Countries in South East Asia are undergoing a nutrition transition, which typically involves a dietary shift from plant to animal proteins. To explore the main drivers of protein consumption, the SCRiPT (Socio Cultural Research in Protein Transition) study recruited a population sample in Malaysia (N = 1604). Participants completed in-person 24 h dietary recalls and socio-demographic surveys. Energy and nutrient intakes were estimated using Nutritionist Pro. A novel recipe-based frequency count coded protein sources as meat (chicken, beef, pork, and mutton), fish, eggs, dairy, and plants (cereals, pulses, tubers). Dietary intakes and frequencies were examined by gender, age, income, education, ethnicity, religion, and family status, using ANOVAs and general linear models. Energy intakes were 1869 kcal/d for men and 1699 kcal/d for women. Protein intakes were 78.5 g/d for men and 72.5 g/d for women. Higher energy and protein intakes were associated with Chinese ethnicity, higher education and incomes. Frequency counts identified plant proteins in 50% of foods, followed by meat (19%), fish (12%), eggs (12%), and dairy (7%). Most frequent source of meat was chicken (16%) rather than pork or beef (1.5% each). In bivariate analyses, animal protein counts were associated with younger age, higher education and incomes. In mutually adjusted multivariate regression models, animal proteins were associated with education and ethnicity; plant proteins were associated with ethnicity and religion. Protein choices in Malaysia involve socio-cultural as well as economic variables.

Higher consumption of ‘ultra-processed’ (UP) foods has been linked to adverse health outcomes. The present paper aims to characterise percentage energy from UP foods by participant socio-economic status (SES), diet quality, self-reported food expenditure and energy-adjusted diet cost. Participants in the population-based Seattle Obesity Study III (n755) conducted in WA in 2016–2017 completed socio-demographic and food expenditure surveys and the FFQ. Education and residential property values were measures of SES. Retail prices of FFQ component foods (n378) were used to estimate individual-level diet cost. Healthy Eating Index (HEI-2015) and Nutrient Rich Food Index 9.3 (NRF_9.3) were measures of diet quality. UP foods were identified following NOVA classification. Multivariable linear regressions were used to test associations between UP foods energy, socio-demographics, two estimates of food spending and diet quality measures. Higher percentage energy from UP foods was associated with higher energy density, lower HEI-2015 and NRF_9.3scores. The bottom decile of diet cost ($216·4/month) was associated with 67·5 % energy from UP foods; the top decile ($369·9/month) was associated with only 48·7 % energy from UP foods. Percentage energy from UP foods was inversely linked to lower food expenditures and diet cost. In multivariate analysis, percentage energy from UP foods was predicted by lower food expenditures, diet cost and education, adjusting for covariates. Percentage energy from UP foods was linked to lower food spending and lower SES. Efforts to reduce UP foods consumption, an increasingly common policy measure, need to take affordability, food expenditures and diet costs into account.

Summary: Background: In‐person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self‐reported PA and weight accuracy and identified patterns of misreporting in a diverse sample.Methods: King, Pierce and Yakima county residents, aged 21–59 years (n = 728), self‐reported their moderate‐to‐vigorous PA (MVPA) and weight, in kilograms. Self‐reports were compared with minutes of bout‐level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight.Results: MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40–49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA–obesity relation was similar using measured and self‐reported PA but not self‐reported weight. Perceived PA and weight predicted measured values.Conclusion: Self‐reported PA and weight may be useful should objective measurement be infeasible; however, though population‐specific adjustment for differential reporting should be considered.

Objective: Affordable nutrition refers to the relation between nutrient density of foods and their monetary cost. There are limited data on affordable nutrition in low- and middle-income countries. The present study aimed to develop a nutrient density score and nutrient affordability metrics for 377 most consumed foods in Brazil.Design: The foods were aggregated into seven major food groups and four NOVA food categories. Nutrient composition data were obtained from the Brazilian Institute of Geography and Statistics. Food prices were obtained from retailer websites and were converted to prices per 100 g and 418 kJ. The Nutrient Rich Food (NRF_8.2) score was based on protein, fiber, vitamins A, C and E, Ca, Fe and K. Nutrients to limit were sugar and Na. Affordability was measured as kcal/R$ and nutrients/R$.Results: Grains, fats and sweets were more energy dense and had lower NRF_8.2 scores than dairy, vegetables and fruits. Grains, fats and sweets were the lowest cost sources of energy. Vegetables and fruits, beans, nuts and seeds and eggs and dairy were the lowest cost sources of multiple nutrients. Ultra-processed foods (48 % of total) had higher energy density and lower NRF_8.2 scores than did unprocessed foods. In Brazil, fruits, vegetables and dairy products offered the most nutrients per real.Conclusions: Analysis of the relationship between nutrient density of foods and their cost can help identify locally available foods that are nutrient rich, affordable and culturally acceptable. Achieving high nutrient density at an affordable cost should be the goal of Brazil’s food systems.

Background: Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks. Objective: We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments. Methods: We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data. Results: We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values. Conclusions: Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions. International Registered Report Identifier (IRRID): DERR1-10.2196/16787