Digital Symptom Tracking Reveals How Multiple Sclerosis Patients Really Fare Day to Day

Multiple sclerosis is a disease of accumulation in slow motion. Relapses, fatigue, cognitive fog and mobility problems unfold over years, yet most clinical data about the condition are still captured in snapshots: a visit to the neurologist every few months, a standardized questionnaire completed in a waiting room, a magnetic resonance scan scheduled at intervals that have little to do with how a patient actually feels on a Tuesday afternoon. A new longitudinal analysis published in PLOS Digital Health offers one of the most detailed real-world views yet of what people with multiple sclerosis experience between appointments, drawing on more than four thousand users of a German companion app called Brisa.

The study, led by Ann-Sophie Stratil and colleagues including Tjalf Ziemssen, analyzed retrospective data from Brisa, a prescription-free digital health application designed to support people with multiple sclerosis throughout their patient journey. The app allows users to log symptoms on a simple emoticon-based scale ranging from one, indicating poor condition, to five, indicating excellent condition, and to complete validated patient-reported outcome measures, or PROs. These included the Modified Fatigue Impact Scale for fatigue, the Patient-Determined Disease Steps for self-assessed mobility, the five-item Perceived Deficits Questionnaire for cognitive symptoms, the Incontinence Voiding Intensity Scale for bladder symptoms, and the Pain Effects Scale for pain. Together, these instruments cover many of the symptoms that most strongly shape quality of life in multiple sclerosis but that often receive less clinical attention than relapse counts or lesion load.

The researchers set strict inclusion criteria to keep their analysis clean. Users had to be at least eighteen years old, had to have reported use of a disease-modifying therapy classified into one of three categories according to German neurological society guidelines, and had to have remained on the same therapy without switching during the observation window. Category one covered low-efficacy treatments, category two medium-efficacy treatments, and category three the high-efficacy agents that have increasingly become first-line options in modern multiple sclerosis care. Users diagnosed within the previous zero to one years were classified as newly diagnosed, allowing the team to track how treatment patterns are shifting at the very start of the disease course.

The resulting cohort painted a familiar demographic picture of the multiple sclerosis population, but in unusually fine grain. Among 4,194 users, 80.8 percent were female, consistent with the well-documented female predominance of the disease. The largest age group, 30.5 percent of users, was between twenty-six and thirty-five years old, and 77.4 percent reported a diagnosis of relapsing-remitting multiple sclerosis, the form characterized by discrete attacks followed by periods of recovery. Notably, 27.9 percent of the cohort fell into the newly diagnosed category, meaning more than one in four app users were within their first year of diagnosis, a population whose treatment decisions are among the most consequential in the entire disease course.

One of the most striking findings concerned those early treatment decisions. Among newly diagnosed users, the proportion receiving category three, high-efficacy disease-modifying therapies increased significantly between 2023 and 2024 and 2025, a difference the authors report as statistically robust with a p-value below 0.0001. This shift mirrors a broader transformation in the field, in which neurologists have moved away from the traditional escalation approach, starting with milder drugs and reserving potent agents for treatment failures, toward an early high-efficacy strategy that aims to suppress inflammatory disease activity before irreversible damage accumulates. That this trend is visible in self-reported app data, rather than in registry data curated by clinics, suggests digital platforms can capture prescribing patterns as they happen in routine care.

Engagement, the perennial Achilles heel of digital health, was treated with unusual statistical rigor. The researchers defined engagement as any screen activity within the app and disengagement as more than 180 consecutive days without any activity, a threshold long enough to distinguish genuine abandonment from ordinary lapses in use. They then applied Cox proportional hazards regression, a survival-analysis technique that estimates how different characteristics change the hazard of an event over time, to identify which users were most likely to stay active. The results challenged some common assumptions about who benefits from health apps.

Three groups showed a significantly lower risk of disengagement. Users receiving high-efficacy, category three therapies were more likely to remain engaged than those on category one treatments, possibly reflecting a greater sense of investment in monitoring their disease under aggressive treatment. Users aged thirty-six and older stayed engaged longer than those aged eighteen to twenty-five, suggesting the youngest users, often assumed to be the most digitally native, may actually be the hardest to retain. And newly diagnosed users showed lower disengagement risk than those diagnosed two or more years earlier, indicating that the period immediately after diagnosis, when patients are actively seeking information and control, is when digital tools have their strongest foothold. For developers and clinicians alike, this is a roadmap: engagement strategies should be tailored to the newly diagnosed, to older users, and to those on intensive treatment regimens.

Overall, about 76 percent of users tracked at least one symptom or patient-reported outcome, a remarkably high proportion for a voluntary health application. The longitudinal picture among users with at least one year of tracking was one of stability: symptom ratings and PRO scores remained largely unchanged over time. At first glance, stability might seem like a null result, but in the context of multiple sclerosis it is arguably the most reassuring finding possible. It suggests that the population actively using the app, many of them on modern high-efficacy therapies, was not experiencing progressive worsening of fatigue, mobility, cognition, bladder function or pain during the observation period. It also demonstrates that these instruments, designed for clinical settings, produce coherent, trackable data when completed by patients at home on their own schedule.

The technical significance of the study lies in what it validates rather than in any single clinical result. Patient-reported outcomes collected in randomized trials are often criticized as artificial, captured under conditions that bear little resemblance to daily life. Real-world digital collection solves that problem but introduces others: attrition, self-selection, inconsistent use, and the question of whether people who download a health app are representative of patients in general. By characterizing exactly who uses the app, how long they stay, and how their self-reports trend over time, the Brisa analysis provides the kind of baseline that future studies will need to interpret digital symptom data responsibly. The finding that engagement itself is patterned by treatment intensity, age and disease duration is particularly valuable, because it tells researchers which covariates must be accounted for when app data are used to infer disease activity or treatment effects.

The implications reach beyond multiple sclerosis. Digital health applications are being prescribed and deployed across chronic disease care in Germany and across Europe, yet evidence about their real-world performance, who uses them, whether they keep users, and whether the data they generate are clinically meaningful, remains thin. This study offers a template for generating that evidence: define engagement operationally, apply survival analysis to retention, anchor symptom tracking to validated instruments, and report demographic and clinical characteristics of the user base transparently. If the shift toward high-efficacy therapy visible in this data continues, and if digital tools can reliably flag symptom changes between clinic visits, the quiet data stream from a phone in a patient’s pocket may become as important to managing multiple sclerosis as the annual MRI. For now, the Brisa analysis shows that thousands of patients are willing to report how they feel, day after day, and that what they report is stable, structured and scientifically usable, a foundation on which the next generation of remote disease monitoring can be built.

Subject of Research: Longitudinal real-world symptom tracking and patient-reported outcomes in users of a German digital health app for multiple sclerosis

Article Title: A longitudinal analysis of real world symptom tracking and patient-reported outcomes in Brisa, a German Digital Health App for multiple sclerosis

Article References: Stratil, A.-S., Papukchieva, S., Steinmetz-Späh, J., Joschko, N., Masanneck, L., Friedrich, B., & Ziemssen, T. (2026). A longitudinal analysis of real world symptom tracking and patient-reported outcomes in Brisa, a German Digital Health App for multiple sclerosis. PLOS Digital Health, 5(10), e0001751. https://doi.org/10.1371/journal.pdig.0001751

Image Credits: AI Generated

DOI: 10.1371/journal.pdig.0001751

Keywords: multiple sclerosis, digital health app, patient-reported outcomes, symptom tracking, real-world data, disease-modifying therapy, app engagement, high-efficacy treatment, newly diagnosed, longitudinal analysis, PLOS Digital Health, Cox regression