Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI.

The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community.

We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms.

Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence.

The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

To support school foods programmes by evaluating the relationship between nutritional quality, cost, student consumption and the environmental impacts of menus.

Using linear programming and data from previously served menu items, the relationships between the nutritional quality, cost, student consumption and the environmental impacts of lunch menus were investigated. Optimised lunch menus with the maximum potential student consumption and nutritional quality and lowest costs and environmental impacts were developed and compared with previously served menus (baseline).

Boston Public Schools (BPS), Boston Massachusetts, USA.

Menu items served on the 2018–2019 BPS lunch menu (n 142).

Using single-objective models, trade-offs were observed between most interests, but the use of multi-objective models minimised these trade-offs. Compared with the current weekly menus offered, multi-objective models increased potential caloric intake by up to 27 % and Healthy Eating Index scores by up to 19 % and reduced costs and environmental impacts by up to 13 % and 71 %, respectively. Improvements were made by reducing the frequency of beef and cheese entrées and increasing the frequency of fish and legume entrées on weekly menus.

This work can be extrapolated to monthly menus to provide further direction for school districts, and the methods can be employed with different recipes and constraints. Future research should test the implementation of optimised menus in schools and consider the broader implications of implementation.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health is still being unravelled. It is important to identify which individuals are at greatest risk of worsening symptoms. This study aimed to examine changes in depression, anxiety and post-traumatic stress disorder (PTSD) symptoms using prospective and retrospective symptom change assessments, and to find and examine the effect of key risk factors.

Online questionnaires were administered to 34 465 individuals (aged 16 years or above) in April/May 2020 in the UK, recruited from existing cohorts or via social media. Around one-third (n = 12 718) of included participants had prior diagnoses of depression or anxiety and had completed pre-pandemic mental health assessments (between September 2018 and February 2020), allowing prospective investigation of symptom change.

Prospective symptom analyses showed small decreases in depression (PHQ-9: −0.43 points) and anxiety [generalised anxiety disorder scale – 7 items (GAD)-7: −0.33 points] and increases in PTSD (PCL-6: 0.22 points). Conversely, retrospective symptom analyses demonstrated significant large increases (PHQ-9: 2.40; GAD-7 = 1.97), with 55% reported worsening mental health since the beginning of the pandemic on a global change rating. Across both prospective and retrospective measures of symptom change, worsening depression, anxiety and PTSD symptoms were associated with prior mental health diagnoses, female gender, young age and unemployed/student status.

We highlight the effect of prior mental health diagnoses on worsening mental health during the pandemic and confirm previously reported sociodemographic risk factors. Discrepancies between prospective and retrospective measures of changes in mental health may be related to recall bias-related underestimation of prior symptom severity.

Primary care providers (PCPs) are expected to help patients with obesity to lose weight through behavior change counseling and patient-centered use of available weight management resources. Yet, many PCPs face knowledge gaps and clinical time constraints that hinder their ability to successfully support patients’ weight loss. Fortunately, a small and growing number of physicians are now certified in obesity medicine through the American Board of Obesity Medicine (ABOM) and can provide personalized and effective obesity treatment to individual patients. Little is known, however, about how to extend the expertise of ABOM-certified physicians to support PCPs and their many patients with obesity.

To develop and pilot test an innovative care model – the Weight Navigation Program (WNP) – to integrate ABOM-certified physicians into primary care settings and to enhance the delivery of personalized, effective obesity care.

Quality improvement program with an embedded, 12-month, single-arm pilot study. Patients with obesity and ≥1 weight-related co-morbidity may be referred to the WNP by PCPs. All patients seen within the WNP during the first 12 months of clinical operations will be compared to a matched cohort of patients from another primary care site. We will recruit a subset of WNP patients (n = 30) to participate in a remote weight monitoring pilot program, which will include surveys at 0, 6, and 12 months, qualitative interviews at 0 and 6 months, and use of an electronic health record (EHR)-based text messaging program for remote weight monitoring.

Obesity is a complex chronic condition that requires evidence-based, personalized, and longitudinal care. To deliver such care in general practice, the WNP leverages the expertise of ABOM-certified physicians, health system and community weight management resources, and EHR-based population health management tools. The WNP is an innovative model with the potential to be implemented, scaled, and sustained in diverse primary care settings.

Poor mental health is a state of psychological distress that is influenced by lifestyle factors such as sleep, diet, and physical activity. Compulsivity is a transdiagnostic phenotype cutting across a range of mental illnesses including obsessive–compulsive disorder, substance-related and addictive disorders, and is also influenced by lifestyle. Yet, how lifestyle relates to compulsivity is presently unknown, but important to understand to gain insights into individual differences in mental health. We assessed (a) the relationships between compulsivity and diet quality, sleep quality, and physical activity, and (b) whether psychological distress statistically contributes to these relationships.

We collected harmonized data on compulsivity, psychological distress, and lifestyle from two independent samples (Australian n = 880 and US n = 829). We used mediation analyses to investigate bidirectional relationships between compulsivity and lifestyle factors, and the role of psychological distress.

Higher compulsivity was significantly related to poorer diet and sleep. Psychological distress statistically mediated the relationship between poorer sleep quality and higher compulsivity, and partially statistically mediated the relationship between poorer diet and higher compulsivity.

Lifestyle interventions in compulsivity may target psychological distress in the first instance, followed by sleep and diet quality. As psychological distress links aspects of lifestyle and compulsivity, focusing on mitigating and managing distress may offer a useful therapeutic approach to improve physical and mental health. Future research may focus on the specific sleep and diet patterns which may alter compulsivity over time to inform lifestyle targets for prevention and treatment of functionally impairing compulsive behaviors.

To develop a fully automated algorithm using data from the Veterans’ Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies.

Retrospective cohort study.

This study was conducted in 11 VA hospitals.

Patients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort).

Relevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans’ Affairs Surgical Quality Improvement Program (VASQIP).

Overall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively.

Considering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.