Cognitive fusion has been related to the development and maintenance of a series of mental health difficulties. Specifically, growing research on eating psychopathology has been demonstrating the important role of cognitive fusion related to body image in these disorders. Nonetheless, cognitive fusion specifically focused on eating remained to be investigated. The current study aimed at developing and validating the Cognitive Fusion Questionnaire-Food Craving, a measure assessing the extent to which an individual is fused with food-craving undesirable and disturbing thoughts and urges. This study was conducted with distinct samples comprising men and women from the student and general population. A principal component analysis was conducted to assess the scale's structure, which was further examined in a confirmatory factor analysis. The scale's reliability and validities were also analysed. Results indicated that the CFQ-FC presented a one-dimensional structure with 7 items, accounting for 66.14% of the variance. A CFA confirmed the plausibility of the measurement model, which was found to be invariant in both sexes. The CFQ-FC also revealed very good internal consistency, construct reliability, temporal stability, and convergent and divergent validity, being positively associated with similar constructs and with indicators of eating and general psychopathology. CFQ-FC also discriminated individuals with clinically significant symptoms of binge eating from participants with no symptoms. Finally, the CFQ-FC presents incremental validity over a global measure of cognitive fusion in predicting eating psychopathology, namely binge eating. The CFQ-FC is a psychometrically sound measure that allows for a brief and reliable assessment of eating-related cognitive fusion. This is a novel measure that may significantly contribute for the assessment of this specific dimension of cognitive fusion and for the understanding of its role in eating psychopathology.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.appet.2016.03.004DOI Listing

Publication Analysis

Top Keywords

cognitive fusion
32
eating psychopathology
12
cognitive
8
fusion
8
measure cognitive
8
binge eating
8
eating
7
measure
5
cfq-fc
5
caught struggle
4

Similar Publications

Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.

Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.

View Article and Find Full Text PDF

This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.

View Article and Find Full Text PDF

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.

View Article and Find Full Text PDF

This study examined relationships among caregiver burden, depressive symptoms, and key processes related to psychological flexibility (experiential avoidance, cognitive fusion, values-driven actions, and mindfulness) in 157 family caregivers of individuals with dementia in the United States. Path analyses were used. Participants' mean age was 59.

View Article and Find Full Text PDF

Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!