A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care.

BACKGROUND: People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. OBJECTIVE: This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. METHODS: During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. RESULTS: MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. CONCLUSIONS: Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense-funded feasibility and efficacy trial, which is now underway. TRIAL REGISTRATION: ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x.

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.

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.

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.

Characteristics of multiple sclerosis and demyelinating disease in an Asian American population.

BACKGROUND: Race and ancestry influence the course of multiple sclerosis (MS). OBJECTIVES: Explore clinical characteristics of MS and neuromyelitis optica spectrum disorder (NMOSD) in Asian American patients. METHODS: Chart review was performed for 282 adults with demyelinating disease who self-identified as Asian at a single North American MS center. Demographics and clinical characteristics were compared to non-Asian MS patients and by region of Asian ancestry. RESULTS: Region of ancestry was known for 181 patients. Most (94.7%) preferred English, but fewer East Asian patients did (80%, p = 0.0001). South Asian patients had higher neighborhood household income (p = 0.002). Diagnoses included MS (76.2%) and NMOSD (13.8%). More patients with NMOSD than MS were East and Southeast Asian (p = 0.004). For MS patients, optic nerve and spinal cord involvement were similar across regions of ancestry. Asian MS patients were younger at symptom onset and diagnosis than non-Asian MS patients. MS Severity Scale scores were similar to non-Asian MS patients but worse among Southeast Asians (p = 0.006). CONCLUSIONS: MS severity was similar between Asian American patients and non-Asian patients. Region of ancestry was associated with differences in sociodemographics and MS severity. Further research is needed to uncover genetic, socioeconomic, or environmental factors causing these differences.