Exposure to anti-Black Lives Matter movement and obesity of the Black population

"Now I just need something sweet": Racism, emotional eating, and health among African Americans

BACKGROUND

The Environmental Affordances Model theorizes that systemic racism disproportionately exposes African Americans in the United States to chronic everyday stressors (e.g., individual racism) while simultaneously shaping the availability of coping resources (e.g., fast food outlets) and engagement in self-regulatory strategies (e.g., emotional eating). Greater engagement in self-regulatory strategies is theorized to preserve mental health while contributing to medical morbidities and mortality.

OBJECTIVE

However, few studies have tested the Environmental Affordances Model, limiting our understanding of how the proposed pathways operate in the lives of African Americans.

METHODS

In the present study, the associations between systemic racism (institutional racism, cultural racism, neighborhood disadvantage), chronic everyday stressors (exposure to individual racism), emotional eating, and mental (anxiety symptomatology) and physical (self-rated overall physical health) health are assessed in a sample of 751 African Americans aged 18 to 88.

RESULTS

The path analysis reveals that institutional and cultural racism are both positively associated with individual racism. Neighborhood disadvantage is inversely associated with individual racism. Individual racism is significantly associated with greater anxiety symptomatology but is unrelated to self-rated overall physical health. Institutional and cultural racism are associated with emotional eating although individual racism and neighborhood disadvantage are not. Moreover, engagement in emotional eating exacerbates, rather than mitigates, the impacts of individual racism on anxiety symptomatology.

CONCLUSIONS

We conclude that institutional and cultural racism contribute to individual racism experiences and emotional eating whereas emotional eating exacerbates associations among individual racism and anxiety symptomatology.

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

BACKGROUND

Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.

OBJECTIVE

This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.

METHODS

We conducted a scoping review in PubMed and Web of Science on the applications of AI to measure, predict, and treat obesity. We summarized and categorized the AI methodologies used in the hope of identifying synergies, patterns, and trends to inform future investigations. We also provided a high-level, beginner-friendly introduction to the core methodologies to facilitate the dissemination and adoption of various AI techniques.

RESULTS

We identified 46 studies that used diverse ML and DL models to assess obesity-related outcomes. The studies found AI models helpful in detecting clinically meaningful patterns of obesity or relationships between specific covariates and weight outcomes. The majority (18/22, 82%) of the studies comparing AI models with conventional statistical approaches found that the AI models achieved higher prediction accuracy on test data. Some (5/46, 11%) of the studies comparing the performances of different AI models revealed mixed results, indicating the high contingency of model performance on the data set and task it was applied to. An accelerating trend of adopting state-of-the-art DL models over standard ML models was observed to address challenging computer vision and natural language processing tasks. We concisely introduced the popular ML and DL models and summarized their specific applications in the studies included in the review.

CONCLUSIONS

This study reviewed AI-related methodologies adopted in the obesity literature, particularly ML and DL models applied to tabular, image, and text data. The review also discussed emerging trends such as multimodal or multitask AI models, synthetic data generation, and human-in-the-loop that may witness increasing applications in obesity research.

Theoretical Approaches to Research on the Social Determinants of Obesity

This article reviews selected theoretical approaches explaining the social determinants of obesity. The significance of this topic for medicine, public health, and other areas of obesity-related research is the growing body of evidence showing that the social environment is often key to understanding the risk of obesity. A review of relevant literature and analysis of empirical evidence linking theory to data in studies of obesity was performed. Several studies show that differences in social behavior and living conditions associated with SES, lifestyles, inequality, and other social variables have important roles in weight gain. Because the social determinants of obesity often begin in childhood, life course theory and its concepts of cumulative advantage/disadvantage and cumulative inequality are initially reviewed, followed by a discussion of how fundamental cause theory, health lifestyle theory, and cultural capital theory can be applied to obesity research. The stress process model and the concepts of social networks and neighborhood disadvantage concerning obesity are also included.