3D Body shape for regional and appendicular body composition estimation.

Body composition is correlated to bone mineral density, muscle strength, and physical performance. This is important for diagnosing conditions like sarcopenia, which is defined as the age-associated decrease in muscle mass leading to decreased mobile function, increased frailty, and imbalance. Existing methods for body composition measurement either suffer from inaccurate results or require expensive equipment such as Dual-energy x-ray absorptiometry (DXA). Although DXA measures lean mass and not muscle mass, previous studies have considered extremity lean mass as appendicular skeletal muscle mass (ASMM) approximation. In this study, we develop a new shape descriptor to predict regional body composition (in particular, regional lean mass) from 3D body shapes. In addition, we propose a neural network for ASMM assessment which is calculated by lean mass. We evaluate the effectiveness by comparing adjusted R-Squared values and Root Mean Square Error (RMSE). In our experiment, the regression models utilizing level circumference as the training feature outperforms all regional anthropometric measurements and lowers the average RMSE by about 21%. For ASMM, the proposed neural network, which combines shape features and demographic features, surpasses all other traditional regression models and reaches the lowest RMSE at 1.85 kg. Compared to the vanilla linear regression model, our approach improves the RMSE by 17%. The experimental results suggest that the 3D body shape has the potential to be used to predict body composition, and in particular lean mass, for the whole body as well as specific regions of the body.

Liver Fat Assessment with Body Shape.

Hepatic steatosis has become a serious health concern among the general population, but especially for those who are obese. Liver fat can increase the risk of cirrhosis and even liver cancer. Current standard methods to assess hepatic steatosis, such as liver biopsy and CT/MR imaging techniques, are expensive and/or may have associated risks to health. In this paper, we use body shapes to assess hepatic steatosis using both traditional linear regression models and a deep neural network. We apply our models to a medical dataset and evaluate the approaches for both regression and classification. We compare the performance of several models via popular evaluation metrics. The experimental results indicate that our proposed neural network outperforms the vanilla linear regression model by 22.37% in RMSE and the accuracy by 18%. The R-squared value of the neural model is more than 0.72 and the accuracy reaches 78%. Hence, the body shape features can provide an additional accurate and affordable choice to monitor the degree of the patient's liver fat. Clinical relevance - This paper presents a low cost and convenient approach to predict liver fat percentage using body shapes. This approach will not replace the gold standard for assessing hepatic steatosis. However, with the wide availability for depth cameras (including on smartphones), the approach promises to provide another modality that can be deployed widely in clinical setting as well for home use for telehealth.

Mediating Effect of Loneliness in the Relationship between Depressive Symptoms and Cognitive Frailty in Community-Dwelling Older Adults.

BACKGROUND: This study aims to explore the mediating role of loneliness between depressive symptoms and cognitive frailty among older adults in the community. METHODS: A total of 527 community-dwelling older adults aged ≥ 60 years were included in this cross-sectional study. A five-item geriatric depression scale was used to assess depression symptoms. Then, an eight-item University of California at Los Angeles Loneliness Scale was used to assess loneliness. Moreover, the FRAIL scale and Mini-Mental State Examination were used to assess cognitive frailty. Furthermore, regression and bootstrap analyses were used to explore the mediating role of loneliness in depression symptoms and cognitive frailty. RESULTS: Loneliness mediates the association between depression symptoms and cognitive frailty (95% CI = 0.164~0.615), and after adjusting for loneliness, the direct effect is no longer significant (95% CI = -0.113~1.318, p = 0.099). CONCLUSIONS: Results show that the effect of cognitive frailty is not depression symptoms but loneliness. All levels of society (the government, medical institutions, and communities) need to pay more attention to the mental health of the older adults, screen for loneliness, and take timely intervention and treatment measures. They should also build an age-friendly society and promote active aging.