanisms implicated in the progression of MS and novel imaging techniques, beyond brain atrophy, to unravel these pathologies. Indeed, although brain volume assessment demonstrates changes linked to disease progression, identifying the biological mechanisms leading up to that volume loss are key for understanding disease mechanisms. To this end, the workshop focused on the application of advanced magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging techniques to assess and measure disease progression in both the brain and the spinal cord. Clinical translation of quantitative MRI was recognized as of vital importance, although the need to maintain a relatively short acquisition time mandated by most radiology departments remains the major obstacle toward this effort. Regarding PET, the panel agreed upon its utility to identify ongoing pathological processes. However, due to costs, required expertise, and the use of ionizing radiation, PET was not considered to be a viable option for ongoing care of persons with MS. Collaborative efforts fostering robust study designs and imaging technique standardization across scanners and centers are needed to unravel disease mechanisms leading to progression and discovering medications halting neurodegeneration and/or promoting repair.

CoachMS, an innovative closed-loop, interdisciplinary platform to monitor and proactively treat MS symptoms: A pilot study

Background

There are numerous challenges to treating co-occurring symptoms in multiple sclerosis (MS).

Objective

To pilot the feasibility of a novel symptom management platform, CoachMS, to monitor MS symptoms (bladder function, ambulation, and mood: BAM) and respond to changes in real-time.

Methods

In this 12-week randomized controlled pilot trial, participants’ symptoms were monitored using weekly questionnaires and remote ambulatory monitoring (Fitbit Flex2®). Behavioral change principles used included shared goal setting at 2 weeks. Between weeks 2-12, the CoachMS group received targeted contact and interventions if symptoms worsened; the control group were treated through usual clinic practice. Our outcomes were feasibility (retention, adherence and acceptability; primary) and proportion of recommended treatments pursued (secondary); efficacy was explored.

Results

Of 21 participants enrolled, 13 (62%) completed the study; protocol adherence was excellent. CoachMS participants demonstrated greater follow-through with clinical recommendations than controls (OR 9.3, 95% CI (0.9, 97.6)). As a cohort, each BAM symptom tended to improve. Suicidality was detected in one control participant, resulting in urgent evaluation and hospitalization.

Conclusions

The innovative CoachMS platform was feasible and acceptable in this cohort with baseline BAM symptoms. It could represent an accessible, cost-effective tool to monitor MS symptoms in real-time; a larger trial is planned.

Evaluating the Utility of Smartphone-Based Sensor Assessments in Persons With Multiple Sclerosis in the Real-World Using an App (elevateMS): Observational, Prospective Pilot Digital Health Study

Background

Multiple sclerosis (MS) is a chronic neurodegenerative disease. Current monitoring practices predominantly rely on brief and infrequent assessments, which may not be representative of the real-world patient experience. Smartphone technology provides an opportunity to assess people’s daily-lived experience of MS on a frequent, regular basis outside of episodic clinical evaluations.

Objective

The objectives of this study were to evaluate the feasibility and utility of capturing real-world MS-related health data remotely using a smartphone app, “elevateMS,” to investigate the associations between self-reported MS severity and sensor-based active functional tests measurements, and the impact of local weather conditions on disease burden.

Methods

This was a 12-week, observational, digital health study involving 3 cohorts: self-referred participants who reported an MS diagnosis, clinic-referred participants with neurologist-confirmed MS, and participants without MS (controls). Participants downloaded the elevateMS app and completed baseline assessments, including self-reported physical ability (Patient-Determined Disease Steps [PDDS]), as well as longitudinal assessments of quality of life (Quality of Life in Neurological Disorders [Neuro-QoL] Cognitive, Upper Extremity, and Lower Extremity Function) and daily health (MS symptoms, triggers, health, mobility, pain). Participants also completed functional tests (finger-tapping, walk and balance, voice-based Digit Symbol Substitution Test [DSST], and finger-to-nose) as an independent assessment of MS-related cognition and motor activity. Local weather data were collected each time participants completed an active task. Associations between self-reported baseline/longitudinal assessments, functional tests, and weather were evaluated using linear (for cross-sectional data) and mixed-effects (for longitudinal data) regression models.

Results

A total of 660 individuals enrolled in the study; 31 withdrew, 495 had MS (n=359 self-referred, n=136 clinic-referred), and 134 were controls. Participation was highest in clinic-referred versus self-referred participants (median retention: 25.5 vs 7.0 days). The top 5 most common MS symptoms, reported at least once by participants with MS, were fatigue (310/495, 62.6%), weakness (222/495, 44.8%), memory/attention issues (209/495, 42.2%), and difficulty walking (205/495, 41.4%), and the most common triggers were high ambient temperature (259/495, 52.3%), stress (250/495, 50.5%), and late bedtime (221/495, 44.6%). Baseline PDDS was significantly associated with functional test performance in participants with MS (mixed model–based estimate of most significant feature across functional tests [β]: finger-tapping: β=–43.64, P<.001; DSST: β=–5.47, P=.005; walk and balance: β=–.39, P=.001; finger-to-nose: β=.01, P=.01). Longitudinal Neuro-QoL scores were also significantly associated with functional tests (finger-tapping with Upper Extremity Function: β=.40, P<.001; walk and balance with Lower Extremity Function: β=–99.18, P=.02; DSST with Cognitive Function: β=1.60, P=.03). Finally, local temperature was significantly associated with participants’ test performance (finger-tapping: β=–.14, P<.001; DSST: β=–.06, P=.009; finger-to-nose: β=–53.88, P<.001).

Conclusions

The elevateMS study app captured the real-world experience of MS, characterized some MS symptoms, and assessed the impact of environmental factors on symptom severity. Our study provides further evidence that supports smartphone app use to monitor MS with both active assessments and patient-reported measures of disease burden. App-based tracking may provide unique and timely real-world data for clinicians and patients, resulting in improved disease insights and management.

Smartphone based behavioral therapy for pain in multiple sclerosis (MS) patients: A feasibility acceptability randomized controlled study for the treatment of comorbid migraine and ms pain

BACKGROUND

Multiple Sclerosis (MS) and Migraine are comorbid neurologic conditions. Migraine prevalence is three times higher in the MS clinic population compared to the general population, and patients with MS and migraine are more symptomatic than patients with MS without migraine.

OBJECTIVE

We sought to conduct a pilot feasibility and acceptability study of the RELAXaHEAD app in MS-Migraine patients and to assess whether there was any change in migraine disability and MS pain-related disability.

METHODS

Randomized controlled study of patients with MS-migraine ages 18-80 years with 4+ headache days/ month who were willing to engage in smartphone based behavioral therapy. Half received the RELAXaHEAD app with progressive muscle relaxation (PMR) and the other half received the app without the PMR. Data was collected for 90 days on measures of recruitment, retention, engagement, and adherence to RELAXaHEAD. Preliminary data was also collected on migraine disability (MIDAS) and MS pain (PES).

RESULTS

Sixty-two subjects with MS-migraine were enrolled in the study (34 in PMR arm, 28 in monitored usual care arm). On average, during the 90 days, participants played the PMR on average 1.8 times per week, and for 12.9 min on days it was played. Forty-one percent (14/34) of the participants played the PMR two or more times weekly on average. Data was entered into the daily diaries, on average, 49% (44/90) of the days. There were major challenges in reaching subjects in follow-up for the efficacy data, and there was no significant change in migraine disability (MIDAS) scores or MS Pain (PES) scores from baseline to the endpoints. During the six-month follow-up, most patients felt either positively or neutral about the relaxation therapy.

CONCLUSION

There was interest in scalable accessible forms of behavioral therapy to treat migraine and MS-related pain in patients with MS and comorbid migraine. Similar to prior studies, a significant minority were willing to practice the PMR at least twice weekly. In the societal shift from telephone to more text and internet-based interactions, follow up was challenging, but those reached indicated that they appreciated the PMR and would recommend it to others. Future work should focus on engagement and efficacy.

A roadmap to precision medicine for multiple sclerosis

Multiple sclerosis (MS) affects approximately 1 million persons in the United States, and is the leading cause of neurological disability in young adults. The concept of precision medicine is now being applied to MS and has the promise of improved care. MS patients experience a variety of neurological symptoms, and disease severity ranges from mild to severe, and the biological underpinnings of these phenotypes are now starting to be elucidated. Precision medicine involves the classification of disease subtypes based on the underlying biology, rather than clinical phenotypes alone, and may govern disease course and treatment response. Over 18 disease-modifying drugs have been approved for the treatment of MS, and several biomarkers of treatment response are emerging. This article provides an overview of the concepts of precision medicine and emerging biological markers and their evolving role in decision-making in MS management.

Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study

Background

Current clinical assessments of people with multiple sclerosis are episodic and may miss critical features of functional fluctuations between visits.

Objective

The goal of the research was to assess the feasibility of remote active testing and passive monitoring using smartphones and smartwatch technology in people with multiple sclerosis with respect to adherence and satisfaction with the FLOODLIGHT test battery.

Methods

People with multiple sclerosis (aged 20 to 57 years; Expanded Disability Status Scale 0-5.5; n=76) and healthy controls (n=25) performed the FLOODLIGHT test battery, comprising active tests (daily, weekly, every two weeks, or on demand) and passive monitoring (sensor-based gait and mobility) for 24 weeks using a smartphone and smartwatch. The primary analysis assessed adherence (proportion of weeks with at least 3 days of completed testing and 4 hours per day passive monitoring) and questionnaire-based satisfaction. In-clinic assessments (clinical and magnetic resonance imaging) were performed.

Results

People with multiple sclerosis showed 70% (16.68/24 weeks) adherence to active tests and 79% (18.89/24 weeks) to passive monitoring; satisfaction score was on average 73.7 out of 100. Neither adherence nor satisfaction was associated with specific population characteristics. Test-battery assessments had an at least acceptable impact on daily activities in over 80% (61/72) of people with multiple sclerosis.

Conclusions

People with multiple sclerosis were engaged and satisfied with the FLOODLIGHT test battery. FLOODLIGHT sensor-based measures may enable continuous assessment of multiple sclerosis disease in clinical trials and real-world settings.

Trial Registration

ClinicalTrials.gov: NCT02952911; https://clinicaltrials.gov/ct2/show/NCT02952911

Smartphone Level Test Measures Disability in Several Neurological Domains for Patients With Multiple Sclerosis

Our long-term goal is to employ smartphone-embedded sensors to measure various neurological functions in a patient-autonomous manner. The interim goal is to develop simple smartphone tests (apps) and evaluate the clinical utility of these tests by selecting optimal outcomes that correlate well with clinician-measured disability in different neurological domains. In this paper, we used prospectively-acquired data from 112 multiple sclerosis (MS) patients and 15 healthy volunteers (HV) to assess the performance and optimize outcomes of a Level Test. The goal of the test is to tilt the smartphone so that a free-rolling ball travels to and remains in the center of the screen. An accelerometer detects tilting and records the coordinates of the ball at set time intervals. From this data, we derived five features: path length traveled, time spent in the center of the screen, average distance from the center, average speed while in the center, and number of direction changes underwent by the ball. Time in center proved to be the most sensitive feature to differentiate MS patients from HV and had the strongest correlations with clinician-derived scales. Its superiority was validated in an independent validation cohort of 29 MS patients. A linear combination of different Level features failed to outperform time in center in an independent validation cohort. Limited longitudinal data demonstrated that the Level features were relatively stable intra-individually within 4 months, without definitive evidence of learning. In contrast to previously developed smartphone tests that predominantly measure motoric functions, Level features correlated strongly with reaction time and moderately with cerebellar functions and proprioception, validating its complementary clinical value in the MS app suite. The Level Test measures neurological disability in several domains in two independent cross-sectional cohorts (original and validation). An ongoing longitudinal cohort further investigates whether patient-autonomous collection of granular functional data allows measurement of patient-specific trajectories of disability progression to better guide treatment decisions.

Association of Continuous Assessment of Step Count by Remote Monitoring With Disability Progression Among Adults With Multiple Sclerosis

IMPORTANCE

Disability measures in multiple sclerosis (MS) fail to capture potentially important variability in walking behavior. More sensitive and ecologically valid outcome measures are needed to advance MS research.

OBJECTIVES

To assess continuous step count activity remotely among individuals with MS for 1 year and determine how average daily step count is associated with other measures of MS disability.

DESIGN, SETTING, AND PARTICIPANTS

In a prospective longitudinal observational cohort study, 95 adults with relapsing or progressive MS who were able to walk more than 2 minutes with or without an assistive device were recruited between June 15, 2015, and August 8, 2016, and remotely monitored in their natural environment for 1 year. Patients were excluded if they had a clinical relapse within 30 days or comorbidity contributing to ambulatory impairment. Longitudinal analysis was performed from October 2017 to March 2018. Revised analysis was performed in December 2018.

INTERVENTION

Activity monitoring of step count using a wrist-worn accelerometer.

MAIN OUTCOMES AND MEASURES

Average daily step count compared with in-clinic assessments and patient-reported outcomes.

RESULTS

Of the 95 participants recruited (59 women and 36 men; mean [SD] age, 49.6 [13.6] years [range, 22.0-74.0 years]), 35 (37%) had progressive MS, and the median baseline Expanded Disability Status Scale score was 4.0 (range, 0-6.5). At 1 year, 79 participants completed follow-up (83% retention). There was a modest reduction in accelerometer use during the 1 year of the study. A decreasing average daily step count during the study was associated with worsening of clinic-based outcomes (Timed 25-Foot Walk, β = -13.09; P < .001; Timed-Up-and-Go, β = -9.25; P < .001) and patient-reported outcomes (12-item Multiple Sclerosis Walking Scale, β = -17.96; P < .001). A decreasing average daily step count occurred even when the Expanded Disability Status Scale score remained stable, and 12 of 25 participants (48%) with a significant decrease in average daily step count during the study did not have a reduction on other standard clinic-based metrics. Participants with a baseline average daily step count below 4766 (cohort median) had higher odds of clinically meaningful disability (Expanded Disability Status Scale score) worsening at 1 year, adjusting for age, sex, and disease duration (odds ratio, 4.01; 95% CI, 1.17-13.78; P = .03).

CONCLUSIONS AND RELEVANCE

Continuous remote activity monitoring of individuals with MS for 1 year appears to be feasible. In this study, a decreasing average daily step count during a 1-year period was associated with worsening of standard ambulatory measures but could also occur even when traditional disability measures remained stable. These results appear to support the prospect of using the average daily step count as a sensitive longitudinal outcome measure in MS and as a clinically relevant metric for targeted intervention.

Cognitive Deficits in Multiple Sclerosis: Recent Advances in Treatment and Neurorehabilitation

PURPOSE OF REVIEW

This article highlights recent progress in research on treatment and neurorehabilitation of cognitive impairment in multiple sclerosis (MS) including pharmacological interventions, physical exercise, and neuropsychological rehabilitation, both in conventional and technology-assisted settings.

RECENT FINDINGS

The most consistent evidence in terms of improvement or preservation of circumscribed cognitive scores in MS patients comes from moderately sampled randomized clinical trials on multimodal approaches that combine conventional or computerized neuropsychological training with psychoeducation or cognitive behavioral therapy. Disease-modifying treatments also appear to have beneficial effects in preventing or attenuating cognitive decline, whereas there is little evidence for agents such as donepezil or stimulants. Finally, physical exercise may yield some cognitive improvement in MS patients. Despite substantial and often promising research efforts, there is a lack of validated and widely accepted clinical procedures for cognitive neurorehabilitation in MS. Development of such approaches will require collaborative efforts towards the design of interventions that are fundamentally inspired by cognitive neuroscience, potentially guided by neuroimaging, and composed of conventional neuropsychological training and cognitive behavioral therapy as well as physical exercise and therapeutic video games. Subsequently, large-scale validation will be needed with meaningful outcome measures reflecting transfer to everyday cognitive function and maintenance of training effects.

Identifying and Quantifying Neurological Disability via Smartphone

Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.

Toward a low-cost, in-home, telemedicine-enabled assessment of disability in multiple sclerosis

Background:

Remote assessment of neurological disability in people with multiple sclerosis (MS) could improve access to clinical care and efficiency of clinical research.

Objective:

To develop and validate a telemedicine-based MS disability examination that does not require an in-home examiner.

Methods:

Adults with MS were recruited after a standardized in-person Expanded Disability Status Scale (EDSS) evaluation, and within 1 week underwent a blinded televideo-enabled EDSS examination with a different clinician. EDSS and tele-EDSS scores were compared.

Results:

Overall, 41 adults participated (mean (standard deviation (SD)) age: 47.0 years (11.6); median EDSS: 2 (range: 0–7)); 37 required no in-home assistance for the tele-EDSS evaluation (e.g. help positioning camera). Mean difference between EDSS and tele-EDSS was 0.34 (95% confidence interval (CI): 0.07–0.61). For 88% of evaluations, tele-EDSS and EDSS scores were within 1 point (similar to reported in-person inter-rater differences). Unweighted kappa for agreement within 0.5 point was 0.72. Correlation for individual functional systems (FS) ranged from modest (vision: 0.37) to high (bowel/bladder: 0.79). Overall correlation between EDSS and tele-EDSS was 0.89 ( p < 0.0001); and 0.98 ( p < 0.0001) at EDSS range: 4–7.

Conclusion:

In this proof of principle study, disability evaluation in mild to moderate MS is feasible using telemedicine without an aide at the patient’s location.

Multiple Sclerosis in the Contemporary Age: Understanding the Millennial Patient with Multiple Sclerosis to Create Next-Generation Care

The average age of onset of multiple sclerosis (MS) is between 20 and 40 years of age. Therefore, most new patients diagnosed with MS within the next 10 to 15 years will be from the millennial generation, representing those born between 1982 and 2000. Certain preferences and trends of this contemporary generation will present new challenges to the MS physician and effective MS care. By first understanding these challenges, relevant and successful solutions can be created to craft a system of care that best benefits the millennial patient with MS.

Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis

OBJECTIVE

The number of mobile applications addressing health topics is increasing. Whether these apps underwent scientific evaluation is unclear. We comprehensively assessed papers investigating the diagnostic value of available diagnostic health applications using inbuilt smartphone sensors.

METHODS

Systematic Review-MEDLINE, Scopus, Web of Science inclusive Medical Informatics and Business Source Premier (by citation of reference) were searched from inception until 15 December 2016. Checking of reference lists of review articles and of included articles complemented electronic searches. We included all studies investigating a health application that used inbuilt sensors of a smartphone for diagnosis of disease. The methodological quality of 11 studies used in an exploratory meta-analysis was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the reporting quality with the 'STAndards for the Reporting of Diagnostic accuracy studies' (STARD) statement. Sensitivity and specificity of studies reporting two-by-two tables were calculated and summarised.

RESULTS

We screened 3296 references for eligibility. Eleven studies, most of them assessing melanoma screening apps, reported 17 two-by-two tables. Quality assessment revealed high risk of bias in all studies. Included papers studied 1048 subjects (758 with the target conditions and 290 healthy volunteers). Overall, the summary estimate for sensitivity was 0.82 (95 % CI 0.56 to 0.94) and 0.89 (95 %CI 0.70 to 0.97) for specificity.

CONCLUSIONS

The diagnostic evidence of available health apps on Apple's and Google's app stores is scarce. Consumers and healthcare professionals should be aware of this when using or recommending them.

PROSPERO REGISTRATION NUMBER

42016033049.

The Berlin Treatment Algorithm: recommendations for tailored innovative therapeutic strategies for multiple sclerosis-related fatigue

More than 80% of multiple sclerosis (MS) patients suffer from fatigue. Despite this, there are few therapeutic options and evidence-based pharmacological treatments are lacking. The associated societal burden is substantial (MS fatigue is a major reason for part-time employment or early retirement), and at least one out of four MS patients view fatigue as the most burdensome symptom of their disease. The mechanisms underlying MS-related fatigue are poorly understood, and objective criteria for distinguishing and evaluating levels of fatigue and tiredness have not yet been developed. A further complication is that both symptoms may also be unspecific indicators of many other diseases (including depression, sleep disorders, anemia, renal failure, liver diseases, chronic obstructive pulmonary disease, drug side effects, recent MS relapses, infections, nocturia, cancer, thyroid hypofunction, lack of physical exercise). This paper reviews current treatment options of MS-related fatigue in order to establish an individualized therapeutic strategy that factors in existing comorbid disorders. To ensure that such a strategy can also be easily and widely implemented, a comprehensive approach is needed, which ideally takes into account all other possible causes and which is moreover cost efficient. Using a diagnostic interview, depressive disorders, sleep disorders and side effects of the medication should be identified and addressed. All MS patients suffering from fatigue should fill out the Modified Fatigue Impact Scale, Epworth Sleepiness Scale, the Beck Depression Inventory (or a similar depression scale), and the Pittsburgh Sleep Quality Index (or the Insomnia Severity Index). In some patients, polygraphic or polysomnographic investigations should be performed. The treatment of underlying sleep disorders, drug therapy with alfacalcidol or fampridine, exercise therapy, and cognitive behavioral therapy-based interventions may be effective against MS-related fatigue. The objectives of this article are to identify the reasons for fatigue in patients suffering from multiple sclerosis and to introduce individually tailored treatment approaches. Moreover, this paper focuses on current knowledge about MS-related fatigue in relation to brain atrophy and lesions, cognition, disease course, and other findings in an attempt to identify future research directions.