Purpose: The present study examined time trends in adolescent consumption of sugar-sweetened beverages and energy drinks among secondary school students in Wales, United Kingdom. This is the first study to examine such historical trends and to model change in inequalities over time.

Methods: Between 1998 to 2017, students aged 11-16 years old completed the Health Behaviour in School-aged Children (HBSC) survey and the Welsh School Health Research Network (SHRN) survey. Surveys were conducted approximately every two years from 1998 to 2017 and data appended over the years to create a repeated cross-sectional dataset. Questions on drink consumption asked; ‘How many times a week do you usually drink Coke or other soft drinks that contain sugar?’ and ‘How many times a week do you usually drink energy drinks (such as Red Bull, Monster, and Rockstar)?’ For both questions, responses were recoded to form a three-category variable; ‘Never or less than weekly’ (included ‘Never’ and ‘Less than weekly’), ‘Weekly’ (included ‘Weekly/once a week’, ‘2–4 times a week’ and ‘5–6 times a week’) and ‘Daily or more’ (included ‘Daily’ and ‘More than one a day’). Sociodemographic questions concerned gender, year group and family affluence. Multinomial regression models were employed alongside tests for interaction effects.

Results: The sample comprised 176,094 student responses (49% boys). The prevalence of daily sugar-sweetened beverage consumption decreased between 1998 (57%) to 2017 (18%) while weekly consumption remained constant since 2006 (49% to 52%). From 2013 to 2017, daily consumption of energy drinks remained stable (6%) while weekly consumption reports steadily decreased (23% to 15%). Higher consumption rates of sugar-sweetened beverages and energy drinks were found among boys, older children and those from a low socioeconomic group. The only characteristic to show a statistically significant change over time was consumption according to socioeconomic group, revealing a widening disparity between sugar-sweetened beverage consumption rates of those from low and high socioeconomic groups.

Conclusions: While results indicate a positive shift in overall consumption rates of both sugar-sweetened beverages and energy drinks, urgent policy action is required with close attention to equity of impact throughout policy design and evaluation plans.

The role of alcohol on body weight regulation, food intake, and appetite has received much attention, however, the consensus is that we still don’t completely understand the role of alcohol in weight regulation. Recent evidence has demonstrated that alcohol intake increases circulating levels of fibroblast growth factor 21(FGF-21), suppresses appetite for alcohol and carbohydrate, and increases appetite for protein. We tested the predictions that i. FGF21-elicited protein seeking will result in increased proportional protein intake when drinking alcohol; ii. the strong satiating effect of increased protein will be associated with decreased energy intake (“protein leverage”), but iii. when protein seeking results in consumption of umami-flavoured (savory) low protein snack foods (the “protein decoy effect”) increased energy intake will be associated with alcohol consumption. We used proportions-based nutritional geometry and mixture models to test these in participants which were measured with one 24-hour recall in the Australian National Nutrition and Physical Activity Survey (n=9,341 adults). One alcohol peak corresponded with high protein and low carbohydrate and fat (%E) and total energy intake was below estimated energy requirements (EER). Alcohol in this region was accompanied by lean meats, poultry, fish, seafood, nuts, or legumes. A second alcohol peak corresponded with moderate protein (%E) and high fat (%E) and higher energy intake than the EER. This was partly attributed to a higher intake of “protein decoys” including savory snack foods and processed meats. The third peak in alcohol was due to intake from spirits and was accompanied by energy from sugary beverages and was low in protein and fat (%E) but high in carbohydrate (%E) and total energy intake. Our results are consistent with increased FGF-21 stimulated by alcohol consumption causing protein seeking and the resulting increase in the dietary proportion of protein reducing total energy intake via protein leverage. However, if processed “protein decoys” are eaten instead of whole foods, the reduced proportional dietary protein leverages excess energy intake. These results could resolve contradictory epidemiological evidence that demonstrates no relationship or an inverse relationship with weight gain or otherwise finds alcohol a risk factor for overweight but not obesity.

Purpose:

Most evidence on the neighborhood food environment (FE) influence on obesity is from high-income countries (HICs). Mexico has been undergoing an obesity epidemic for the past two decades, and obesogenic environments are thought to be an important contributor. However, little research using objective measures to assess the FE of residential neighborhoods in relation to objectively measured obesity outcomes has been conducted in Mexico. Furthermore, the FE of Mexican cities varies widely from that of HICs settings, given the substantial presence of informal food vendors. This study aimed to examine the relationship between a comprehensive set of objectively assessed FE variables and obesity among a representative sample of adults from Mexico City.

Methods:

A cross-sectional study was conducted in Mexico City, Mexico, between May and December 2018. A multistage, clustered, stratified sampling approach was used to draw a representative sample of adults. Body mass index (BMI) and waist circumference (WC) were objectively measured and were used to calculate dichotomous variables denoting having/not having BMI- and abdominal obesity. Using environmental audits and GIS, the presence of eighteen food-vending locations, including formal and informal food vending outlets, within a 500-meter radius of each participant's geocoded home, were calculated. Multilevel logistic regression was used to estimate the associations between food environment variables (e.g., street food stalls, fast-food restaurants, full-service restaurants, supermarkets, markets, bodegas/grocery stores, convenience stores, baked goods, bars, and liquor stores) and BMI-based and abdominal obesity.

Results:

There were no statistically significant associations between the examined FE characteristics and either BMI- or WC-based obesity (p>0.05 for all tested associations). However, the interclass correlation coefficients revealed that some neighborhood-level clustering is present and is responsible for 9% and 11% of the variability of BMI-based obesity and WC-based obesity, respectively.  

Conclusion: This was the first study to examine the association between objectively measured FE exposures and objectively measured obesity status among Mexican adults. The null findings are inconsistent with reports from other global settings on the association between food outlet density and obesity, warranting further study. In addition, our findings suggest caution in generalizing findings from high-income countries to low- and middle-income countries.

Purpose: With accelerating digitalisation and growing reliance on online technologies to facilitate contactless living, the way we are accessing foods is changing. Online food delivery services (OFDS) potentially further exacerbate unhealthy takeaway food consumption as menu items are delivered straight from food outlets to doorstep. Mobile Ecological Momentary Assessment (mEMA) is a method of data collection to gather real-time and contextual data from participants as they go about their daily lives. This pilot study aims to determine the feasibility and acceptability of a mEMA protocol in a sample of young people who have used OFDS. A secondary aim is to identify preliminary associations between frequency of OFDS usage, lifestyle chronic disease risk factors and related food selection behaviours.

Methods: This cross-sectional study will be conducted online using the Ilumivu mEMA software. Approximately 100 participants who are aged between 16-35 years, living in Australia, have access to a smartphone and are existing users of OFDS will be recruited via targeted social media advertising. Participants will be monitored over 3 days including 2 weekdays (Monday-Thursday) and 1 day on the weekend (Friday, Saturday or Sunday) between 7:00am and 11:00pm. Six EMA surveys consisting of 8-tems will be prompted via the Ilumivu mEMA smartphone app on each of the 3 days.

Results: Expected results will establish which OFDS is most used amongst the study sample, what menu items were ordered, and factors influencing food choice such as price (economical), hunger, taste and appetite (biological), emotional status (psychological), physical access to foods and knowledge/cooking skills (physical) and their dining setting and patterns concerning culture, family, peers (social). Feasibility will be reported as the percentage of number of mEMA surveys answered compared to expected total number of mEMA surveys completed. Acceptability of the protocol will determine the clarity of questions asked, ease of use and perceived participant burden.

Conclusion: Using mEMA to obtain data on dietary intake and related behaviours may be a highly feasible and acceptable method. The information gathered from mEMA may be critical to understanding the consumption of online food delivery in young people, hence enabling the development of future targeted interventions.