Shape and weight overvaluation is a core component of body image theorized to drive many of the symptoms of eating disorders (ED) and associated distress and impairment. Identifying variables that protect against the negative effects of shape and weight overvaluation is needed for informing primary intervention targets. Self-compassion may be a protective factor given its role as an adaptive affect regulation strategy. We thus examined whether self-compassion would attenuate the relationships between shape and weight overvaluation and ED psychopathology, psychosocial impairment, and psychological distress. Cross-sectional data were analyzed from 992 (619 women and 373 men) participants. Multiple regression analyses revealed that self-compassion moderated the relationship between shape and weight overvaluation and each dependent variable. Specifically, among men and women with lower levels of self-compassion, overvaluation of shape and weight was strongly associated with each of the criterion variables; however, these relationships were either absent or weaker among those with higher levels of self-compassion. Present findings suggest that it may be beneficial for ED prevention and early intervention programs to explicitly incorporate components of compassion-focused interventions to improve mental health outcomes among the general public.
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http://dx.doi.org/10.1016/j.bodyim.2020.03.001 | DOI Listing |
Cardiovasc Diabetol
January 2025
Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, People's Republic of China.
Background: Hypertension (HTN) is a global public health concern and a major risk factor for cardiovascular disease (CVD) and mortality. Insulin resistance (IR) plays a crucial role in HTN-related metabolic dysfunction, but its assessment remains challenging. The triglyceride-glucose (TyG) index and its derivatives (TyG-BMI, TyG-WC, and TyG-WHtR) have emerged as reliable IR markers.
View Article and Find Full Text PDFEat Behav
January 2025
Cincinnati Children's Hospital Medical Center, Division of Behavioral Medicine and Clinical Psychology, University of Cincinnati College of Medicine, Cincinnati, OH, USA. Electronic address:
Objective: This study identified mealtime challenges and emotions experienced during challenges among adolescents with anorexia nervosa (AN) or atypical anorexia nervosa (AAN) and their caregivers during the early phase of family-based treatment (FBT).
Method: Caregivers with high expressed emotion (i.e.
Cien Saude Colet
January 2025
Escola Nacional de Saúde Pública, Fundação Oswaldo Cruz. Rio de Janeiro RJ Brasil.
The scope of this study was to determine the diagnostic performance of ABSI for obesity and sarcopenic obesity, compared to the results of bioimpedance analysis (BIA) and BMI, by sex and age group. It involved a cross-sectional study with 12,793 participants in the second round of ELSA-Brasil (Longitudinal Study of Adult Health in Brazil), which obtained measurements of body fat percentage using BIA and anthropometry, verifying the performance of the diagnostic tests in order to compare the indices. The results showed that for obesity in men in all three age groups, the sensitivity was below 49%.
View Article and Find Full Text PDFPLoS One
January 2025
School of Mathematics and Finance, Hunan University of Humanities, Science and Technology, Loudi, China.
During the iterative process of the progressive iterative approximation, it is necessary to calculate the difference between the current interpolation curve and the corresponding data points, known as the adjustment vector. To achieve more precise adjustments of control points, this paper decomposes the adjustment vector into its coordinate components and introduces a weight for each component. By dynamically adjusting these weights, we can accelerate the convergence of iterations and enhance approximation accuracy.
View Article and Find Full Text PDFFront Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
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