Continuous leg dyskinesia assessment in Parkinson's disease –clinical validity and ecological effect
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.
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In Parkinson's disease, wearables were used to detect and quantify medication-evoked adverse symptoms such as dyskinesia and motor fluctuations.
“In PD, wearables were used to detect and quantify cardinal motor symptoms including bradykinesia [19], tremor [20, 21], and postural sway [22, 23] as well as medication-evoked adverse symptoms such as dyskinesia [24–27] and motor fluctuations [28, 29].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
Moderate-to-strong correlations were reported between dyskinesia detected from wearables and clinical ratings.
“Moderate-to-strong correlations were reported between dyskinesia detected from wearables and clinical ratings [24–26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
Dyskinesia measures derived from wearables discriminated between patients with and without dyskinesia.
“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [24, 26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
Measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with Parkinson's disease and healthy controls.
“Different measures derived from wearables quantifying tremor, dyskinesia, postural sway, and spatiotemporal gait characteristics discriminated well between individuals with PD and healthy controls [20, 22, 24, 26, 31]; dyskinesia measures discriminated also between patients with and without dyskinesia [24, 26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
Promising results have been reported for correctly classifying individuals with dyskinesia using a single ankle-worn sensor in home environment.
“and correctly classifying individuals with dyskinesia using a single ankle-worn sensor in home environment [26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
Gyroscope signals have shown promise for increasing the sensitivity and specificity when measuring dyskinesia and postural instability in laboratory settings.
“Predominantly acceleration signals were used in hospital and free-living settings, even though gyroscope signals have shown promise for increasing the sensitivity and specificity when measuring dyskinesia and postural instability in laboratory settings [23–26].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [26]
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- Authors
- Ramsperger, R., Meckler, S., Heger, T., van Uem, J., Hucker, S., Braatz, U., Graessner, H., Berg, D., Manoli, Y., Serrano, J. A., Ferreira, J. J., Hobert, M. A., & Maetzler, W.
- Affiliations
- Hahn-Schickard-Gesellschaft für angewandte Forschung (DE)
- German Center for Neurodegenerative Diseases (DE)
- Hertie Institute for Clinical Brain Research (DE)
- University of Tübingen (DE)
- University Hospital of North Norway (NO)
- University of Lisbon (PT)
- Corresponding author
- Maetzler, Walter
- ORCID
- Venue
- Parkinsonism & Related Disorders
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 26
- Pages
- 41-46
- ISSN
- 1353-8020 (ISSN-L)
- OpenAlex
- W2266899181
- Cited by
- 41
- References
- 29
- Citations by year
- 2025: 1 · 2024: 5 · 2023: 5 · 2022: 4 · 2021: 7 · 2020: 4 · 2019: 2 · 2018: 6 · 2017: 2 · 2016: 5
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Parkinsonism & Related Disorders (publishedVersion)
- PubMed (publishedVersion)
- DZNE Pub (submittedVersion)
- Funders
- European Commission
- Seventh Framework Programme (288557, 316639)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- Parkinson's disease
- MeSH terms
- Aged, Antiparkinson Agents, Dyskinesia, Drug-Induced, Female, Humans, Leg, Levodopa, Male, Middle Aged, Parkinson Disease, Severity of Illness Index, Video Recording, Reproducibility of Results, Cohort Studies
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