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To model dietary changes required to shift the UK population to diets that meet dietary recommendations for health, have lower greenhouse gas emissions (GHGE) and are affordable for different income groups. Linear programming was used to create diets that meet dietary requirements for health and reduced GHGE (57 and 80 % targets) by income quintile, taking account of food budgets and foods currently purchased, thereby keeping dietary change to a minimum.Setting/ParticipantsNutrient composition, GHGE and price data were mapped to 101 food groups in household food purchase data (UK Living Cost and Food Survey (2013), 5144 households). Current diets of all income quintiles had similar total GHGE, but the source of GHGE differed by types of meat and amount of fruit and vegetables. It was possible to create diets with a 57 % reduction in GHGE that met dietary and cost restraints in all income groups. In the optimised diets, the food sources of GHGE differed by income group due to the cost and keeping the level of deviation from current diets to a minimum. Broadly, the changes needed were similar across all groups; reducing animal-based products and increasing plant-based foods but varied by specific foods. Healthy and lower-GHGE diets could be created in all income quintiles but tailoring changes to income groups to minimise deviation may make dietary changes more achievable. Specific attention must be given to make interventions and policies appropriate for all income groups.

PurposeEnergy intake varies day-to-day because we select different foods, and different amounts of these foods. Energy balance is not tightly regulated over the short-term, and the variability in diet results in an energy surplus or deficit. The aim of this study was to explore how consuming more, or less, than usual amounts of foods contributed towards balancing of total energy intake (TEI) within a day.MethodsFour-day food records came from 6155 adult participants of the National Diet and Nutrition Survey to study these effects. Within-individual regression models of the energy from 60 food groups on TEI were calculated. Energy intake variation within-individuals was regressed separately on the variation in amounts of each food group. Regression models were also fitted to individual four day means.ResultsWithin-individual coefficients ranged from about 0 for high-fibre breakfast cereals to 1.7 for sugar preserves and spreads. Three food groups (e.g. low-calorie soft drinks) tended to reduce TEI, and 13 food groups (e.g. margarine and other spreads, and alcoholic drinks) tended to elevate TEI above the energy content of the food group when more than usual amounts were consumed. Foods groups of higher energy densities, or lower fibre content (e.g. typical “snack” foods, low-fibre bread, and processed meat) tended to promote greater TEI more so than did food groups of lower energy densities (e.g. meat, fish, high-fibre foods, and potatoes).ConclusionDifferent food groups vary considerably in the extent to which they affect TEI in free-living adults. The associations between consuming more, or less, than usual amounts of foods and the effects on TEI are consistent with those found in laboratory studies. Importantly, the present study found similar associations, but using a different methodology and in observational data, providing novel information on energy intake compensation.

Background Diet norms are the shared social behaviours and beliefs about diets. In many societies, including the UK, these norms are typically linked to unhealthy diets and impede efforts to improve food choices. Social interactions that could influence one another’s food choices, were highly disrupted during the lockdowns in response to the COVID-19 pandemic. A return to workplaces and re-establishment of eating networks may present an opportunity to influence dietary norms by introducing minimum dietary standards to in workplaces, which could then spread through wider home and workplace networks. Methods An agent-based model was constructed to simulate a society reflecting the structure of a city population (1000 households) to explore changes in personal and social diet-related norms. The model tracked individual meal choices as agents interact in home, work or school settings and recorded changes in diet quality (range 1 to 100). Scenarios were run to compare individuals’ diet quality with the introduction of minimum dietary standards with degrees of working from home. Results The more people mixed at work the greater the impact of minimum standards on improving diet norms. Socially isolated households remained unaffected by minimum standards, whereas household members exposed directly, in workplaces or schools, or indirectly, influenced by others in the household, had a large and linear increase in diet quality in relation to minimum standards (0.48 [95% CI 0.34, 0.62] per unit increase in minimum standards). Since individuals regressed to the new population mean, a small proportion of diets decreased toward lower population norms. The degree of return to work influenced the rate and magnitude of change cross the population (-2.4 points [-2.40, -2.34] in mean diet quality per 20% of workers isolating). Conclusions These model results illustrate the qualitative impact social connectivity could have on changing diets through interventions. Norms can be changed more in a more connected population, and social interactions spread norms between contexts and amplified the influence of, for example, workplace minimum standards beyond those directly exposed. However, implementation of minimum standards in a single type of setting would not reach the whole population and in some cases may decrease diet quality. Any non-zero standard could yield improvements beyond the immediate adult workforce and this could spill between social contexts, but would be contingent on population connectivity.

Background Average population dietary intakes do not reflect the wide diversity of dietary patterns across the population. It is recognised that most people in the UK do not meet dietary recommendations and have diets with a high environmental impact, but changing dietary habits has proved very difficult. The purpose of this study was to investigate the diversity in dietary changes needed to achieve a healthy diet and a healthy diet with lower greenhouse gas emissions (GHGE) (referred to as a sustainable diet ) by taking into account each individual’s current diet and then minimising the changes they need to make. Methods Linear programming was used to construct two new diets for each adult in the UK National Diet and Nutrition Survey ( n  = 1491) by minimising the changes to their current intake. Stepwise changes were applied until (i) dietary recommendations were achieved and (ii) dietary recommendations and a GHGE target were met. First, gradual changes (≤50 %) were made to the amount of any foods currently eaten. Second, new foods were added to the diet. Third, greater reductions (≤75 %) were made to the amount of any food currently eaten and finally, foods were removed from the diet. Results One person out of 1491 in the sample met all the dietary requirements based on their reported dietary intake. Only 7.5 and 4.6 % of people achieved a healthy diet and a sustainable diet, respectively, by changing the amount of any food they currently ate by up to 50 %. The majority required changes to the amount of each food eaten plus the addition of new foods. Fewer than 5 % had to remove foods they ate to meet recommendations. Sodium proved the most difficult nutrient recommendation to meet. The healthy diets and sustainable diets produced a 15 and 27 % reduction in greenhouse gas emissions respectively. Conclusions Since healthy diets alone do not produce substantial reductions in greenhouse gas emissions, dietary guidelines need to include recommendations for environmental sustainability. Minimising the shift from current dietary intakes is likely to make dietary change more realistic and achievable.

From 2008, the UK’s National Diet and Nutrition Survey (NDNS) changed the method of dietary data collection from a 7-d weighed diary to a 4-d unweighed diary, partly to reduce participant burden. This study aimed to test whether self-reported energy intake changed significantly over the 4-d recording period of the NDNS rolling programme. Analyses used data from the NDNS years 1 (2008/2009) to 8 (2015/2016) inclusive, from participants aged 13 years and older. Dietary records from participants who reported unusual amounts of food and drink consumed on one or more days were excluded, leaving 6932 participants. Mean daily energy intake was 7107 kJ (1698 kcal), and there was a significant decrease of 164 kJ (39 kcal) between days 1 and 4 (P < 0·001). There was no significant interaction of sex or low-energy reporter status (estimated from the ratio of reported energy intake:BMR) with the change in reported energy intake. The decrease in reported energy intake on day 4 compared with day 1 was greater (P < 0·019) for adults with higher BMI (>30 kg/m2) than it was for leaner adults. Reported energy intake decreased over the 4-d recording period of the NDNS rolling programme suggesting that participants change their diet more, or report less completely, with successive days of recording their diet. The size of the effect was relatively minor, however.

BackgroundThere is evidence that the energetic demand of metabolically active tissue is associated with day-to-day food intake (EI). However, the extent to which behavioural components of total daily energy expenditure (EE) such as activity energy expenditure (AEE) are also associated with EI is unknown. Therefore, the present study examined the cross-sectional associations between body composition, resting metabolic rate (RMR), AEE and EI.MethodsData for 242 individuals (114 males; 128 females; BMI = 25.7 ± 4.9 kg/m2) were collated from the baseline control conditions of five studies employing common measures of body composition (air displacement plethysmography) and RMR (indirect calorimetry). Daily EI (weighed-dietary records) and EE (FLEX heart rate) were measured over 6–7 days, and AEE was calculated as total daily EE minus RMR.ResultsLinear regression indicated that RMR (β = 0.39; P < 0.001), fat mass (β = −0.26; P < 0.001) and AEE (β = 0.18; P = 0.002) were independent predictors of mean daily EI, with AEE adding ≈3% of variance to the model after controlling for age, sex and study (F(10, 231) = 18.532, P < 0.001; R2 = 0.445). Path analyses indicated that the effect of FFM on mean daily EI was mediated by RMR (P < 0.05), while direct (β = 0.19; P < 0.001) and indirect (β = 0.20; P = 0.001) associations between AEE and mean daily EI were observed.ConclusionsWhen physical activity was allowed to vary under free-living conditions, AEE was associated with mean daily EI independently of other biological determinants of EI arising from body composition and RMR. These data suggest that EE per se exerts influence over daily food intake, with both metabolic (RMR) and behavioral (AEE) components of total daily EE potentially influencing EI via their contribution to daily energy requirements.

BackgroundWhile recent studies in humans indicate that fat-free mass (FFM) is closely associated with energy intake (EI) when in energy balance, associations between fat mass (FM) and EI are inconsistent.ObjectivesThe present study used a cross-sectional design to examine the indirect and direct effects of FFM, FM and resting metabolic rate (RMR) on EI in individuals at or close to energy balance.MethodsData for 242 individuals (114 males; 128 females; BMI = 25.7 ± 4.9 kg/m2) were collated from the non-intervention baseline conditions of five studies employing common measures of body composition (air-displacement plethysmography), RMR (indirect calorimetry) and psychometric measures of eating behaviours (Dutch Eating Behaviour Questionnaire). Daily EI (weighed dietary records) and energy expenditure (flex heart rate) were measured for 6–7 days. Sub-analyses were conducted in 71 individuals who had additional measures of body composition (dual-energy X-ray absorptiometry) and fasting glucose, insulin and leptin.ResultsAfter adjusting for age, sex and study, linear regression and mediation analyses indicated that the effect of FFM on EI was mediated by RMR (P < 0.05). FM also independently predicted EI, with path analysis indicating a positive indirect association (mediated by RMR; P < 0.05), and a stronger direct negative association (P < 0.05). Leptin, insulin and insulin resistance failed to predict EI, but cognitive restraint was a determinant of EI and partially mediated the association between FM and EI (P < 0.05).ConclusionsWhile the association between FFM and EI was mediated by RMR, FM influenced EI via two separate and opposing pathways; an indirect ‘excitatory’ effect (again, mediated by RMR), and a stronger direct ‘inhibitory’ effect. Psychological factors such as cognitive restraint remain robust predictors of EI when considered alongside physiological determinants of EI, and indeed, have the potential to play a mediating role in the overall expression of EI.

Errors inherent in self-reported measures of energy intake (EI) are substantial and well documented, but correlates of misreporting remain unclear. Therefore, potential predictors of misreporting were examined. In Study One, fifty-nine individuals (BMI = 26·1 (sd 3·8) kg/m2, age = 42·7 (sd 13·6) years, females = 29) completed a 14-d stay in a residential feeding behaviour suite where eating behaviour was continuously monitored. In Study Two, 182 individuals (BMI = 25·7 (sd 3·9) kg/m2, age = 42·4 (sd 12·2) years, females = 96) completed two consecutive days in a residential feeding suite and five consecutive days at home. Misreporting was directly quantified by comparing covertly measured laboratory weighed intakes (LWI) with self-reported EI (weighed dietary record (WDR), 24-h recall, 7-d diet history, FFQ). Personal (age, sex and %body fat) and psychological traits (personality, social desirability, body image, intelligence quotient and eating behaviour) were used as predictors of misreporting. In Study One, those with lower psychoticism (P = 0·009), openness to experience (P = 0·006) and higher agreeableness (P = 0·038) reduced EI on days participants knew EI was being measured to a greater extent than on covert days. Isolated associations existed between personality traits (psychoticism and openness to experience), eating behaviour (emotional eating) and differences between the LWI and self-reported EI, but these were inconsistent between dietary assessment techniques and typically became non-significant after accounting for multiplicity of comparisons. In Study Two, sex was associated with differences between LWI and the WDR (P = 0·009), 24-h recall (P = 0·002) and diet history (P = 0·050) in the laboratory, but not home environment. Personal and psychological correlates of misreporting identified displayed no clear pattern across studies or dietary assessment techniques and had little utility in predicting misreporting.

The effects of incremental exercise on appetite, energy intake (EI), expenditure (EE) and balance (EB) in lean men and women were examined. Six men (age 29·7 (sd 5·9) years, weight 75·2 (sd 15·3) kg, height 1·75 (sd 0·11) m) and six women (age 24·7 (sd 5·9) years, weight 66·7 (sd 9·10) kg, height 1·70 (sd 0·09) m) were each studied three times during a 16 d protocol, corresponding to no additional exercise (Nex), moderate-intensity exercise (Mex; 1·5–2·0 MJ/d) and high-intensity exercise (Hex; 3·0–4·0 MJ/d) regimens. Subjects were fed to EB during days 1–2, and during days 3–16 they fed ad libitum from a medium-fat diet of constant composition. Daily EE, assessed using the doubly labelled water method, was 9·2, 11·6 and 13·7 MJ/d (P < 0·001; sed 0·45) for the women and 12·2, 14·0 and 16·7 MJ/d (P = 0·007; sed 1·11) for the men on the Nex, Mex and Hex treatments, respectively. EI was 8·3, 8·6 and 9·9 MJ/d (P = 0·118; sed 0·72) for the women and 10·6, 11·6 and 12·0 MJ/d (P = 0·031; sed 0·47) for the men, respectively. On average, subjects compensated for about 30 % of the exercise-induced energy deficit. However, the degree of compensation varied considerably among individuals. The present study captured the initial compensation in EI for exercise-induced energy deficits. Total compensation would take a matter of weeks.