scholarly journals Quantitative Digitography Solves the Remote Measurement Problem in Parkinson's disease

2021 ◽  
Author(s):  
Kevin B Wilkins ◽  
Matthew N. Petrucci ◽  
Yasmine M Kehnemouyi ◽  
Anca Velisar ◽  
Katie Han ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Measurement Problem ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Unmet Need ◽  
Remote Measurement ◽  
Motor Disability ◽  
Therapeutic Trials

Background: Assessment of motor signs in Parkinson's disease (PD) has required an in-person examination. However, 50% of people with PD do not have access to a neurologist. Wearable sensors can provide remote measures of some motor signs but require continuous data acquisition for several days. A major unmet need is reliable metrics of all cardinal motor signs, including rigidity, from a simple short active task that can be performed remotely or in the clinic. Objective: Investigate whether thirty seconds of repetitive alternating finger tapping (RAFT) on a portable quantitative digitography (QDG) device, which measures amplitude and timing, produces reliable metrics of all cardinal motor signs in PD Methods: Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and clinical motor assessment. Eighteen individuals were followed longitudinally with repeated assessments for an average of three years and up to six years. Results: QDG-RAFT metrics differentiated individuals with PD from controls and provided validated metrics for total motor disability (MDS-UPDRS III) and for rigidity, bradykinesia, tremor, gait impairment and freezing of gait (FOG). Additionally, QDG-RAFT tracked disease progression over several years off therapy, and differentiated akinetic rigid from tremor dominant phenotypes, as well as people with from those without FOG. Conclusions: QDG is a reliable technology, which will improve access to care, allows complex remote disease management, and accurate monitoring of disease progression over time in PD. QDG-RAFT also provides the comprehensive PD motor metrics needed for therapeutic trials.

Complex Parkinson's disease: review and experiences

2019 ◽  
Vol 15 (3) ◽  
pp. 140-145
Author(s):  
Phil Cotterell ◽  
Debbie Weight ◽  
Sharon Joseph ◽  
Paul Joseph
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Unmet Need ◽  
Motor Fluctuations ◽  
Financial Problems ◽  
Care Environment ◽  
Psychological Issues ◽  
Palliative Approach ◽  
The Impact

The rate of Parkinson's disease progression is highly individual. It is important to identify those in the complex stage of the disease. Non-oral therapies may be appropriate for those in the complex stage of the disease who experience motor fluctuations, but there can be reasons for a failure to explore these options. Complex Parkinson's disease is a challenging time for the patient, their carer/s and for Parkinson's specialists. Changes to independence can be difficult to deal with, and the disease has an impact both physically and psychologically. For carers, the impact of Parkinson's can also result in physical and psychological issues as well as social and financial problems. Consideration often turns to moving into a care environment, dealing with neuropsychiatric issues and challenging unmet need. Dealing with symptoms and problems using a team and a palliative approach is required.

scholarly journals Freezing of gait and fall detection in Parkinson’s disease using wearable sensors: a systematic review

2017 ◽  
Vol 264 (8) ◽  
pp. 1642-1654 ◽  
Author(s):  
Ana Lígia Silva de Lima ◽  
Luc J. W. Evers ◽  
Tim Hahn ◽  
Lauren Bataille ◽  
Jamie L. Hamilton ◽  
...  
Keyword(s):  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Fall Detection

scholarly journals Abnormal Olfaction in Parkinson’s Disease Is Related to Faster Disease Progression

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Sara Cavaco ◽  
Alexandra Gonçalves ◽  
Alexandre Mendes ◽  
Nuno Vila-Chã ◽  
Inês Moreira ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Rating Scale ◽  
Freezing Of Gait ◽  
Motor Symptom ◽  
Identification Test ◽  
Disease Rating ◽  
Abnormal Performance ◽  
Smell Identification

Introduction. A possible association between olfactory dysfunction and Parkinson’s disease (PD) severity has been a topic of contention for the past 40 years. Conflicting reports may be partially explained by procedural differences in olfactory assessment and motor symptom evaluation.Methods. One hundred and sixty-six nondemented PD patients performed the Brief-Smell Identification Test and test scores below the estimated 20th percentile as a function of sex, age, and education (i.e., 80% specificity) were considered demographically abnormal. Patients underwent motor examination after 12 h without antiparkinsonian medication.Results. Eighty-two percent of PD patients had abnormal olfaction. Abnormal performance on the Brief-Smell Identification Test was associated with higher disease severity (i.e., Hoehn and Yahr, Unified Parkinson’s Disease Rating Scale-III, Freezing of Gait questionnaire, and levodopa equivalent dose), even when disease duration was taken into account.Conclusions. Abnormal olfaction in PD is associated with increased severity and faster disease progression.

scholarly journals Wearable-Sensor-based Detection and Prediction of Freezing of Gait in Parkinson’s Disease: A Review

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5141 ◽  
Author(s):  
Pardoel ◽  
Kofman ◽  
Nantel ◽  
Lemaire
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Current Knowledge ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Machine Learning Algorithms ◽  
Model Generalization ◽  
Feature Extraction And Selection ◽  
Prediction Approach ◽  
Fog Detection

Freezing of gait (FOG) is a serious gait disturbance, common in mid- and late-stage Parkinson’s disease, that affects mobility and increases fall risk. Wearable sensors have been used to detect and predict FOG with the ultimate aim of preventing freezes or reducing their effect using gait monitoring and assistive devices. This review presents and assesses the state of the art of FOG detection and prediction using wearable sensors, with the intention of providing guidance on current knowledge, and identifying knowledge gaps that need to be filled and challenges to be considered in future studies. This review searched the Scopus, PubMed, and Web of Science databases to identify studies that used wearable sensors to detect or predict FOG episodes in Parkinson’s disease. Following screening, 74 publications were included, comprising 68 publications detecting FOG, seven predicting FOG, and one in both categories. Details were extracted regarding participants, walking task, sensor type and body location, detection or prediction approach, feature extraction and selection, classification method, and detection and prediction performance. The results showed that increasingly complex machine-learning algorithms combined with diverse feature sets improved FOG detection. The lack of large FOG datasets and highly person-specific FOG manifestation were common challenges. Transfer learning and semi-supervised learning were promising for FOG detection and prediction since they provided person-specific tuning while preserving model generalization.

Complex Parkinson's disease: review and experiences

2019 ◽  
Vol 13 (8) ◽  
pp. 394-400
Author(s):  
Phil Cotterell ◽  
Debbie Weight ◽  
Sharon Joseph ◽  
Paul Joseph
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Unmet Need ◽  
Motor Fluctuations ◽  
Financial Problems ◽  
Care Environment ◽  
Psychological Issues ◽  
Palliative Approach ◽  
The Impact

The rate of Parkinson's disease progression is highly individual. It is important to identify those in the complex stage of the disease. Non-oral therapies may be appropriate for those in the complex stage of the disease who experience motor fluctuations, but there can be reasons for a failure to explore these options. Complex Parkinson's disease is a challenging time for the patient, their carer/s and for Parkinson's specialists. Changes to independence can be difficult to deal with, and the disease has an impact both physically and psychologically. For carers, the impact of Parkinson's can also result in physical and psychological issues, as well as social and financial problems. Consideration often turns to moving into a care environment, dealing with neuropsychiatric issues and challenging unmet need. Dealing with symptoms and problems using a team and a palliative approach is required.

scholarly journals Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 948 ◽  
Author(s):  
Ivan Mazzetta ◽  
Alessandro Zampogna ◽  
Antonio Suppa ◽  
Alessandro Gumiero ◽  
Marco Pessione ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Fall Risk ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Clinical Manifestations ◽  
Leg Muscles ◽  
Trunk Inclination ◽  
First Time ◽  
Underlying Pathophysiology

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art.

Asymmetry at Disease Onset Is Not a Predictor of Parkinson’s Disease Progression

2021 ◽  
pp. 1-6
Author(s):  
Marco Cotogni ◽  
Lucia Sacchi ◽  
Aleksander Sadikov ◽  
Dejan Georgiev
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
De Novo ◽  
Disease Onset ◽  
Asymmetry Index ◽  
The Body ◽  
Motor Disability ◽  
The Asymmetry

Background: Even though a significant fraction of Parkinson’s disease (PD) patients presents with only minor or no motor asymmetry, the motor symptoms in PD typically start on one side of the body and worse symptoms on the side of the disease onset usually persist long after the disease has become clinically bilateral. The asymmetric presentation of PD has been studied over the years, with some studies showing slower progression in PD subjects with asymmetric disease presentation. In other studies, however, it was not possible to relate the asymmetry to disease progression. Objective: The main objective of the present study was to assess the effect of asymmetry at disease onset on disease progression. Methods: Using the data available in the Parkinson’s Progression Markers Initiative (PPMI) database, at baseline, 423 subjects with de-novo PD were included in the study. Instead of dichotomizing the subjects in asymmetric and symmetric, we kept the asymmetry index and the non-motor, disability, and motor progression at one-, three-, and five-year follow-up continuous. Linear regression was used to correlate asymmetry indices and disease progression. Results: There was no correlation between neither clinically, nor DatSCAN defined asymmetry and non-motor, motor, and disability progression in the de-novo PD subjects with a 5-year follow-up. Conclusion: Asymmetry does not predict progression of PD. Further studies are needed to investigate whether early detection of asymmetry on clinical grounds could successfully distinguish between PD and symmetric types of atypical parkinsonism in the early stages of the disease.

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