Abstract
This paper presents a potential model for understanding the development and maintenance of Anorexia Nervosa, a serious and potentially life-threatening eating disorder characterized by severe food intake restrictions, leading to extreme weight loss and a distorted body image. The proposed model is an adaptive temporal-causal network model that considers the complex interactions between multiple factors over time, and allows for the identification of potential perpetuating cycles that may maintain the disorder. Specifically, this work focuses on the perpetuating cycle between restrictive dieting and anxiety. The model suggests that initial states of low self-acceptance, adherence to the thin ideal, and perfectionism can lead to states of body dissatisfaction and feeling worthless, which in turn may drive individuals to engage in restrictive dieting behaviors. These behaviors can lead to weight loss, which can temporarily reduce anxiety levels, but ultimately perpetuate the cycle of anxiety and restrictive dieting. The potential impact of therapy on breaking this perpetuating cycle is discussed, as a form of improving self-acceptance, body satisfaction, and overall mental health. The potential strengths and limitations of this model are discussed, together with suggestions of possible directions for future research in this area. Overall, this proposed model offers a novel approach for understanding the complex interactions that contribute to the development and maintenance of Anorexia Nervosa, and the potential benefits of therapeutic interventions in addressing these underlying issues.
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Inamdar, S., Liberatore, T., Treur, J. (2023). An Adaptive Network Model for Anorexia Nervosa: Addressing the Effects of Therapy. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_56
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