This dataset contains anonymized survey data from a study examining determinants of digital health acceptance among people with Multiple Sclerosis (MS) compared to individuals with other chronic conditions. The survey was conducted in Winter 2024/2025 and includes established Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) constructs, such as Perceived Usefulness, Perceived Ease of Use, Behavioral Intention, Social Influence, Trust in Technology, and Technological Anxiety. Additional measures capture symptom severity, use of AI-supported health applications, and wearable sensor use. The dataset supports analyses of emotional and disease-related moderators of digital health adoption, providing the empirical basis for an exploratory Extended Disease-Specific Technology Acceptance Model (D-TAM) for MS. All data are fully anonymized and contain no personally identifiable information. The associated manuscript is currently under review and has not yet been published. Researchers may use the dataset for secondary analyses, replication studies, or methodological benchmarking.