Higher sedentary energy expenditure in patients with Huntington's disease

2000 ◽  
Vol 47 (1) ◽  
pp. 64-70 ◽  
Author(s):  
Richard E. Pratley ◽  
Arline D. Salbe ◽  
Eric Ravussin ◽  
John N. Caviness
2015 ◽  
Vol 67 (4) ◽  
pp. 267-273
Author(s):  
Cecilia Gil Polo ◽  
Esther Cubo Delgado ◽  
Ana Mateos Cachorro ◽  
Jéssica Rivadeneyra Posadas ◽  
Natividad Mariscal Pérez ◽  
...  

Introduction: Little is known about the energy needs in Huntington's disease (HD). The aims of this study are to analyze and compare the total energy expenditure (TEE) and energy balance (EB) in a representative sample of HD patients with healthy controls. Methods: This is an observational, case-control single-center study. Food caloric energy intake (EI) and TEE were considered for estimating EB. A dietary recall questionnaire was used to assess the EI. TEE was computed as the sum of resting energy expenditure (REE), measured by indirect calorimetry and physical activity (PA) monitored by an actigraph. Results: A total of 22 patients were included (36% men, mean age 50.3 ± 15.6 years, motor Unified Huntington's Disease Scale 27.9 ± 23.7, total functional capacity 11.0 (7.0-13.0), EI 38.6 ± 10.0 kcal/kg, PA 5.3 (3.0-7.4) kcal/kg, REE 30.9 ± 6.4 kcal/kg, TEE 2,023.4 (1,592.0-2,226.5) kcal/day) and 18 controls (50% men, mean age 47.4 ± 13.8 years, EI 38.6 ± 10.3 kcal/kg, PA 8.4 (5.0-13.8) kcal/kg, REE 30.8 ± 6.6 kcal/kg, TEE 2,281.0 (2,057.3-2,855.3) kcal/day). TEE was significantly lower in patients compared to controls (p = 0.03). PA was lower in patients compared to controls (p = 0.02). Conclusions: Although patients with HD appeared to have lower energy expenditure, mainly due to decreased voluntary PA, they were still able to maintain their energy needs with an adequate food intake.


Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3236 ◽  
Author(s):  
Andrius Lauraitis ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Tomas Krilavičius

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.


2008 ◽  
Vol 35 (S 01) ◽  
Author(s):  
M Mühlau ◽  
A Wohlschläger ◽  
C Gaser ◽  
M Valet ◽  
S Nunnemann ◽  
...  

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