Validation of a self-completed Dystonia Non-Motor Symptoms Questionnaire

Objetive: To develop and validate a novel 14-item self-completed questionnaire (in English and German) enquiring about the presence of non-motor symptoms (NMS) during the past month in patients with craniocervical dystonia in an international multicenter study. Methods: The Dystonia Non-Motor Symptoms Questionnaire (DNMSQuest) covers seven domains including sleep, autonomic symptoms, fatigue, emotional well-being, stigma, activities of daily living, sensory symptoms. The feasibility and clinimetric attributes were analyzed. Results: Data from 194 patients with CD (65.6% female, mean age 58.96 ± 12.17 years, duration of disease 11.95 ± 9.40 years) and 102 age- and sex-matched healthy controls (66.7% female, mean age 55.67 ± 17.62 years) were collected from centres in Germany and the UK. The median total NMS score in CD patients was 5 (interquartile range 3-7), significantly higher than in healthy controls with 1 (interquartile range 0.75-2.25) (P < 0.001, Mann-Whitney U-test). Evidence for intercorrelation and convergent validity is shown by moderate to high correlations of total DNMSQuest score with motor symptom severity (TWSTRS: rs  = 0.61), clinical global impression (rs  = 0.40), and health-related quality of life measures: CDQ-24 (rs  = 0.74), EQ-5D index (rs  = -0.59), and scale (rs  = -0.49) (all P < 0.001). Data quality and acceptability was very satisfactory. Interpretation: The DNMSQuest, a patient self-completed questionnaire for NMS assessment in CD patients, appears robust, reproducible, and valid in clinical practice showing a tangible impact of NMS on quality of life in CD. As there is no specific, comprehensive, validated tool to assess the burden of NMS in dystonia, the DNMSQuest can bridge this gap and could easily be integrated into clinical practice.S

A review of current treatment strategies for restless legs syndrome (Willis-Ekbom disease)

Restless legs syndrome (RLS), recently renamed Willis-Ekbom disease (WED), is a common movement disorder. It is characterised by the need to move mainly the legs due to uncomfortable, sometimes painful sensations in the legs, which have a diurnal variation and a release with movement. Management is complex. First, centres should establish the severity of RLS using a simple 10-item RLS severity rating scale (IRLS). They should also exclude secondary causes, in particular ensuring normal iron levels. Mild cases can be managed by lifestyle changes, but patients with a IRLS score above 15 usually require pharmacological treatment. Dopaminergic therapies remain the mainstay of medical therapies, with recent evidence suggesting opioids may be particularly effective. This article focuses on the different treatment strategies in RLS, their associated complications and ways to manage them

Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning

Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings