Obesity is a major risk factor for adverse outcomes after infection with SARS-CoV-2. We aimed to examine this association, including interactions with demographic and behavioural characteristics, type 2 diabetes, and other health conditions. In this prospective, community-based, cohort study, we used de-identified patient-level data from the QResearch database of general practices in England, UK. We extracted data for patients aged 20 years and older who were registered at a practice eligible for inclusion in the QResearch database between Jan 24, 2020 (date of the first recorded infection in the UK) and April 30, 2020, and with available data on BMI. Data extracted included demographic, clinical, clinical values linked with Public Health England's database of positive SARS-CoV-2 test results, and death certificates from the Office of National Statistics. Outcomes, as a proxy measure of severe COVID-19, were admission to hospital, admission to an intensive care unit (ICU), and death due to COVID-19. We used Cox proportional hazard models to estimate the risk of severe COVID-19, sequentially adjusting for demographic characteristics, behavioural factors, and comorbidities. Among 6 910 695 eligible individuals (mean BMI 26·78 kg/m2 [SD 5·59]), 13 503 (0·20%) were admitted to hospital, 1601 (0·02%) to an ICU, and 5479 (0·08%) died after a positive test for SARS-CoV-2. We found J-shaped associations between BMI and admission to hospital due to COVID-19 (adjusted hazard ratio [HR] per kg/m2 from the nadir at BMI of 23 kg/m2 of 1·05 [95% CI 1·05–1·05]) and death (1·04 [1·04–1·05]), and a linear association across the whole BMI range with ICU admission (1·10 [1·09–1·10]). We found a significant interaction between BMI and age and ethnicity, with higher HR per kg/m2 above BMI 23 kg/m2 for younger people (adjusted HR per kg/m2 above BMI 23 kg/m2 for hospital admission 1·09 [95% CI 1·08–1·10] in 20–39 years age group vs 80–100 years group 1·01 [1·00–1·02]) and Black people than White people (1·07 [1·06–1·08] vs 1·04 [1·04–1·05]). The risk of admission to hospital and ICU due to COVID-19 associated with unit increase in BMI was slightly lower in people with type 2 diabetes, hypertension, and cardiovascular disease than in those without these morbidities. At a BMI of more than 23 kg/m2, we found a linear increase in risk of severe COVID-19 leading to admission to hospital and death, and a linear increase in admission to an ICU across the whole BMI range, which is not attributable to excess risks of related diseases. The relative risk due to increasing BMI is particularly notable people younger than 40 years and of Black ethnicity. NIHR Oxford Biomedical Research Centre.

Nationally representative surveys of food intake in U.S. children show large increases in snacking between the 1989–91 to 1994–98 and 1994–98 to 2003–06 periods. Childhood snacking trends are movin...

This study built on limited knowledge about patterns and trends of adult snacking in the US. We selected adults aged 19 y and older (n = 44,754) betw

Background: New technology-based dietary assessment tools, including Web-based programs, mobile applications, and wearable devices, may improve accuracy and reduce costs of dietary data collection and processing. The International Life Sciences Institute (ILSI) Europe Dietary Intake and Exposure Task Force launched this project to evaluate new tools in order to recommend general quality standards for future applications. Methods: A comprehensive literature search identified technology-based dietary assessment tools, including those published in English from 01/2011 to 09/2017, and providing details on tool features, functions and uses. Each of the 43 tools identified (33 for research and 10 designed for consumer use) was rated on 25 attributes. Results: Most of the tools identified (79%) relied on self-reported dietary intakes. Most (91%) used text entry and 33% used digital images to help identify foods. Only 65% had integrated databases for estimating energy or nutrients. Fewer than 50% contained any features of customization and about half generated automatic reports. Most tools reported on usability or reported validity compared with another assessment method (77%). A set of Best Practice Guidelines was developed for reporting dietary assessment tools using new technology. Conclusions: Dietary assessment methods that utilize technology offer many advantages for research and are often preferable to consumers over more traditional methods. In order to meet general quality standards, new technology tools require detailed publications describing tool development, food identification and quantification, customization, outputs, food composition tables used, and usability/validity testing.

Diet is an important determinant of health, and food purchasing is a key antecedent to consumption. We set out to evaluate the effectiveness of grocery store interventions to change food purchasing, and to examine whether effectiveness varied based on intervention components, setting, or socioeconomic status. We conducted a systematic review of randomized controlled trials (search performed June 2017). Studies must have: aimed to change food purchasing; been implemented in grocery stores (real or simulated); reported purchasing; and had a minimal control or compared interventions fulfilling our criteria. Searching, screening, bias assessment, and data extraction followed Cochrane methods. We grouped studies by intervention type (economic, environmental, swaps, and/or education), synthesized results narratively, and conducted an exploratory qualitative comparative analysis. We included 35 studies representing 89 interventions, >20,000 participants, and >800 stores. Risk of bias was mixed. Economic interventions showed the most promise, with 8 of the 9 studies in real stores and all 6 in simulated environments detecting an effect on purchasing. Swap interventions appeared promising in the 2 studies based in real stores. Store environment interventions showed mixed effects. Education-only interventions appeared effective in simulated environments but not in real stores. Available data suggested that effects of economic interventions did not differ by socioeconomic status, whereas for other interventions impact was variable. In our qualitative comparative analysis, economic interventions (regardless of setting) and environmental and swap interventions in real stores were associated with statistically significant changes in purchasing in the desired direction for ≥1 of the foods targeted by the intervention, whereas education-only interventions in real stores were not. Findings suggest that interventions implemented in grocery stores—particularly ones that manipulate price, suggest swaps, and perhaps manipulate item availability—have an impact on purchasing and could play a role in public health strategies to improve health. Review protocol registered at https://www.crd.york.ac.uk/PROSPERO/ as CRD42017068809.

Summary: Meal replacements (MR) are generally not recommended in clinical guidelines for the management of obesity. The aim of this review is to provide an up‐to‐date systematic evaluation of the effect of weight loss interventions incorporating MR compared with alternative interventions on weight change at 1 year in adults with overweight or obesity. Six electronic databases were searched from inception to the end of August 2018 for randomized controlled trials comparing the effect of MR with interventions that did not include MR on weight at 1 year. We excluded studies using diets providing <3347 kJ/(800 kcal)/day and those which used total diet replacement (TDR) from this review. Risk of bias was assessed using the Cochrane risk of bias tool. Twenty‐three studies with 7884 adult participants were included. Six out of 23 studies were judged at low risk of bias across all domains, and 5/23 studies were judged at high risk of bias in at least one domain. Studies with similar intervention and comparators were grouped into five comparisons for analysis. Mean weight change at 1 year favoured the MR group relative to the control group in each comparison. In those comparisons where we conducted meta‐analysis, in people assigned to a diet incorporating MR, mean difference was −1.44 kg (−2.48 to −0.39 kg; I² = 38%) compared with alternative kinds of diets. In those assigned to a MR diet along with support, mean difference was −2.22 kg (−3.99 to −0.45, I² = 81%) compared with other diets with support and −3.87 kg (−7.34 to −0.40; I² = 60%) compared with other kinds of diet without support. In those assigned a MR diet with an enhanced level of support, mean difference was −6.13 kg (−7.35 to −4.91, I² = 19%) compared with alternative diets and regular support. Programmes incorporating meal replacements led to greater weight loss at 1 year than comparator weight loss programmes and should be considered as a valid option for management of overweight and obesity in community and health care settings.