**Background:** Consumer-facing health technologies for chronic disease management are proliferating, yet most suffer from low adoption rates. Understanding a target population's prior technology exposure is critical for improving adoption. This study proposes a low-resource approach to capture and cluster technology exposure patterns, using Multiple Sclerosis (MS) as a case study. MS is a chronic autoimmune disease with highly individual and unpredictable progression, and people with MS have access to over 100 smartphone apps, yet evidence on usability and utility remains limited.
**Methods:** This nested cross-sectional analysis used data from the Swiss MS Registry (SMSR), a nationwide citizen-science registry. In October 2020, a SARS-CoV-2 survey was released that included four questions on digital tool use: (1) frequency of use of internet-connected devices (PC, tablet, smartphone, smartwatch) on a 7-item Likert scale; (2) areas of technology use (e.g., finding MS information, contacting healthcare providers, peer interaction) on a 7-item Likert scale; (3) types of leisure-time activities with electronic devices (e.g., information searches, chatting, video calls, apps) on a 7-item Likert scale; and (4) whether additional support was needed (yes/no). Responses were dichotomized to at least weekly use vs. less than weekly/never. Exploratory factorial analysis was conducted following Howard's recommendations, with factor selection based on scree plots (Eigenvalues >1) and oblique Oblimin rotation. Factor loadings were selected using the 0.4–0.2 rule. K-means clustering was applied to individual-level factor scores, with the number of groups determined by the Calinski-Harabasz pseudo-F and starting points selected via Wards-linkage. Clusters were compared across sociodemographic and health status characteristics.
**Key Results:** Of 1039 survey respondents, 990 (95.3%) completed the digitalization questions and were included. The sample was 74.3% female, with a median age of 50 years (IQR: 41–58) and median diagnosis year of 2010 (IQR: 2002–2015). MS types included 63.9% relapsing-remitting, 10.9% primary-progressive, 17.3% secondary-progressive, 4.2% transitional, and 3.9% unknown. Three clusters were identified: (1) Average users (n=772, 78%) — regularly used smartphones (99.7% weekly), apps (93.0%), and the internet (95.9%) for daily activities but rarely for health purposes (e.g., only 24.1% searched for MS information weekly, 0.4% communicated with healthcare providers). (2) Health-interested users (n=88, 8.9%) — similar daily technology use but much higher health-related use: 78.4% searched for MS information weekly, 67.0% communicated with healthcare providers, 83.0% made appointments, and 45.5% engaged in peer exchange. This cluster reported lower health-related quality of life (median EQ-5D index 83.2, VAS 70) and more symptoms (median 6) vs. average users (EQ-5D 90.7, VAS 80, median 3 symptoms). (3) Low-frequency users (n=130, 13.1%) — infrequent technology use: only 22.3% used smartphones weekly, 16.2% used apps, and 54.6% used the internet. This cluster was older (median birth year 1960), had higher disability (21.3% severe), more wheelchair use (43.1%), more progressive MS subtypes (33.8% secondary-progressive), and lower quality of life (median EQ-5D 81.5, VAS 70).
**Clinical Implications:** Only about 10% of the sample (health-interested users) regularly used technology for health-related purposes, suggesting most current MS-focused digital health tools may only appeal to a small, intrinsically motivated subset of patients. Disease burden appears to influence adoption in a non-linear way: low burden may reduce motivation for health-related technology use, while high burden may introduce physical and cognitive barriers. The low-frequency user cluster likely faces digital divide issues related to age, education, and disability. The authors recommend that digital health tools include diverse functionalities (e.g., not solely disability-focused), ensure compatibility with tablets/PCs, incorporate larger icons and verbal cues, and include family/peer support features to address the needs of differently affected patients. The proposed low-resource clustering approach may help developers better characterize prospective user populations and design more inclusive technologies.