Loss of functional capacity in elderly individuals with Alzheimer disease

Andrade, Susan Kelly Damião do Rego e Silva; Melo, Maria Clara Silva de; Filho, Bartolomeu Fagundes de Lima; Porto, Fábio Henrique de Gobbi; Peixoto, Vanessa Giffoni de Medeiros Nunes Pinheiro; Gazzola, Juliana Maria

doi:10.1590/1980-57642020dn14-040009

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…Além disso, hoje no Brasil, a negligência acerca de fatores sociais, econômicos e clínicos entre os indivíduos com DA promove uma desproporção no acesso, cobertura e tratamento (ANDRADE et al, 2020).…”

Section: Resultados Epidemiologiaunclassified

Doença de Alzheimer: uma revisão de literatura/ Alzheimer's Disease: A Literature Review

Borstel¹,

Yoshimura²,

Zaura³

et al. 2021

Braz. J. Hea. Rev.

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RESUMOCom o envelhecimento populacional atual, as doenças neurodegenerativas como a Doença de Alzheimer (DA) tornam-se um grande desafio à saúde pública e qualidade de vida dos indivíduos portadores. A DA é uma desordem crônica, progressiva e irreversível, com evolução insidiosa e de etiologia desconhecida. As principais manifestações são a perda de memória e autonomia levando a prejuízos nas relações sociais e cognição. Tal doença acomete cerca de 5% dos indivíduos acima de 65 anos. Com o aumento da longevidade, a prevalência de DA tende também a aumentar. Dentre os fatores de risco, podem ser destacados idade avançada e história familiar positiva. O diagnóstico é majoritariamente clínico, realizado por anamnese e avaliação neuropsicológica que analisam os domínios cognitivos do paciente. Sendo os estudos dos biomarcadores um grande avanço para o diagnóstico, acompanhamento e prevenção da DA. O tratamento é multifatorial, composto por dois segmentos terapêuticos sintomatológicos, o primeiro farmacológico, e o seguinte não farmacológico ou comportamental. O objetivo do presente trabalho foi fazer uma revisão da literatura sobre o tema.

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Section: Resultados Epidemiologiaunclassified

Doença de Alzheimer: uma revisão de literatura/ Alzheimer's Disease: A Literature Review

Borstel¹,

Yoshimura²,

Zaura³

et al. 2021

Braz. J. Hea. Rev.

View full text Add to dashboard Cite

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Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning

Nassajpour,

Shuqair,

Rosenfeld

et al. 2024

Front. Digit. Health

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Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model’s consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.

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Loss of functional capacity in elderly individuals with Alzheimer disease

Cited by 2 publications

References 48 publications

Doença de Alzheimer: uma revisão de literatura/ Alzheimer's Disease: A Literature Review

Doença de Alzheimer: uma revisão de literatura/ Alzheimer's Disease: A Literature Review

Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning

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