MS symptoms affect many functional domains. Knowing the specific impact of symptoms on health-related quality of life (HRQoL) is vital for successful disease and symptom management in MS. We aimed at investigating how specific MS symptoms contribute to the disease burden in individuals and from a population perspective. We included 855 Swiss Multiple Sclerosis Registry participants with a relapsing-remitting form (RRMS) or a progressive form (PMS). HRQoL was measured with the EuroQol 5-Dimension EQ-5D-index and EQ-Visual Analogue Scale (EQ-VAS) on 0-100% scales. Their associations with 20 symptoms, socio-demographic and clinical information were explored in median regression models, stratified by RRMS and PMS. We included 611 participants with RRMS and 244 with PMS. In RRMS, gait (-6.5%) and balance problems (-5.1%) had the largest EQ-5D-index reductions, and were also important at the population level (frequencies 45% and 52%). Fatigue, depression, and spasticity (frequencies 74.1%, 31%, 38%) also contributed to the population disease burden. In PMS, spasticity, paralysis, and bowel problems had the largest impact on EQ-5D-index, both at the individual and population levels. The largest impact on EQ-VAS at population level was associated in RRMS with balance problems, depression, dizziness, and spasticity, while in PMS with weakness, pain, and paralysis. While HRQoL at population level is most affected by balance problems, spasticity, and depression in RRMS, the biggest HRQoL losses in PMS are caused by spasticity, paralysis, weakness, and pain. Many symptoms with the largest effects in individuals substantially contribute to the population disease burden.

Clinician-assessed Expanded Disease Status Scale (EDSS) is gold standard in clinical investigations but normally unavailable in population-based, patient-centred MS-studies. Our objective was to develop a self-reported gait measure reflecting EDSS-categories. We developed the self-reported disability status scale (SRDSS) with three categories (≤3.5, 4-6.5, ≥7) based on three mobility-related questions. The SRDSS was determined for 173 persons with MS and validated against clinical EDSS to calculate sensitivity and specificity. Accuracy was 88.4% (153 correctly classified) and weighted kappa 0.73 (0.62-0.84). Sensitivity/specificity-pairs were 94.5%/77.8%, 69.0%/94.7% and 100%/98.2% for SRDSS ≤3.5, 4-6.5 and ≥7, respectively. Self-reported SRDSS approximates EDSS-categories well and fosters comparability between clinical and population-based studies.

Introduction: The availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population’s previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies. Methods: Using Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics. Results: Our analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently. Conclusions: Only about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.

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