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1.
PLoS One ; 15(10): e0238486, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33031372

RESUMO

OBJECTIVE: We aim to propose a novel method of evaluating the degree of rhythmic irregularity during repetitive tasks in Parkinson's disease (PD) by using autocorrelation to extract serial perturbation in the periodicity of body part movements as recorded by objective devices. METHODS: We used publicly distributed sequential joint movement data recorded during a leg agility task or pronation-supination task. The sequences of body part trajectory were processed to extract their short-time autocorrelation (STACF) matrices; the sequences of single task conducted by participants were then divided into two clusters according to their similarity in terms of their STACF representation. The Unified Parkinson's Disease Rating Scale sub-score rated for each task was compared with cluster membership to obtain the area under the curve (AUC) to evaluate the discrimination performance of the clustering. We compared the AUC with those obtained from the clustering of the raw sequence or short-time Fourier transform (STFT). RESULTS: In classifying the pose estimator-based trajectory data of the knee during the leg agility task, the AUC was the highest when the STACF sequence was used for clustering instead of other types of sequences with up to 0.815, being comparable to the results reported in the original analysis of the data using an approach different from ours. In addition, in classifying another dataset of accelerometer-based trajectory data of the wrist during a pronation-supination task, the AUC was again highest up to 0.785 when clustering was performed using the STACF rather than other types of sequence. CONCLUSION: Our autocorrelation-based method achieved a fair performance in detecting sequences with irregular rhythm, suggesting that it might be used as another evaluation strategy that is potentially widely applicable to qualify the disordered rhythm of PD regardless of the kinds of task or the modality of devices, although further refinement is needed.


Assuntos
Doença de Parkinson/diagnóstico , Doença de Parkinson/fisiopatologia , Análise e Desempenho de Tarefas , Acelerometria , Área Sob a Curva , Fenômenos Biomecânicos , Análise por Conglomerados , Bases de Dados Factuais , Diagnóstico por Computador , Análise de Fourier , Humanos , Articulação do Joelho/fisiopatologia , Movimento/fisiologia , Doença de Parkinson/classificação , Pronação/fisiologia , Estudos Retrospectivos , Análise Espacial , Supinação/fisiologia
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 789-792, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018104

RESUMO

The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. Data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both. Different templates were compared and an optimized template was created. The classification scored a F1-measure of 86.7% for evaluation on a data set acquired in a clinical setting. We believe that this approach can be transferred to home-monitoring systems and will facilitate a more efficient and automated gait analysis.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Algoritmos , Marcha , Análise da Marcha , Humanos , Doença de Parkinson/diagnóstico
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 798-802, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018106

RESUMO

BACKGROUND: Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases: stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases. OBJECTIVE: In this paper, we compared two techniques: wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals. METHODS: Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained by both methods. RESULTS: PD group showed significant reductions in speed (wavelet p = 0.001, peak detection p <0.001) and significantly greater swing (wavelet p = 0.003, peak detection p =0.005) and stance times (wavelet p = 0.003, peak detection p =0.004). Hamming distances showed small differences between the signals obtained by both methods (16 to 18 signal points). A very strong correlation (Spearman rho > 0.8, p <0.05) was found between the spatiotemporal variables obtained by each signal processing technique. CONCLUSION: Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.Clinical Relevance- this establishes that peaks and wavelet techniques are comparable and may use it interchangeably to process signals from the gait of Parkinson's disease patients to support diagnosis and follow up made by a clinical expert.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Marcha , Análise da Marcha , Humanos , Doença de Parkinson/diagnóstico , Caminhada
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 847-850, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018117

RESUMO

Parkinson's disease (PD) patients with freezing of gait (FOG) can suddenly lose their forward moving ability leading to unexpected falls. To overcome FOG and avoid the falls, a real-time accurate FOG detection or prediction system is desirable to trigger on-demand cues. In this study, we designed and implemented an in-place movement experiment for PD patients to provoke FOG and meanwhile acquired multimodal physiological signals, such as electroencephalography (EEG) and accelerometer signals. A multimodal model using brain activity from EEG and motion data from accelerometers was developed to improve FOG detection performance. In the detection of over 700 FOG episodes observed in the experiments, the multimodal model achieved 0.211 measured by Matthews Correlation Coefficient (MCC) compared with the single-modal models (0.127 or 0.139).Clinical Relevance- This is the first study to use multimodal: EEG and accelerometer signal analysis in FOG detection, and an improvement was achieved.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Acelerometria , Eletroencefalografia , Marcha , Transtornos Neurológicos da Marcha/diagnóstico , Humanos , Doença de Parkinson/diagnóstico
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3658-3661, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018794

RESUMO

Parkinson's Disease (PD) is a neurodegenerative disease characterized by its hallmark motor symptoms of bradykinesia and tremor. Numerous studies have suggested novel quantification methods of its symptoms. However, there lacks the means to accurately assess improvements in an intraoperative setting during deep brain stimulation (DBS) electrode implantation. This study introduces a methodology to quantify selected PD motor symptoms in such a restrictive environment using a wireless Leap Motion sensor. The result suggests that utilizing the Leap Motion sensor intraoperatively is feasible for quantifying motor parameters for bradykinesia and resting tremor of a PD patient.


Assuntos
Estimulação Encefálica Profunda , Doenças Neurodegenerativas , Doença de Parkinson , Humanos , Hipocinesia/diagnóstico , Doença de Parkinson/diagnóstico , Tremor/diagnóstico
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3676-3679, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018798

RESUMO

Finger tapping test is an important neuropsychological test to evaluate human motor function. Most recent researches simplified the finger tapping motion as a scissors-like motion, though the rotation axis of the thumb was different from that of the forefinger. In this paper, we proposed a three-dimensional (3-D) finger tapping measurement system to obtain 3-D pattern features in finger tapping test for patients with Parkinson's disease (PD). The proposed system collected the motion of the thumb and the forefinger by nine-degrees-freedom sensors and calculated 3-D motion of finger tapping by an orientation estimation method and a 3-D finger-tapping kinematic model. We further extracted 3-D pattern features, i.e. motor coordination and relative thumb motion, from 3-D Finger Tapping motion. Moreover, we used the proposed system to collect the finger-tapping motion of 43 PD patients and 30 healthy controls in horizontal tasks and vertical tasks. The results indicated that 3-D pattern features showed a better performance than one-dimensional features in the identification of mild PD patients.Clinical Relevance- These three-dimensional pattern features could be used to evaluate finger tapping motion in a novel way, which could be used to better identify mild Parkinson's disease patients. Furthermore, the results showed that a combination of horizontal tasks and vertical tasks might be a better way to identify mild Parkinson's disease patients.


Assuntos
Doença de Parkinson , Fenômenos Biomecânicos , Dedos , Humanos , Movimento (Física) , Doença de Parkinson/diagnóstico , Polegar
9.
Nat Commun ; 11(1): 5046, 2020 10 07.
Artigo em Inglês | MEDLINE | ID: mdl-33028816

RESUMO

Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual's own functional brain organization.


Assuntos
Mapeamento Encefálico/métodos , Córtex Cerebral/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Adolescente , Adulto , Córtex Cerebral/irrigação sanguínea , Córtex Cerebral/fisiologia , Conjuntos de Dados como Assunto , Estimulação Encefálica Profunda , Feminino , Voluntários Saudáveis , Humanos , Masculino , Modelos Neurológicos , Oxigênio/sangue , Doença de Parkinson/diagnóstico , Doença de Parkinson/fisiopatologia , Doença de Parkinson/terapia , Adulto Jovem
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4326-4329, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018953

RESUMO

Parkinson's Disease (PD) is the second most common neurodegenerative disorder with the non-motor symptoms preceding the motor impairment that is needed for clinical diagnosis. In the current study, an angle-based analysis that processes activity data during sleep from a smartwatch for quantification of sleep quality, when applied on controls and PD patients, is proposed. Initially, changes in their arm angle due to activity are captured from the smartwatch triaxial accelerometry data and used for the estimation of the corresponding binary state (awake/sleep). Then, sleep metrics (i.e., sleep efficiency index, total sleep time, sleep fragmentation index, sleep onset latency, and wake after sleep onset) are computed and used for the discrimination between controls and PD patients. A process of validation of the proposed approach when compared with the PSG-based ground truth in an in-the-clinic setting, resulted in comparable state estimation. Moreover, data from 15 early PD patients and 11 healthy controls were used as a test set, including 1,376 valid sleep recordings in-the-wild setting. The univariate analysis of the extracted sleep metrics achieved up to 0.77 AUC in early PD patients vs. healthy controls classification and exhibited a statistically significant correlation (up to 0.46) with the clinical PD Sleep Scale 2 counterpart Items. The findings of the proposed method show the potentiality to capture non-motor behavior from users' nocturnal activity to detect PD in the early stage.


Assuntos
Doença de Parkinson , Transtornos do Sono-Vigília , Humanos , Doença de Parkinson/diagnóstico , Polissonografia , Sono , Privação do Sono , Transtornos do Sono-Vigília/diagnóstico
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4034-4037, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018884

RESUMO

Freezing of gait (FOG) is a major hindrance to daily mobility and can lead to falling in people with Parkinson's disease. While wearable accelerometers and gyroscopes have been commonly used for FOG detection, foot plantar pressure distribution could also be considered for this application, given its usefulness in previous gait-based classification. This research examined 325 plantar-pressure based features and 132 acceleration-based features extracted from the walking data of five males with Parkinson's disease who experienced FOG. A set of 61 features calculated from the time domain, Fast Fourier transform (FFT), and wavelet transform (WT) were extracted from multiple input signals; including, total ground reaction force, foot centre of pressure (COP) position, COP velocity, COP acceleration, and 3D ankle acceleration. Minimum-redundancy maximum relevance (mRMR) feature selection was used to rank all features. Plantar-pressure based features accounted for 4 of the top 5 features (ranks 2, 3, 4, 5); the remaining feature was an ankle acceleration based feature (rank 1). The three highest ranked features were the freeze index (calculated from ankle acceleration), total power in the frequency domain (calculated using the FFT from COP velocity), and mean of the WT detail coefficients (calculated from COP velocity). This preliminary analysis demonstrated that features calculated from plantar pressure, specifically COP velocity, performed comparably to ankle acceleration features. Thus, feature sets for FOG detection may benefit from plantar-pressure based features.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Aceleração , Tornozelo , Marcha , Transtornos Neurológicos da Marcha/diagnóstico , Humanos , Masculino , Doença de Parkinson/diagnóstico
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5410-5415, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019204

RESUMO

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.


Assuntos
Transtornos Neurológicos da Marcha , Doença de Parkinson , Dispositivos Eletrônicos Vestíveis , Marcha , Humanos , Extremidade Inferior , Redes Neurais de Computação , Doença de Parkinson/diagnóstico , Análise de Ondaletas
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5436-5441, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019210

RESUMO

Passive, continuous monitoring of Parkinson's Disease (PD) symptoms in the wild (i.e., in home environments) could improve disease management, thereby improving a patient's quality of life. We envision a system that uses machine learning to automatically detect PD symptoms from accelerometer data collected in the wild. Building such systems, however, is challenging because it is difficult to obtain labels of symptom occurrences in the wild. Many researchers therefore train machine learning algorithms on laboratory data with the assumption that findings will translate to the wild. This paper assesses how well laboratory data represents wild data by comparing PD symptom (tremor) detection performance of three models on both lab and wild data. Findings indicate that, for this application, laboratory data is not a good representation of wild data. Results also show that training on wild data, even though labels are less precise, leads to better performance on wild data than training on accurate labels from laboratory data.


Assuntos
Doença de Parkinson , Tremor , Algoritmos , Humanos , Aprendizado de Máquina , Doença de Parkinson/diagnóstico , Qualidade de Vida , Tremor/diagnóstico
15.
J Parkinsons Dis ; 10(4): 1355-1364, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32925108

RESUMO

BACKGROUND: The ongoing COVID-19 pandemic has many consequences for people with Parkinson's disease (PD). Social distancing measures complicate regular care and result in lifestyle changes, which may indirectly cause psychological stress and worsening of PD symptoms. OBJECTIVE: To assess whether the COVID-19 pandemic was associated with increased psychological distress and decreased physical activity in PD, how these changes related to PD motor and non-motor symptom severity, and what frequency and burden of COVID-related stressors were. METHODS: We sent an online survey to the Personalized Parkinson Project (PPP) cohort (n = 498 PD patients) in the Netherlands. In the survey, we distinguished between COVID-related stressor load, psychological distress, PD symptom severity, and physical activity. We related inter-individual differences to personality factors and clinical factors collected before the pandemic occurred. RESULTS: 358 PD patients completed the survey between April 21 and May 25, 2020 (response rate 71.9%). Patients with higher COVID-related stressor load experienced more PD symptoms, and this effect was mediated by the degree of psychological distress. 46.6% of PD patients were less physically active since the COVID-19 pandemic, and reduced physical activity correlated with worse PD symptoms. Symptoms that worsened most were rigidity, fatigue, tremor, pain and concentration. Presence of neuropsychiatric symptoms (anxiety, depression) before the pandemic, as well as cognitive dysfunction and several personality traits predicted increased psychological distress during the COVID-19 pandemic. CONCLUSION: Our findings show how an external stressor (the COVID-19 pandemic) leads to a worsening of PD symptoms by evoking psychological distress as well as lifestyle changes (reduced physical activity).


Assuntos
Infecções por Coronavirus/complicações , Infecções por Coronavirus/psicologia , Exercício Físico , Doença de Parkinson/psicologia , Pneumonia Viral/complicações , Pneumonia Viral/psicologia , Angústia Psicológica , Idoso , Feminino , Humanos , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade , Pandemias , Doença de Parkinson/diagnóstico , Índice de Gravidade de Doença , Inquéritos e Questionários
16.
Fortschr Neurol Psychiatr ; 88(9): 609-619, 2020 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-32957144

RESUMO

The present work provides an overview of the various nuclear medicine methods in the diagnosis of neurodegenerative parkinsonian syndromes and their respective evidence and is intended to enable practical decision-making aids in the application and interpretation of the methods and findings. The value of the procedures differs considerably in relation to the two relevant diagnostic questions. On the one hand, it is the question of whether there is a neurodegenerative parkinsonian syndrome at all, and on the other hand the question of which one. While the DAT-SPECT is undisputedly the method of choice for answering the first question (taking certain parameters into account), this method is not suitable for answering the second question. To categorise parkinsonian syndromes into idiopathic (i. e. Parkinson´s disease) or atypical, various procedures are used in everyday clinical practice including MIBG scintigraphy, and FDG-PET. We explain why FDG-PET currently is not only the most suitable of these methods to differentiate an idiopathic parkinsonian syndrome, from an atypical Parkinson's syndrome, but also enables sufficiently valid to distinguish the various atypical neurodegenerative Parkinson's syndromes (i. e. MSA, PSP and CBD) from each other and therefore should be reimbursed by health insurances.


Assuntos
Transtornos Parkinsonianos/classificação , Transtornos Parkinsonianos/diagnóstico , Diagnóstico Diferencial , Humanos , Doença de Parkinson/diagnóstico , Tomografia por Emissão de Pósitrons , Tomografia Computadorizada de Emissão de Fóton Único
17.
Neurology ; 95(9): e1267-e1284, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32747521

RESUMO

OBJECTIVE: The Systemic Synuclein Sampling Study (S4) measured α-synuclein in multiple tissues and biofluids within the same patients with Parkinson disease (PD) vs healthy controls (HCs). METHODS: S4 was a 6-site cross-sectional observational study of participants with early, moderate, or advanced PD and HCs. Motor and nonmotor measures and dopamine transporter SPECT were obtained. Biopsies of skin, colon, submandibular gland (SMG), CSF, saliva, and blood were collected. Tissue biopsy sections were stained with 5C12 monoclonal antibody against pathologic α-synuclein; digital images were interpreted by neuropathologists blinded to diagnosis. Biofluid total α-synuclein was quantified using ELISA. RESULTS: The final cohort included 59 patients with PD and 21 HCs. CSF α-synuclein was lower in patients with PD vs HCs; sensitivity/specificity of CSF α-synuclein for PD diagnosis was 87.0%/63.2%, respectively. Sensitivity of α-synuclein immunoreactivity for PD diagnosis was 56.1% for SMG and 24.1% for skin; specificity was 92.9% and 100%, respectively. There were no significant relationships between different measures of α-synuclein within participants. CONCLUSIONS: S4 confirms lower total α-synuclein levels in CSF in patients with PD compared to HCs, but specificity is low. In contrast, α-synuclein immunoreactivity in skin and SMG is specific for PD but sensitivity is low. Relationships within participants across different tissues and biofluids could not be demonstrated. Measures of pathologic forms of α-synuclein with higher accuracy are critically needed. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that total CSF α-synuclein does not accurately distinguish patients with PD from HCs, and that monoclonal antibody staining for SMG and skin total α-synuclein is specific but not sensitive for PD diagnosis.


Assuntos
Encéfalo/diagnóstico por imagem , Colo/metabolismo , Doença de Parkinson/metabolismo , Saliva/metabolismo , Pele/metabolismo , Glândula Submandibular/metabolismo , alfa-Sinucleína/metabolismo , Adulto , Idoso , Idoso de 80 Anos ou mais , Biópsia , Encéfalo/metabolismo , Estudos de Casos e Controles , Proteínas da Membrana Plasmática de Transporte de Dopamina , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Doença de Parkinson/diagnóstico , Sensibilidade e Especificidade , Tomografia Computadorizada de Emissão de Fóton Único , alfa-Sinucleína/sangue , alfa-Sinucleína/líquido cefalorraquidiano
18.
PLoS One ; 15(8): e0236728, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32750069

RESUMO

OBJECTIVES: Psychiatric symptoms (PS) can be non-motor features in Parkinson's disease (PD) which are common even in the prodromal, untreated phase of the disease. Some PS, especially depression and anxiety recently became known predictive markers for PD. Our objective was to explore retrospectively the prevalence of PS before the diagnosis of PD. METHODS: In the framework of the Hungarian Brain Research Program we created a database from medical and medication reports submitted for reimbursement purposes to the National Health Insurance Fund in Hungary, a country with 10 million inhabitants and a single payer health insurance system. We used record linkage to evaluate the prevalence of PS before the diagnosis of PD and compared that with patients with ischemic cerebrovascular lesion (ICL) in the period between 2004-2016 using ICD-10 codes of G20 for PD, I63-64 for ICL and F00-F99 for PS. We included only those patients who got their PD, ICL and psychiatric diagnosis at least twice. RESULTS: There were 79 795 patients with PD and 676 874 patients with ICL. Of the PD patients 16% whereas of those with ischemic cerebrovascular lesion 9.7% had a psychiatric diagnosis before the first appearance of PD or ICL (p<0.001) established in psychiatric care at least twice. The higher rate of PS in PD compared to ICL remained significant after controlling for age and gender in logistic regression analysis. The difference between PD and ICL was significant for Mood disorders (F30-F39), Organic, including symptomatic, mental disorders (F00-F09), Neurotic, stress-related and somatoform disorders (F40-F48) and Schizophrenia, schizotypal and delusional disorders (F20-F29) diagnosis categories (p<0.001, for all). DISCUSSION: The higher rate of psychiatric morbidity in the premotor phase of PD may reflect neurotransmitter changes in the early phase of PD.


Assuntos
Infarto Cerebral/psicologia , Transtornos Mentais/epidemiologia , Doença de Parkinson/psicologia , Idoso , Idoso de 80 Anos ou mais , Bases de Dados Factuais , Feminino , Humanos , Masculino , Transtornos Mentais/etiologia , Pessoa de Meia-Idade , Doença de Parkinson/diagnóstico , Prevalência , Estudos Retrospectivos
19.
PLoS One ; 15(8): e0236886, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32790749

RESUMO

Falls pose a serious problem in elderly and clinical populations. Most often, they lead to a loss of mobility and independence. They might also be an indirect cause of death. The aim of this study was to determine an objective predictor of the fear of falling and falls in elderly subjects (ESs) and Parkinson's disease (PD) subjects. Thirty-two ESs were examined in this study, of whom sixteen were diagnosed with PD. The testing procedures comprised force plate measurements (limit of stability test-LOS test) and clinical tests (Berg Balance Scale, Functional Reach Test, Timed Up and Go test, Tinetti test). The Falls Efficacy Scale International (FES-I) was used to evaluate the fear of falling. The range of the maximum forward lean was normalized to the length from the ankle joint to the head of the first metatarsal bone and was named the functional forward stability indicator (FFSI). The FFSI, derived from the LOS test, allowed us to demonstrate the real deficit in functional stability and individual safety margins. Moreover, the FFSI was highly correlated with the FES-I score and almost all clinical test results in elderly subjects (r>0,6; p<0.05). In PD subjects, the FFSI was poorly correlated with the fear of falling, the BBS score and the FR distance; however, a high correlation with the Tinetii test (r>0,6, p<0.05) was noted. The PD subjects presented a different balance strategy when close to their stability limits, which was also reflected in the lower values of sample entropy (t = (-2.40); p<0.05; d = 0.87). The FFSI might be a good predictor of the fear of falling in the group of elderly people. Additionally, the FFSI allows us to show real balance deficits both in PD subjects and in their healthy peers without the need for a reference group and norms. In conclusion, it is postulated that the popular clinical assessments of postural balance in PD subjects should be accompanied by reliable posturography measurements.


Assuntos
Acidentes por Quedas/estatística & dados numéricos , Doença de Parkinson/diagnóstico , Idoso , Antropometria , Entropia , Medo/psicologia , Feminino , Idoso Fragilizado , Humanos , Masculino , Doença de Parkinson/patologia , Equilíbrio Postural , Índice de Gravidade de Doença
20.
Georgian Med News ; (303): 86-92, 2020 Jun.
Artigo em Russo | MEDLINE | ID: mdl-32841187

RESUMO

At present, the problem of differential diagnosis and therapy of Parkinson's disease and essential tremor is one of the topical issues of modern clinical neurology. Despite the nosological independence of these diseases, there is evidence of their pathogenetic relationship. The article presents a review of the results of modern scientific research devoted to the study of criteria for diagnosing Parkinson's disease and essential tremor. The clinical features of tremor in Parkinson's disease and essential tremor are considered in detail, and the results of studies indicating the simultaneous coexistence of these diseases are presented. Verification of the diagnosis in these nosologies is based on a thorough collection of anamnesis, including family history; specific characteristics of the tremor, taking into account the frequency, amplitude, involvement of body parts; identification of additional neurological symptoms. The article deals with modern medical and surgical methods of treatment of Parkinson's disease and essential tremor. A clinical example of differential diagnosis of Parkinson's disease and essential tremor is presented.


Assuntos
Tremor Essencial/diagnóstico , Doença de Parkinson/diagnóstico , Diagnóstico Diferencial , Humanos , Tremor/diagnóstico
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