Robert Ramsperger1, Stefan Meckler1, Tanja Heger2, Janet van Uem2, Svenja Hucker2, Ulrike Braatz3, Holm Graessner4, Daniela Berg2, Yiannos Manoli5, J Artur Serrano6, Joaquim J Ferreira7, Markus A Hobert2, Walter Maetzler8. 1. Institute of Microsystems and Information Technology, Hahn-Schickard Gesellschaft e.V., Villingen-Schwenningen, Germany. 2. Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tuebingen, Tuebingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany. 3. Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tuebingen, Tuebingen, Germany. 4. Institute for Medical Genetics and Applied Genomics, University of Tuebingen, Germany. 5. Institute of Microsystems and Information Technology, Hahn-Schickard Gesellschaft e.V., Villingen-Schwenningen, Germany; Fritz Huettinger Chair of Microelectronics, Department of Microsystems Engineering - IMTEK, Freiburg, Germany. 6. Norwegian Centre for Integrated Care and Telemedicine, University Hospital North Norway, Tromsø, Norway; Department of Clinical Medicine, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway. 7. Clinical Pharmacology Unit, Instituto de Medicina Molecular, Lisbon, Portugal; Laboratory of Clinical Pharmacology and Therapeutics, Faculty of Medicine, University of Lisbon, Portugal. 8. Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tuebingen, Tuebingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany. Electronic address: walter.maetzler@uni-tuebingen.de.
Abstract
INTRODUCTION: Dyskinesias in Parkinson's disease (PD) patients are a common side effect of long-term dopaminergic therapy and are associated with motor dysfunctions, including gait and balance deficits. Although promising compounds have been developed to treat these symptoms, clinical trials have failed. This failure may, at least partly, be explained by the lack of objective and continuous assessment strategies. This study tested the clinical validity and ecological effect of an algorithm that detects and quantifies dyskinesias of the legs using a single ankle-worn sensor. METHODS: Twenty-three PD patients (seven with leg dyskinesias) and 13 control subjects were investigated in the lab. Participants performed purposeful daily activity-like tasks while being video-taped. Clinical evaluation was performed using the leg dyskinesia item of the Unified Dyskinesia Rating Scale. The ecological effect of the developed algorithm was investigated in a multi-center, 12-week, home-based sub-study that included three patients with and seven without dyskinesias. RESULTS: In the lab-based sub-study, the sensor-based algorithm exhibited a specificity of 98%, a sensitivity of 85%, and an accuracy of 0.96 for the detection of dyskinesias and a correlation level of 0.61 (p < 0.001) with the clinical severity score. In the home-based sub-study, all patients could be correctly classified regarding the presence or absence of leg dyskinesias, supporting the ecological relevance of the algorithm. CONCLUSION: This study provides evidence of clinical validity and ecological effect of an algorithm derived from a single sensor on the ankle for detecting leg dyskinesias in PD patients. These results should motivate the investigation of leg dyskinesias in larger studies using wearable sensors.
INTRODUCTION:Dyskinesias in Parkinson's disease (PD) patients are a common side effect of long-term dopaminergic therapy and are associated with motor dysfunctions, including gait and balance deficits. Although promising compounds have been developed to treat these symptoms, clinical trials have failed. This failure may, at least partly, be explained by the lack of objective and continuous assessment strategies. This study tested the clinical validity and ecological effect of an algorithm that detects and quantifies dyskinesias of the legs using a single ankle-worn sensor. METHODS: Twenty-three PDpatients (seven with leg dyskinesias) and 13 control subjects were investigated in the lab. Participants performed purposeful daily activity-like tasks while being video-taped. Clinical evaluation was performed using the leg dyskinesia item of the Unified Dyskinesia Rating Scale. The ecological effect of the developed algorithm was investigated in a multi-center, 12-week, home-based sub-study that included three patients with and seven without dyskinesias. RESULTS: In the lab-based sub-study, the sensor-based algorithm exhibited a specificity of 98%, a sensitivity of 85%, and an accuracy of 0.96 for the detection of dyskinesias and a correlation level of 0.61 (p < 0.001) with the clinical severity score. In the home-based sub-study, all patients could be correctly classified regarding the presence or absence of leg dyskinesias, supporting the ecological relevance of the algorithm. CONCLUSION: This study provides evidence of clinical validity and ecological effect of an algorithm derived from a single sensor on the ankle for detecting leg dyskinesias in PDpatients. These results should motivate the investigation of leg dyskinesias in larger studies using wearable sensors.
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Authors: Hanna Marie Röhling; Patrik Althoff; Radina Arsenova; Daniel Drebinger; Norman Gigengack; Anna Chorschew; Daniel Kroneberg; Maria Rönnefarth; Tobias Ellermeyer; Sina Cathérine Rosenkranz; Christoph Heesen; Behnoush Behnia; Shigeki Hirano; Satoshi Kuwabara; Friedemann Paul; Alexander Ulrich Brandt; Tanja Schmitz-Hübsch Journal: JMIR Hum Factors Date: 2022-04-01
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