Obesity is a persistent societal and health problem. Its prevalence has doubled since 1990. The increasing availability, low prices and promotion of unhealthy food has contributed to the current obesity epidemic. There are two structural solutions to address the current unhealthy food environment: self-regulation by the food industry and governmental regulation. In practice, self-regulation has limited effectiveness. The increasing burden of obesity and associated health care costs warrants governmental regulation. However, lobbying from the food industry and the liberal political climate in the Netherlands seem to be hindering the introduction of effective measures. We provide an overview of promising policy measures for a healthy food environment to prevent obesity: financial measures, restricting price promotions and marketing of unhealthy products, banning unhealthy products at checkouts and restricting unhealthy product availability. This requires a reduction of industry influences on policy, which can be achieved by acknowledging and discussing these influences.
Context-specific interventions may contribute to sustained behaviour change and improved health outcomes. We evaluated the real-world effects of supermarket nudging and pricing strategies and mobile physical activity coaching on diet quality, food-purchasing behaviour, walking behaviour, and cardiometabolic risk markers.
Abstract Background Healthy food nudges may be more, or especially, effective among groups experiencing socioeconomic disadvantage. We investigated the modifying role of socioeconomic and demographic characteristics in the effectiveness of nudge interventions targeting healthy foods in real-world grocery store settings on food purchasing patterns. Methods We pooled individual participant data from multiple trials. Eligible trials were identified via a PubMed search and selected based on having a controlled real-world design, testing a nudging intervention promoting healthy purchases, while collecting participants’ sociodemographic and purchasing data. Out of four eligible trials, three had longitudinal measurements, one consisted of a single time point, two were randomised and two were not. Applied nudges consisted of a combination of placement nudges (focussing on availability or positioning) and property nudges (presentation and/or information). Harmonised data included dichotomised socioeconomic and demographic variables and the percentage of purchased fruits and vegetables of total purchases. Multilevel meta-regression based on linear mixed-effects models were used to explore modifying effects using two approaches: longitudinal and cross-sectional analyses. Results The analytical sample in the longitudinal analysis comprised of 638 participants, who were predominantly female (76.3%), had a lower education attainment (67.7%), and a mean age of 46.6 years (SD 13.5). These characteristics were similar in the cross-sectional analysis ( n = 855). Compared to control group participants, there was no main effect of healthy food nudges on the percentage of fruit and vegetable purchases by intervention group participants in the longitudinal analysis (β = 0.00; 95%CI -0.03, 0.09). This main effect was not modified by educational attainment (β group*higher education = -0.06; -0.40, 0.02), sex (β group*females = 0.13; -0.00, 0.61) nor age (β group*older adults = -0.05; -0.39, 0.02). Results from the cross-sectional analysis were comparable. Conclusions This pooled analyses of four controlled trials did not find evidence supporting the hypothesis that grocery store nudge interventions of healthy foods work more effectively among groups experiencing socioeconomic disadvantage. Future studies are needed to address the identified limitations through rigorous trial design using comprehensive interventional strategies, standardised outcome measures, while also evaluating context-specific approaches. Such insights will help to better understand the equity of nudging interventions in grocery store settings and the potential for reducing diet-related health disparities. Trial registrations The trial of Ayala et al. (2022) was retrospectively registered at ClinicalTrials.gov (NCT01475526; at 14 November 2011, https://clinicaltrials.gov/study/NCT01475526 ), the of Huitink et al. (2020) was retrospectively registered in the ISRCTN registry (ISRCTN39440735; at 5 September 2018, https://doi.org/10.1186/ISRCTN39440735 ), the of Vogel et al. (2024) was retrospectively registered at ClinicalTrials.gov (NCT03518151; at 24 April 2018, https://clinicaltrials.gov/study/NCT03518151 ), and finally of Stuber et al. (2024) was registered in the Dutch Trial Register (ID NL7064, at 30 May 2018, https://www.onderzoekmetmensen.nl/en/trial/20990 ).
Nudging and salient pricing are promising strategies to promote healthy food purchases, but it is possible their effects differ across food groups.To investigate in which food groups nudging and/or pricing strategies most effectively changed product purchases and resulted in within-food groups substitutions or spillover effects.In total, 318 participants successfully completed a web-based virtual supermarket experiment in the Netherlands. We conducted a secondary analysis of a mixed randomized experiment consisting of 5 conditions (within subject) and 3 arms (between subject) to investigate the single and combined effects of nudging (e.g., making healthy products salient), taxes (25% price increase), and/or subsidies (25% price decrease) across food groups (fruit and vegetables, grains, dairy, protein products, fats, beverages, snacks, and other foods). Generalized linear mixed models were used to estimate the incidence rate ratios and 95% CIs for changes in the number of products purchased.Compared with the control condition, the combination of subsidies on healthy products and taxes on unhealthy products in the nudging and price salience condition was overall the most effective, as the number of healthy purchases from fruit and vegetables increased by 9% [incidence rate ratio (IRR) = 1.09; 95% CI: 1.02, 1.18], grains by 16% (IRR = 1.16; 95% CI: 1.05, 1.28), and dairy by 58% (IRR = 1.58; 95% CI: 1.31, 1.89), whereas the protein and beverage purchases did not significantly change. Regarding unhealthy purchases, grains decreased by 39% (IRR = 0.72; 95% CI: 0.63, 0.82) and dairy by 30% (IRR = 0.77; 95% CI: 0.68, 0.87), whereas beverage and snack purchases did not significantly change. The groups of grains and dairy showed within-food group substitution patterns toward healthier products. Beneficial spillover effects to minimally targeted food groups were seen for unhealthy proteins (IRR = 0.81; 95% CI: 0.73, 0.91).Nudging and salient pricing strategies have a differential effect on purchases of a variety of food groups. The largest effects were found for dairy and grains, which may therefore be the most promising food groups to target in order to achieve healthier purchases. The randomized trial on which the current secondary analyses were based is registered in the Dutch trial registry (NTR7293; www.trialregister.nl).
Abstract Objective: Low dietary guideline adherence is persistent, but there is limited understanding of how individuals with varying socio-economic backgrounds reach a certain dietary intake. We investigated how quantitative and qualitative data on dietary guidelines adherence correspond and complement each other, to what extent determinants of guideline adherence in quantitative data reflect findings on determinants derived from qualitative data and which of these determinants emerged as interdependent in the qualitative data. Design: This mixed-methods study used quantitative questionnaire data ( n 1492) and qualitative data collected via semi-structured telephone interviews ( n 24). Quantitative data on determinants and their association with total guideline adherence (scored 0–150) were assessed through linear regression. Directed content analysis was used for qualitative data. Setting: Dutch urban areas. Participants: Adults aged 18–65 years. Results: A range of determinants emerged from both data sources, for example higher levels of cognitive restraint ( β 5·6, 95 % CI 4·2, 7·1), habit strength of vegetables ( β 4·0, 95 % CI 3·3, 4·7) and cooking skills ( β 4·7, 95 % CI 3·5, 5·9), were associated with higher adherence. Qualitative data additionally suggested the influence of food prices, strong dietary habits and the social aspect of eating, and for the determinants cognitive restraint, habit strength related to vegetables, food prices and home cooking, some variation between interviewees with varying socio-economic backgrounds emerged in how these determinants affected guideline adherence. Conclusions: This mixed-methods exploration provides a richer understanding of why adults with varying socio-economic backgrounds do or do not adhere to dietary guidelines. Results can guide future interventions promoting healthy diets across populations.
Regular consumption of ultra-processed foods (UPF) is a risk factor for morbidity and mortality. UPF are widely available in supermarkets. Nudging and pricing strategies are promising strategies to promote healthier supermarket purchases and may reduce UPF purchases. We investigated whether supermarket nudging and pricing strategies targeting healthy foods, but not specifically discouraging UPF, would change UPF availability, price, promotion and placement (UPF-APPP) in supermarkets and customer UPF purchases. We used data from the Supreme Nudge parallel cluster-randomized controlled trial, testing the effect of a combined nudging and pricing intervention promoting healthy products. The Dutch Consumer Food Environment Score (D-CFES) was used to audit 12 participating supermarkets in terms of UPF-APPP. We used customer loyalty card data of the first twelve intervention weeks from 321 participants to calculate the proportion of UPF purchases. Descriptive statistics were used to assess differences in D-CFES between supermarkets. Mixed model analyses were used to assess the association between the D-CFES and UPF purchases and the effect of the intervention on UPF purchases. No difference in the D-CFES between intervention and control supermarkets were found. No statistically significant association between the D-CFES and UPF purchases (β = -0.00, 95%CI: -0.02, 0.01) and no significant effect of the intervention on UPF purchases (β = 0.02, 95%CI: -0.07, 0.12) was observed. Given the significant proportion of unhealthy and UPF products in Dutch supermarkets, nudging and pricing strategies aimed at promoting healthy food purchases are not sufficient for reducing UPF-APPP nor purchases, and nationwide regulation may be needed.Trial registration number: Dutch Trial Register ID NL7064, May 30, 2018, https://trialsearch.who.int/Trial2.aspx?TrialID=NTR7302.
Our aim was to describe the prevalence of disease-related undernutrition (DRU) on admission to a department of surgery in Suriname and to explore its association with ethnicity and adverse outcomes. All patients 18 years or older who were not pregnant were invited to participate. Data were collected on weight (history), length, fat-free mass index (FFMI) using bioimpedance analysis, and ethnicity. Age, sex, and diagnosis data were extracted from the medical files. Associations between DRU and ethnicity, functionality, and length of hospital stay were assessed using logistic and Cox regression analyses adjusting for age, sex, diagnosis, and disease severity. The study population of 351 participants revealed 46% were undernourished, 31% had unintended weight loss (UWL), and 27% had a low FFMI. DRU and low FFMI were associated with low handgrip strength, but UWL was not. DRU, UWL, and low FFMI were associated with length of stay. Determinants of DRU seemed to vary between ethnic groups. The prevalence of DRU was high, and nutrition protocols should be implemented to increase awareness and limit adverse outcomes. Further research is needed to reveal whether ethnicity should be part of the DRU risk assessment.
Abstract There is increasing evidence for the effectiveness of population-based policies to reduce the burden of type 2 diabetes. Yet, there are concerns about the equity effects of some policies, whereby socioeconomically disadvantaged populations are not reached or are adversely affected. There is a lack of knowledge on the effectiveness and equity of policies that are both population based (i.e. targeting both at-risk and low-risk populations) and low agency (i.e. not requiring personal resources to benefit from the policy). In this narrative review, we selected 16 policies that were both population based and low agency and reviewed the evidence on their effectiveness and equity. Substantial evidence suggests that fruit and vegetable subsidies, unhealthy food taxes, mass media campaigns, and school nutrition and physical activity education are effective in promoting healthier lifestyle behaviours. Less evidence was available for mandatory food reformulation, reduced portion sizes, marketing restrictions and restriction of availability and promotion of unhealthy products, although the available evidence suggested that these policies were effective in reducing unhealthy food choices. Effects could rarely be quantified across different studies due to substantial heterogeneity. There is an overall lack of evidence on equity effects of population-based policies, although available studies mostly concluded that the policies had favourable equity effects, with the exception of food-labelling policies. Each of the policies is likely to have a relatively modest effect on population-level diabetes risks, which emphasises the importance of combining different policy measures. Future research should consider the type of evidence needed to demonstrate the real-world effectiveness and equity of population-based diabetes prevention policies. Graphical Abstract
Abstract Background Nutrition labels show potential in increasing healthy food and beverage purchases, but their effectiveness seems to depend on the type of label, the targeted food category and the setting, and evidence on their impact in real-world settings is limited. The aim of this study was to evaluate the effectiveness of an industry-designed on-shelf sugar label on the sales of beverages with no, low, medium and high sugar content implemented within a real-world supermarket. Methods In week 17 of 2019, on-shelf sugar labels were implemented by a Dutch supermarket chain. Non-alcoholic beverages were classified using a traffic-light labeling system and included the beverage categories “green” for sugar free (< 1.25 g/250 ml), “blue” for low sugar (1.25–6.24 g/250 ml), “yellow” for medium sugar (6.25–13.5 g/250 ml) and “amber” for high sugar (> 13.5 g/250 ml). Store-level data on beverage sales and revenue from 41 randomly selected supermarkets for 13 weeks pre-implementation and 21 weeks post-implementation were used for analysis. In total, 30 stores implemented the on-shelf sugar labels by week 17, and the 11 stores that had not were used as comparisons. Outcome measures were differences in the number of beverages sold in the four label categories and the total revenue from beverage sales in implementation stores relative to comparison stores. Analyses were conducted using a multiple-group Interrupted Time Series Approach. Results of individual store data were combined using random effect meta-analyses. Results At the end of the intervention period, the changes in sales of beverages with green (B 3.4, 95%CI -0.3; 7.0), blue (B 0.0, 95%CI -0.6; 0.7), yellow (B 1.3, 95%CI -0.9; 3.5), and amber (B 0.9, 95%CI -5.5; 7.3) labels were not significantly different between intervention and comparison stores. The changes in total revenues for beverages at the end of the intervention period were also not significantly different between intervention and comparison stores. Conclusion The implementation of an on-shelf sugar labeling system did not significantly decrease unhealthy beverage sales or significantly increase healthier beverage sales. Nutrition labeling initiatives combined with complementary strategies, such as pricing strategies or other healthy food nudging approaches, should be considered to promote healthier beverage purchases.
