Fatigue in Brazilian patients with Parkinson’s disease

Nassif, Daniel; Pereira, Joäo Santos

doi:10.1590/1980-5764-dn-2021-0083

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…The mean fatigue score was 4.05 (SD = 1.88), with all participants scoring ≥4, signifying significant fatigue. This finding aligns with similar levels of fatigue reported in studies of Parkinson’s disease patients ( 27 ). The mean score for quality of life was 54.60 (SD = 36.13), comparable to findings from research on PD patients ( 28 ).…”

Section: Discussionsupporting

confidence: 92%

The impact of fatigue severity and depression level on the quality of life in individuals with Parkinson’s disease in Taiwan

Lin,

Chiou

et al. 2024

Front. Psychiatry

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BackgroundPatients diagnosed with Parkinson’s disease undergo alterations in physical, psychological, and social functionality, with the psychological domain being particularly predisposed to inducing fatigue and depressive symptoms. Understanding the alterations occurring within a patient’s body and mind and how these influence their overall quality of life is crucial.PurposeThis study sought to explore the predictive capacity of fatigue severity, the presence of depressive symptoms, and diverse demographic factors on the quality of life among individuals with Parkinson’s disease.MethodsA cross-sectional correlational study was conducted at a teaching hospital in southern Taiwan. The research utilized a questionnaire survey to interview 133 study participants, focusing on the Quality of Life Scale, Depression Scale, Fatigue Severity Scale, Social Support Scale, and demographic attributes.ResultsA total of 130 valid questionnaires were obtained. The results showed that Hoehn and Yahr stage, fatigue severity, and depression status could predict quality of life, explaining 51.1% of the total variance. These findings suggest that patients at advanced Hoehn and Yahr stages, experiencing more severe fatigue, and exhibiting higher levels of depression, tended to report a lower overall quality of life. Our findings suggest that, in addition to Hoehn and Yahr stage, the severity of fatigue and levels of depression significantly impact the quality of life in individuals with Parkinson’s disease.ConclusionNurses need to understand the “stressful life events” and the changes in appearance and physical function that patients with Parkinson’s disease face due to chronic degenerative diseases. Hence, apart from addressing patients’ physiological needs, healthcare professionals should also offer appropriate care for psychological issues, such as depressive symptoms. Encouraging patients to participate in “counseling groups” can further bolster their social support networks, enhancing their overall well-being and addressing comorbidities associated with chronic degenerative diseases.

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Section: Discussionsupporting

confidence: 92%

The impact of fatigue severity and depression level on the quality of life in individuals with Parkinson’s disease in Taiwan

Lin,

Chiou

et al. 2024

Front. Psychiatry

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“…However, the absence of mandatory PD notification complicates estimating its prevalence in the country. PD primarily affects the motor system, leading to various motor and non-motor symptoms that can deteriorate patients' quality of life [5][6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Figueredo,

Monteiro,

Silva

et al. 2024

Preprint

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Parkinson’s disease (PD) is a neurodegenerative disorder caused by a lack of dopamine secretion, resulting in motor dysfunction that affects speech production. The literature indicates that PD can be identified by observing changes in speech signals over time. Early detection of PD is essential to slow its progression and enable patients to access disease-modifying therapies. In this context, an adapted Convolutional Neural Network (CNN) for this task appears promising. In this paper, we propose a CNN classifier to efficiently detect PD with an accuracy above 95\%. We employed advanced machine learning techniques to identify PD early through voice analysis using the Parkinson’s Disease Voice Recording Data Set, which includes the vocal characteristics of 147 PD patients and 48 healthy individuals. The primary objective is to enhance the diagnostic accuracy of PD using a CNN tailored for this task. The preprocessing steps involved cleaning and normalizing the data, followed by the application of Bayesian hyperparameter optimization to improve model performance. The CNN model achieved an average accuracy of 96.8451\% in PD detection, demonstrating high precision, sensitivity, and a balanced F1 score. This result underscores the model’s effectiveness in identifying individuals with PD and highlights the potential of using a single CNN topology, as opposed to more complex hybrid models, to achieve satisfactory diagnostic rates.To evaluate the performance of the proposed approach, we used simulated data generated from the original dataset. The results indicate that the proposed approach is suitable for the automatic identification of PD in practical scenarios.

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Behavioral disorders in Parkinson disease: current view

Jellinger

2024

J Neural Transm

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Fatigue in Brazilian patients with Parkinson’s disease

Cited by 6 publications

References 42 publications

The impact of fatigue severity and depression level on the quality of life in individuals with Parkinson’s disease in Taiwan

The impact of fatigue severity and depression level on the quality of life in individuals with Parkinson’s disease in Taiwan

An approach for Parkinson's disease detection based on Artificial Inteligence techniques

Behavioral disorders in Parkinson disease: current view

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