Wearables have evolved from research-based instruments into commercially-available sensors that continuously capture real-world function and lived experiences. In multiple sclerosis (MS), accelerometry and ecological momentary assessments (EMAs) captured by wearables provide reliable indicators of health, activity, and sleep/wake patterns, holding strong potential to enrich clinical decision-making. Yet, these data have rarely been integrated into MS clinical workflows. In this course, we summarize current understanding of the relevance of patient-generated wearable data (activity, sleep, etc.) for clinical monitoring and care. We then review workflows for incorporating such data into point of care management through EHR-embedded dashboards. We review principles of human-centered design that is performed in collaboration with MS clinicians and patients, of implementation science, and of rigorous clinical validation. Through examples from our research and practice, we will demonstrate how ambulatory assessments of activity, sleep, fatigue, mood, and urological patterns might be deployed in practice using data-driven approaches to track and improve symptoms and functional outcomes. We share pragmatic lessons on which metrics are most clinically relevant at the point of care, visualization tools for quick trend reading, strategies for missing data, and approaches to clinician onboarding. Limitations of such approaches will also be discussed. Finally, we provide an adaptation checklist encompassing governance, device selection, EHR integration, visualization defaults, and staged rollout—prioritizing actionability over perfection. Together, these methods create a scalable pathway to embed wearables into MS care, enabling personalized, continuous, and treatment-relevant insights during clinical visits. Level of Information: IntermediateWearables have evolved from research-based instruments into commercially-available sensors that continuously capture real-world function and lived experiences. In multiple sclerosis (MS), accelerometry and ecological momentary assessments (EMAs) captured by wearables provide reliable indicators of health, activity, and sleep/wake patterns, holding strong potential to enrich clinical decision-making. Yet, these data have rarely been integrated into MS clinical workflows. In this course, we summarize current understanding of the relevance of patient-generated wearable data (activity, sleep, etc.) for clinical monitoring and care. We then review workflows for incorporating such data into point of care management through EHR-embedded dashboards. We review principles of human-centered design that is performed in collaboration with MS clinicians and patients, of implementation science, and of rigorous clinical validation. Through examples from our research and practice, we will demonstrate how ambulatory assessments of activity, sleep, fatigue, mood, and urological patterns might be deployed in practice using data-driven approaches to track and improve symptoms and functional outcomes. We share pragmatic lessons on which metrics are most clinically relevant at the point of care, visualization tools for quick trend reading, strategies for missing data, and approaches to clinician onboarding. Limitations of such approaches will also be discussed. Finally, we provide an adaptation checklist encompassing governance, device selection, EHR integration, visualization defaults, and staged rollout—prioritizing actionability over perfection. Together, these methods create a scalable pathway to embed wearables into MS care, enabling personalized, continuous, and treatment-relevant insights during clinical visits. Level of Information: Intermediate