| 1. |
- Aghanavesi, Somayeh, 1981-, et al.
(author)
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A multiple motion sensors index for motor state quantification in Parkinson's disease
- 2020
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In: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 189
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Journal article (peer-reviewed)abstract
- Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results. © 2019
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| 2. |
- Cedergren Weber, Gustav, et al.
(author)
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The Impact of COVID-19 on Parkinson's Disease : A Case-Controlled Registry and Questionnaire Study on Clinical Markers and Patients' Perceptions
- 2023
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In: Acta Neurologica Scandinavica. - : John Wiley & Sons. - 0001-6314 .- 1600-0404. ; 2023
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Journal article (peer-reviewed)abstract
- Introduction: Parkinson's disease (PD) is a neurodegenerative disease with motor and nonmotor symptoms. Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Objectives: To explore how COVID-19 affects motor, nonmotor, and general health aspects of PD and to map how PD patients perceive their change in symptoms since falling ill with COVID-19.Method: The study was descriptive, case-controlled, and based on both registry and questionnaire data. At baseline, the controls were matched on age, sex, and disease severity. Information on the severity of the disease, nonmotor symptoms, motor symptoms, and general health was retrieved from the Swedish Registry for PD. Registry data from a COVID-19 group (n=45) and a control group (n=73), as well as questionnaires from a COVID-19 group (n=24) and a control group (n=42), were compared.Results: We did not find that SARS-CoV-2 infection affects any major aspect of nonmotor symptoms, motor symptoms, general health, and perception of change in PD patients' post-COVID-19. Compared to controls, the COVID-19 group reported a more positive subjective experience of pain and quality of life and a perception of change post-COVID-19 regarding general motor function, sleep quality, and mood (all p<0.05).Conclusion: Although SARS-CoV-2 infection does not seem to affect PD symptoms in any major respect, the subjective experience of several aspects of life in PD patients might be slightly improved post-COVID-19 compared to a control group. The findings warrant further investigations due to the small sample size and possible survivorship bias.
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| 3. |
- Johansson, Dongni, 1988, et al.
(author)
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Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
- 2019
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In: Parkinsonism & Related Disorders. - : Elsevier BV. - 1353-8020 .- 1873-5126. ; 64:July, s. 112-117
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Journal article (peer-reviewed)abstract
- Introduction: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models. Methods: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III. Results: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (r(s) = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (r(s) = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used. Conclusion: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.
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| 4. |
- Matić, Teodora, et al.
(author)
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Unsupervised Learning from Motion Sensor Data to Assess the Condition of Patients with Parkinson’s Disease
- 2019
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In: AIME 2019. - Cham : Springer. - 9783030216429 - 9783030216412 ; , s. 420-424
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Conference paper (peer-reviewed)abstract
- Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement.
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| 5. |
- Memedi, Mevludin, 1983-, et al.
(author)
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Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease
- 2015
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In: Sensors. - : MDPI AG. - 1424-8220. ; 15:9, s. 23727-23744
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Journal article (peer-reviewed)abstract
- A challenge for the clinical management of advanced Parkinson's disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.
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| 6. |
- Senek, Marina, et al.
(author)
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Levodopa/carbidopa microtablets in Parkinson's disease : a study of pharmacokinetics and blinded motor assessment
- 2017
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In: European Journal of Clinical Pharmacology. - Heidelberg, Germany : Springer. - 0031-6970 .- 1432-1041 .- 0014-2999. ; 73:5, s. 563-571
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Journal article (peer-reviewed)abstract
- Background: Motor function assessments with rating scales in relation to the pharmacokinetics of levodopa may increase the understanding of how to individualize and fine-tune treatments.Objectives: This study aimed to investigate the pharmacokinetic profiles of levodopa-carbidopa and the motor function following a single-dose microtablet administration in Parkinson’s disease.Methods: This was a single-center, open-label, single-dose study in 19 patients experiencing motor fluctuations. Patients received 150% of their individual levodopa equivalent morning dose in levodopa-carbidopa microtablets. Blood samples were collected at pre-specified time points. Patients were video recorded and motor function was assessed with six UPDRS part III motor items, dyskinesia score, and the treatment response scale (TRS), rated by three blinded movement disorder specialists.Results: AUC0–4/dose and Cmax/dose for levodopa was found to be higher in Parkinson’s disease patients compared with healthy subjects from a previous study, (p = 0.0008 and p = 0.026, respectively). The mean time to maximum improvement in sum of six UPDRS items score was 78 min (±59) (n = 16), and the mean time to TRS score maximum effect was 54 min (±51) (n = 15). Mean time to onset of dyskinesia was 41 min (±38) (n = 13).Conclusions: In the PD population, following levodopa/carbidopa microtablet administration in fasting state, the Cmax and AUC0–4/dose were found to be higher compared with results from a previous study in young, healthy subjects. A large between subject variability in response and duration of effect was observed, highlighting the importance of a continuous and individual assessment of motor function in order to optimize treatment effect.
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| 7. |
- Thomas, Ilias, et al.
(author)
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Sensor-based algorithmic dosing suggestions for oral administration of levodopa/carbidopa microtablets for Parkinson's disease : a first experience
- 2019
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In: Journal of Neurology. - : Springer Science and Business Media LLC. - 0340-5354 .- 1432-1459. ; 266:3, s. 651-658
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Journal article (peer-reviewed)abstract
- OBJECTIVE: Dosing schedules for oral levodopa in advanced stages of Parkinson's disease (PD) require careful tailoring to fit the needs of each patient. This study proposes a dosing algorithm for oral administration of levodopa and evaluates its integration into a sensor-based dosing system (SBDS).MATERIALS AND METHODS: In collaboration with two movement disorder experts a knowledge-driven, simulation based algorithm was designed and integrated into a SBDS. The SBDS uses data from wearable sensors to fit individual patient models, which are then used as input to the dosing algorithm. To access the feasibility of using the SBDS in clinical practice its performance was evaluated during a clinical experiment where dosing optimization of oral levodopa was explored. The supervising neurologist made dosing adjustments based on data from the Parkinson's KinetiGraph™ (PKG) that the patients wore for a week in a free living setting. The dosing suggestions of the SBDS were compared with the PKG-guided adjustments.RESULTS: The SBDS maintenance and morning dosing suggestions had a Pearson's correlation of 0.80 and 0.95 (with mean relative errors of 21% and 12.5%), to the PKG-guided dosing adjustments. Paired t test indicated no statistical differences between the algorithmic suggestions and the clinician's adjustments.CONCLUSION: This study shows that it is possible to use algorithmic sensor-based dosing adjustments to optimize treatment with oral medication for PD patients.
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| 8. |
- Thomas, Ilias, et al.
(author)
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Using measurements from wearable sensors for automatic scoring of Parkinson's disease motor states : Results from 7 patients
- 2017
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In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). - New York : IEEE. - 1094-687X. - 9781509028092 - 9781509028108 ; , s. 131-134
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Conference paper (peer-reviewed)abstract
- The objective of this study was to investigate the validity of an objective gait measure for assessment of different motor states of advanced Parkinson's disease (PD) patients. Seven PD patients performed a gait task up to 15 times while wearing sensors on their upper and lower limbs. Each task was performed at specific points during a test day, following a single dose of levodopa-carbidopa. At the time of the tasks the patients were video recorded and three movement disorder experts rated their motor function on three clinical scales: a treatment response scale (TRS) that ranged from −3 (very bradykinetic) to 0 (ON) to +3 (very dyskinetic), a dyskinesia score that ranged from 0 (no dyskinesia) to 4 (extreme dyskinesia), and a bradykinesia score that ranged from 0 (no bradykinesia) to 4 (extreme bradykinesia). Raw accelerometer and gyroscope data of the sensors were processed and analyzed with time series analysis methods to extract features. The utilized features quantified separate limb movements as well as movement symmetries between the limbs. The features were processed with principal component analysis and the components were used as predictors for separate support vector machine (SVM) models for each of the three scales. The performance of each model was evaluated in a leave-one-patient out setting where the observations of a single patient were used as the testing set and the observations of the other 6 patients as the training set. Root mean square error (RMSE) and correlation coefficients for the predictions showed a good ability of the models to map the sensor data into the rating scales. There were strong correlations between the SVM models and the mean ratings of TRS (0.79; RMSE=0.70), bradykinesia score (0.79; RMSE=0.47), and bradykinesia score (0.78; RMSE=0.46). The results presented in this paper indicate that the use of wearable sensors when performing gait tasks can generate measurements that have a good correlation to subjective expert assessments.
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