Introduction Eating disorders (EDs) are serious mental illnesses with high rates of mortality, morbidity, and personal and societal costs. Onset of the Covid‐19 pandemic led to increased ED diagnoses in the general public, as well as worsening of ED symptoms in those with an existing ED diagnosis. Heightened prevalence and severity of EDs during the pandemic is complicated by the fact that traditional modes of ED care (specialty intensive treatment provided by a multidisciplinary team) have been difficult to access during the pandemic. Methods The current between‐groups study (N = 93 ED) tested a multidisciplinary intensive outpatient program (IOP) delivered via in‐person (pre‐pandemic; n = 60) and virtually via telehealth (during the pandemic; n = 33). Results We found no differences in outcomes via delivery mode, such that regardless of in‐person versus telehealth programming, ED symptoms, depression, and perfectionism significantly decreased and body mass index significantly increased. Conclusions Our findings suggest that a multi‐disciplinary telehealth ED IOP program is feasible and has comparable outcomes to in‐person IOP treatment. These findings have implications for treatment beyond the pandemic, suggesting that adoption of telehealth IOPs is warranted. Such delivery modes of intensive treatments for EDs could be expanded to reach underserved populations, especially in rural areas where treatment is often difficult to access.

Objective: The impact of food insecurity on mental health has not yet been examined in graduate students, a population widely considered at elevated risk for financial strain and negative mental health outcomes. This study aimed to derive initial prevalence estimates of food insecurity in a sample of current graduate students at a large state university and to elucidate the relationship between food insecurity and depression, anxiety and stress in this sample. Design: Cross-sectional online survey including the US Household Food Security Survey Module: Six-Item Short Form and the Depression, Anxiety, and Stress Scales (DASS-21). Setting: University in the northeastern region of the USA. Participants: Two hundred sixty-three graduate students. Results: In the present sample, 59·7 % of participants reported high/marginal food security, 18·5 % reported low food security and 21·8 % reported very low food security. Graduate students with very low food security reported significantly greater depression (η2 = 0·09), anxiety (η2 = 0·10) and stress (η2 = 0·10), compared with those with low food security and high food security (all P’s < 0·001). Conclusions: Food insecurity occurred in nearly half of the graduate students surveyed, and very low food security was associated with elevated levels of depression, anxiety and stress. Findings highlight the need to address food insecurity and associated elevated mental health problems present among graduate students.

Background In the past decade, network analysis (NA) has been applied to psychopathology to quantify complex symptom relationships. This statistical technique has demonstrated much promise, as it provides researchers the ability to identify relationships across many symptoms in one model and can identify central symptoms that may predict important clinical outcomes. However, network models are highly influenced by node selection, which could limit the generalizability of findings. The current study (N = 6850) tests a comprehensive, cognitive–behavioral model of eating-disorder symptoms using items from two, widely used measures (Eating Disorder Examination Questionnaire and Eating Pathology Symptoms Inventory). Methods We used NA to identify central symptoms and compared networks across the duration of illness (DOI), as chronicity is one of the only known predictors of poor outcome in eating disorders (EDs). Results Our results suggest that eating when not hungry and feeling fat were the most central symptoms across groups. There were no significant differences in network structure across DOI, meaning the connections between symptoms remained relatively consistent. However, differences emerged in central symptoms, such that cognitive symptoms related to overvaluation of weight/shape were central in individuals with shorter DOI, and behavioral central symptoms emerged more in medium and long DOI. Conclusions Our results have important implications for the treatment of individuals with enduring EDs, as they may have a different core, maintaining symptoms. Additionally, our findings highlight the importance of using comprehensive, theoretically- or empirically-derived models for NA.

Enhanced cognitive-behavioral therapy (CBT-E) is one of the primary evidence-based treatments for adults with eating disorders (EDs). However, up to 50% of individuals do not respond to CBT-E, likely because of the high heterogeneity present even within similar diagnoses. This high heterogeneity, especially in regard to presenting pathology, makes it difficult to develop a treatment based "on averages" and for clinicians to accurately pinpoint which symptoms should be targeted in treatment. As such, new models based at both the group, and individual level, are needed to more accurately refine targets for personalized evidence-based treatments that can lead to full remission. The current study (Expected N = 120 anorexia nervosa, atypical anorexia nervosa, and bulimia nervosa) will build both group and individual longitudinal models of ED behaviors, cognitions, affect, and physiology. We will collect data for 30 days utilizing a mobile application to assess behaviors, cognition, and affect and a sensor wristband that assesses physiology (heart rate, acceleration). We will also collect outcome data at 1-and 6-month follow-ups to assess ED outcomes and remission status. These data will allow for identification of "on average" and "individual" targets that maintain ED pathology and test if these targets predict outcomes, including ED remission.