Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disease characterized by progressive motor, behavioral, and cognitive decline, ending in death. Despite the discovery of the underlying genetic mutation more than 20 years ago, treatment remains focused on symptomatic management. Chorea, the most recognizable symptom, responds to medication that reduces dopaminergic neurotransmission. Psychiatric symptoms such as depression and anxiety may also respond well to symptomatic therapies. Unfortunately, many other symptoms do not respond to current treatments. Furthermore, high-quality evidence for treatment of HD in general remains limited. To date, there has been minimal success with identifying a disease-modifying therapy based upon molecular models. However, one of the emerging gene silencing techniques may provide a breakthrough in treating this devastating disease.

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson’s disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

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Background and ObjectivesChronic health conditions are influenced by social determinants of health (SDH) including neighborhood-linked markers of affluence. We explored whether neighborhood socioeconomic factors differ in people with different types of clinical movement disorders (MDs).MethodsWe conducted a retrospective study of patients seen in MD clinics at our center in 2021. Patient data were linked to the US National Neighborhood Data Archive linked to US census tract data. We evaluated variations in neighborhood socioeconomic factors across 8 different categories of MDs.ResultsCompared with the neighborhoods of patients with Parkinson disease, neighborhoods of patients with cerebellar ataxias, functional movement disorders, and Huntington disease were characterized by higher proportions of people earning less than 15,000 US dollars/year, people receiving public assistance, and people with less than a high school diploma.DiscussionNeighborhood-linked SDH vary among different MDs. These findings have implications for public health interventions aimed at improving the care of people affected by MDs.