Cognitive function influences cognitive-motor interference during dual task walking in multiple sclerosis.

BACKGROUND: Both physical and cognitive impairments are common in people with multiple sclerosis (PwMS). Performing a cognitive task while walking (i.e., dual-task walking) can introduce cognitive-motor interference (CMI), resulting in changes in walking performance. The association between the levels of cognitive impairment and of CMI in MS remains unclear. OBJECTIVES: To examine the association between cognitive functioning and differences in walking performance arise between single- and dual-task walking. METHODS: Ninety-five PwMS performed self-preferred pace walking and dual-task walking. The gait parameters recorded were used to compute dual task costs (DTC) as a metric of CMI. Cognitive functioning was assessed using Match, an unsupervised test developed based on the Symbol Digit Modalities Test. Participants were categorized as higher (HCF) and lower cognitive functioning (LCF) based on a Match z-score < -1.5. RESULTS: LCF group had elevated DTC for stride velocity, relative to the HCF group. Higher DTC for stride velocity was associated with lower cognition, as assessed by Match test. CONCLUSION: The findings support the hypothesis that CMI is associated with cognitive functioning in PwMS.

A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.

BACKGROUND: Falls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient's specific context. OBJECTIVE: This study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts. METHODS: Stakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users' needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits. RESULTS: A sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility-with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient's community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring). CONCLUSIONS: To our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record-embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences.

Fall Assessment and Monitoring in People With Multiple Sclerosis: A Practical Evidence-Based Review for Clinicians.

Purpose of Review: Falls occur in more than half of all people with multiple sclerosis (MS) but tend to be underdiagnosed and underreported in clinical encounters. This narrative review aims to summarize evidence-based approaches for evaluating fall risk and proven treatment strategies to reduce falling in people with MS to improve care for people with MS and to enhance interprofessional care coordination between treating neurologic and physical therapy (PT) teams. Recent Findings: Screening not just for falls but for near-falls as well because fear of falling can improve fall assessment and identify patients who may benefit from fall prevention interventions. A number of barriers, including time constraints during visits and the fallacy that falling is inevitable in MS, can limit clinician awareness about patient falls and delay timely referral to PT. Consultation with physical therapists for individualized fall prevention treatment can reduce risk of falling. Interventional studies have also shown that PT-guided exercise programs improve balance confidence in people with MS. However, people with MS are often under-referred to PT by treating clinicians. Summary: A clinical approach is provided to summarize practical, accessible, evidence-based, low-burden measurements and interventions likely to improve ascertainment of patients at risk of falling and optimize timely PT referral and treatment.

Association of daily physical activity with brain volumes and cervical spinal cord areas in multiple sclerosis.

BACKGROUND: Remote activity monitoring has the potential to evaluate real-world, motor function, and disability at home. The relationships of daily physical activity with spinal cord white matter and gray matter (GM) areas, multiple sclerosis (MS) disability and leg function, are unknown. OBJECTIVE: Evaluate the association of structural central nervous system pathology with ambulatory disability. METHODS: Fifty adults with progressive or relapsing MS with motor disability who could walk >2 minutes were assessed using clinician-evaluated, patient-reported outcomes, and quantitative brain and spinal cord magnetic resonance imaging (MRI) measures. Fitbit Flex2, worn on the non-dominant wrist, remotely assessed activity over 30 days. Univariate and multivariate analyses were performed to assess correlations between physical activity and other disability metrics. RESULTS: Mean age was 53.3 years and median Expanded Disability Status Scale (EDSS) was 4.0. Average daily step counts (STEPS) were highly correlated with EDSS and walking measures. Greater STEPS were significantly correlated with greater C2-C3 spinal cord GM areas (ρ = 0.39, p = 0.04), total cord area (TCA; ρ = 0.35, p = 0.04), and cortical GM volume (ρ = 0.32, p = 0.04). CONCLUSION: These results provide preliminary evidence that spinal cord GM area is a neuroanatomical substrate associated with STEPS. STEPS could serve as a proxy to alert clinicians and researchers to possible changes in structural nervous system pathology.

Accessibility to Telerehabilitation Services for People With Multiple Sclerosis: Analysis of Barriers and Limitations.

BACKGROUND: In multiple sclerosis (MS), telemedicine improves access to specialized medical care; however, barriers remain, including universal access and effective implementation. Focusing on telerehabilitation, ie, remotely delivered physical therapy, our goal was to identify barriers to telerehabilitation implementation and factors associated with patients completing telerehabilitation physical therapy treatment. METHODS: Quantitative data included a review of electronic health records of patients with MS treated at the University of California San Francisco Physical Therapy Faculty Practice. We extracted demographic, clinical, and transit-related factors. For patients who scheduled an initial evaluation, we recorded the number of follow-ups, cancellations, completed physical therapy goals, and discharges. Qualitative data included interviews with 3 board-certified neurologic physical therapists and patients' perspectives recorded in the subjective portion of physical therapy notes. RESULTS: We identified 111 patients with at least 1 visit (in-person or telerehabilitation) to physical therapy (82 women; mean ± SD age, 54.2 ± 12.7 years). Patients with no disability (Expanded Disability Status Scale [EDSS] score, 0) were 73% less likely to schedule a follow-up appointment (in-person or telerehabilitation) than those with some disability (EDSS score, >0) (odds ratio, 0.27; 95% CI, 0.09-0.75; P = .012). Neurologic physical therapists identified reduced travel burden and scheduling flexibility as benefits of telerehabilitation vs in-person visits. Barriers to telerehabilitation included low technological literacy, cognitive impairment, and fall risk. Patients described scheduling conflicts and pain/illness as barriers to telerehabilitation. CONCLUSIONS: Patients with no disability were least likely to complete physical therapy treatment via telerehabilitation. Although both benefits and barriers to completing physical therapy via telerehabilitation are present, the neurologic physical therapists were supportive of a hybrid model for physical therapy.

The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review.

Fatigue is one of the most common multiple sclerosis (MS) symptoms. Despite this, monitoring and measuring fatigue (subjective lack of energy)- and fatigability (objectively measurable and quantifiable performance decline)- in people with MS have remained challenging. Traditionally, administration of self-report questionnaires during in-person visits has been used to measure fatigue. However, remote measurement and monitoring of fatigue and fatigability have become feasible in the past decade. Traditional questionnaires can be administered through the web in any setting. The ubiquitous availability of smartphones allows for momentary and frequent measurement of MS fatigue in the ecological home-setting. This approach reduces the recall bias inherent in many traditional questionnaires and demonstrates the fluctuation of fatigue that cannot be captured by standard measures. Wearable devices can assess patients' fatigability and activity levels, often influenced by the severity of subjective fatigue. Remote monitoring of fatigue, fatigability, and activity in real-world situations can facilitate quantifying symptom-severity in clinical and research settings. Combining remote measures of fatigue as well as objective fatigability in a single construct, composite score, may provide a more comprehensive outcome. The more granular data obtained through remote monitoring techniques may also help with the development of interventions aimed at improving fatigue and lowering the burden of this disabling symptom.

Making Every Step Count: Minute-by-Minute Characterization of Step Counts Augments Remote Activity Monitoring in People With Multiple Sclerosis.

Background: Ambulatory disability is common in people with multiple sclerosis (MS). Remote monitoring using average daily step count (STEPS) can assess physical activity (activity) and disability in MS. STEPS correlates with conventional metrics such as the expanded disability status scale (Expanded Disability Status Scale; EDSS), Timed-25 Foot walk (T25FW) and timed up and go (TUG). However, while STEPS as a summative measure characterizes the number of steps taken over a day, it does not reflect variability and intensity of activity. Objectives: Novel analytical methods were developed to describe how individuals spends time in various activity levels (e.g., continuous low versus short bouts of high) and the proportion of time spent at each activity level. Methods: 94 people with MS spanning the range of ambulatory impairment (unaffected to requiring bilateral assistance) were recruited into FITriMS study and asked to wear a Fitbit continuously for 1-year. Parametric distributions were fit to minute-by-minute step data. Adjusted R2 values for regressions between distributional fit parameters and STEPS with EDSS, TUG, T25FW and the patient-reported 12-item MS Walking scale (MSWS-12) were calculated over the first 4-weeks, adjusting for sex, age and disease duration. Results: Distributional fits determined that the best statistically-valid model across all subjects was a 3-compartment Gaussian Mixture Model (GMM) that characterizes the step behavior within 3 levels of activity: high, moderate and low. The correlation of GMM parameters for baseline step count measures with clinical assessments was improved when compared with STEPS (adjusted R2 values GMM vs. STEPS: TUG: 0.536 vs. 0.419, T25FW: 0.489 vs. 0.402, MSWS-12: 0.383 vs. 0.378, EDSS: 0.557 vs. 0.465). The GMM correlated more strongly (Kruskal-Wallis: p = 0.0001) than STEPS and gave further information not included in STEPS. Conclusions: Individuals' step distributions follow a 3-compartment GMM that better correlates with clinic-based performance measures compared with STEPS. These data support the existence of high-moderate-low levels of activity. GMM provides an interpretable framework to better understand the association between different levels of activity and clinical metrics and allows further analysis of walking behavior that takes step distribution and proportion of time at three levels of intensity into account.

Challenges to Longitudinal Characterization of Lower Urinary Tract Dysfunction in Multiple Sclerosis.

BACKGROUND: Neurogenic lower urinary tract dysfunction (LUTD) results in lower urinary tract symptoms (LUTS) that impact quality of life in people with multiple sclerosis (PwMS). The risk factors and the contribution of LUTD to multiple sclerosis (MS) disease progression are under-researched. OBJECTIVE: To identify clinical and demographic predictors of LUTS in PwMS and gaps in clinical ascertainment. METHODS: Participants were adults with MS enrolled in a prospective, multicenter study (SUMMIT, N=802), including a subset of N = 258 patients in the UCSF EPIC study for whom medical records were further reviewed. Demographic (age, sex, race, ethnicity), clinical (disease duration, MS type), and female-specific reproductive factors (e.g., parity) were evaluated to determine associations with bowel/bladder functional system score. Participants' medical records were analyzed to understand the patterns of LUTS ascertainment by physicians and the specific contribution of LUTS to overall bowel/bladder functional system scores. RESULTS: 802 participants (71.3% female) contributed to these analyses. Higher bowel/bladder functional system scores, indicating worsening symptoms and function, were significantly associated with female sex (p=0.001) and progressive MS type (p≤ 0.001). In the EPIC participants, female-specific reproductive exposures (parity, menopause) were not significantly associated with worse bowel/bladder functional system scores. Most (98%) bowel/bladder functional system scores reflected the severity of LUTS (relative to bowel dysfunction). LUTS were under-ascertained clinically, and more so in women (X2 = 5.02, p=0.08). CONCLUSIONS: Female sex and MS type are predictive of worsening LUTS. Symptoms may be less likely to be ascertained by clinicians in females compared to males.

Identifying falls remotely in people with multiple sclerosis.

BACKGROUND: Falling is common in people with multiple sclerosis (MS) but tends to be under-ascertained and under-treated. OBJECTIVE: To evaluate fall risk in people with MS. METHODS: Ninety-four people with MS, able to walk > 2 min with or without an assistive device (Expanded Disability Status Scale (EDSS ≤ 6.5) were recruited. Clinic-based measures were recorded at baseline and 1 year. Patient-reported outcomes (PROs), including a fall survey and the MS Walking Scale (MSWS-12), were completed at baseline, 1.5, 3, 6, 9, and 12 months. Average daily step counts (STEPS) were recorded using a wrist-worn accelerometer. RESULTS: 50/94 participants (53.2%) reported falling at least once. Only 56% of participants who reported a fall on research questionnaires had medical-record documented falls. Fallers had greater disability [median EDSS 5.5 (IQR 4.0-6.0) versus 2.5 (IQR 1.5-4.0), p < 0.001], were more likely to have progressive MS (p = 0.003), and took fewer STEPS (mean difference - 1,979, p = 0.007) than Non-Fallers. Stepwise regression revealed MSWS-12 as a major predictor of future falls. CONCLUSIONS: Falling is common in people with MS, under-reported, and under-ascertained by neurologists in clinic. Multimodal fall screening in clinic and remotely may help improve patient care by identifying those at greatest risk, allowing for timely intervention and referral to specialized physical rehabilitation.

Research interrupted: The impact of the COVID-19 pandemic on multiple sclerosis research in the field of rehabilitation and quality of life.

BACKGROUND: The COVID-19 pandemic has likely had a negative impact on rehabilitation and quality of life (QoL) research in multiple sclerosis (MS). METHOD: We explored perceived barriers to research among 87 researchers, representing 18 countries, both prior to and since COVID-19. RESULTS: A Wilcoxon signed-rank test found that significantly more researchers reported experiencing barriers to research since the onset of the pandemic compared to pre-COVID-19 (p < .001), with 78% of respondents reporting at least some barriers since COVID-19. The most commonly-cited barriers related to participant access (n = 38) and interruptions/delays to projects (n = 19). Although no gender differences were found in the number of barriers reported, female respondents were more likely to cite time or competing demands as barriers to research. Females were also more likely to perceive being negatively impacted by the pandemic compared to other genders (p = .007). CONCLUSIONS: Implications for the future landscape of rehabilitation research in MS are discussed.

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.

Underutilization of physical therapy for symptomatic women with MS during and following pregnancy.

BACKGROUND: Many patients with MS continue to have symptoms of their disease even when inflammatory activity is reduced by DMTs. Although disease activity tends to be reduced during pregnancy - especially in the third trimester - women with MS can experience ongoing symptoms during pregnancy, or new ones in the immediate postpartum period, that degrade quality of life. While many MS-related and postpartum symptoms can be improved with physical therapy (PT), there are currently no guidelines on pregnancy-related rehabilitation in MS. In this analysis, we evaluated the prevalence of PT-amenable symptoms and patterns of PT referrals in a cohort of UCSF MS Clinic patients who became pregnant. METHODS: We extracted electronic medical records (EMR) data for the year before conception, during pregnancy, and year postpartum for women with MS cared for at UCSF between 09-2005 and 08-2019. This included clinical visits, MS therapies and symptoms (as defined by the National MS Society). PT and pelvic floor PT orders and notes were also extracted. RESULTS: We included 142 live birth pregnancies from 118 women. During the course of their pregnancy and within the year postpartum, 107 women (75.4%) reported at least one PT-amenable symptom. A total of 30 (28.0%) referrals were made to PT, with attendance confirmed for 10 (33.3% of referrals). Symptoms most commonly triggering a referral for PT evaluation were numbness and urinary incontinence. Falls were reported after 10 of the pregnancies; 4 resulted in a referral to PT. Forty-one women reported urinary incontinence: 11 (26.8%) were referred to PT, and 2 to pelvic floor PT. Nineteen women experienced a documented relapse during pregnancy and/or postpartum: 11 received a PT referral, and 4 attended PT. CONCLUSIONS: While women with MS recorded at least 1 PT-amenable symptom during or following 75.4% of their pregnancies, only 28% of these were referred to PT - and only a third attended PT. Of significance was the 4.9% referral rate for pelvic floor PT in postpartum women with a record of urinary incontinence. Pelvic floor PT is a mainstay of general postpartum care in many European countries. These data illustrate critical gaps in rehabilitation referral, access and use at the intersection of neurological conditions and pregnancy in a large US-based MS clinic. They lend support for quality improvement efforts to improve care pathways and for telerehabilitation innovations to reduce barriers to access and improve synergistic care between PT, MD and urologic care.

A Precision Medicine Tool for Patients With Multiple Sclerosis (the Open MS BioScreen): Human-Centered Design and Development.

BACKGROUND: Patients with multiple sclerosis (MS) face several challenges in accessing clinical tools to help them monitor, understand, and make meaningful decisions about their disease course. The University of California San Francisco MS BioScreen is a web-based precision medicine tool initially designed to be clinician facing. We aimed to design a second, openly available tool, Open MS BioScreen, that would be accessible, understandable, and actionable by people with MS. OBJECTIVE: This study aimed to describe the human-centered design and development approach (inspiration, ideation, and implementation) for creating the Open MS BioScreen platform. METHODS: We planned an iterative and cyclical development process that included stakeholder engagement and iterative feedback from users. Stakeholders included patients with MS along with their caregivers and family members, MS experts, generalist clinicians, industry representatives, and advocacy experts. Users consisted of anyone who wants to track MS measurements over time and access openly available tools for people with MS. Phase I (inspiration) consisted of empathizing with users and defining the problem. We sought to understand the main challenges faced by patients and clinicians and what they would want to see in a web-based app. In phase II (ideation), our multidisciplinary team discussed approaches to capture, display, and make sense of user data. Then, we prototyped a series of mock-ups to solicit feedback from clinicians and people with MS. In phase III (implementation), we incorporated all concepts to test and iterate a minimally viable product. We then gathered feedback through an agile development process. The design and development were cyclical-many times throughout the process, we went back to the drawing board. RESULTS: This human-centered approach generated an openly available, web-based app through which patients with MS, their clinicians, and their caregivers can access the site and create an account. Users can enter information about their MS (basic level as well as more advanced concepts), visualize their data longitudinally, access a series of algorithms designed to empower them to make decisions about their treatments, and enter data from wearable devices to encourage realistic goal setting about their ambulatory activity. Agile development will allow us to continue to incorporate precision medicine tools, as these are validated in the clinical research arena. CONCLUSIONS: After engaging intended users into the iterative human-centered design of the Open MS BioScreen, we will now monitor the adaptation and dissemination of the tool as we expand its functionality and reach. The insights generated from this approach can be applied to the development of a number of self-tracking, self-management, and user engagement tools for patients with chronic conditions.

E-health and multiple sclerosis.

PURPOSE OF REVIEW: To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis. RECENT FINDINGS: The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence. SUMMARY: Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system.

Validation of a consumer-grade activity monitor for continuous daily activity monitoring in individuals with multiple sclerosis.

BACKGROUND: Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. OBJECTIVES: To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. METHODS: Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. RESULTS: Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p < 0.01), although the ICC was high (0.98) indicating that the devices were likely measuring the same kind of activity. CONCLUSIONS: Steps from Flex and Flex2 can be used interchangeably. Differences in total step count between ActiGraph and Flex devices can make cross-device comparisons of numerical step-counts challenging particularly for faster walkers.

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, ß = -13.09; P < .001; Timed-Up-and-Go, ß = -9.25; P < .001) and patient-reported outcomes (12-item Multiple Sclerosis Walking Scale, ß = -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.