Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction.

Nudging customers towards healthier food and beverage purchases in a real-life online supermarket: a multi-arm randomized controlled trial

Background Nudging is increasingly used to promote healthy food choices in supermarkets. Ordering groceries online is gaining in popularity and nudging seems efficacious there as well, but is never comprehensively tested in real-life. We evaluated the real-life effectiveness of nudging in an online supermarket on healthy food purchases. Methods We conducted a multi-arm, parallel-group, individually randomized controlled trial in an online supermarket. During 1 month, all customers were randomized to (1) control condition, (2) information nudges, (3) position nudges, and (4) information and position nudges combined. Allocation was concealed and customers were not blinded, but unaware of the intervention. Mean differences between the control condition and the intervention arms in the total percentage of healthy purchases were assessed with a linear mixed model. We tested for effect modification by area-level deprivation. Results Based on sales data from 11,775 shoppers, no overall significant effects were detected. Yet, effects were modified by area-level deprivation (pArm 2 < 0.001). Among shoppers from deprived areas, those allocated to information nudges purchased a 2.4% (95%CI 0.8, 4.0) higher percentage of healthy products compared to controls. No significant differences were observed for position (− 1.3%; 95%CI − 2.8, 0.3) and combined nudges (− 0.1%; 95%CI − 1.7, 1.5). Shoppers from non-deprived areas exposed to information nudges (− 1.6%; 95%CI − 3.2, − 0.1) and the combined nudges (− 2.1%; 95%CI − 3.6, − 0.6), but not position nudges (− 0.9%; 95%CI − 2.4, 0.7), purchased a lower percentage of healthy products. Conclusion Information nudges in an online supermarket can increase healthy product purchases, but only for those living in deprived areas. The adverse effects found on purchasing behaviors for those from non-deprived areas call for further research. Further research should also focus on real-life effects of online healthy food nudging as part of a broader nutrition intervention strategy, and on the equitability of the online nudging intervention within populations. Trial registration Retrospectively registered in the ISRCTN registry at May 21, 2021 (ISRCTN10491616).

Implementing healthy food environment policies in New Zealand: nine years of inaction

Background The INFORMAS [International Network for Food and Obesity/Non-communicable Diseases (NCDs) Research, Monitoring and Action Support] Healthy Food Environment Policy Index (Food-EPI) was developed to evaluate the degree of implementation of widely recommended food environment policies by national governments against international best practice, and has been applied in New Zealand in 2014, 2017 and 2020. This paper outlines the 2020 Food-EPI process and compares policy implementation and recommendations with the 2014 and 2017 Food-EPI. Methods In March–April 2020, a national panel of over 50 public health experts participated in Food-EPI. Experts rated the extent of implementation of 47 “good practice” policy and infrastructure support indicators compared to international best practice, using an extensive evidence document verified by government officials. Experts then proposed and prioritized concrete actions needed to address the critical implementation gaps identified. Progress on policy implementation and recommendations made over the three Food-EPIs was compared. Results In 2020, 60% of the indicators were rated as having “low” or “very little, if any” implementation compared to international benchmarks: less progress than 2017 (47%) and similar to 2014 (61%). Of the nine priority actions proposed in 2014, there was only noticeable action on one (Health Star Ratings). The majority of actions were therefore proposed again in 2017 and 2020. In 2020 the proposed actions were broader, reflecting the need for multisectoral action to improve the food environment, and the need for a mandatory approach in all policy areas. Conclusions There has been little to no progress in the past three terms of government (9 years) on the implementation of policies and infrastructure support for healthy food environments, with implementation overall regressing between 2017 and 2020. The proposed actions in 2020 have reflected a growing movement to locate nutrition within the wider context of planetary health and with recognition of the social determinants of health and nutrition, resulting in recommendations that will require the involvement of many government entities to overcome the existing policy inertia. The increase in food insecurity due to COVID-19 lockdowns may provide the impetus to stimulate action on food polices.

Nurses’ eating habits in Lebanon during the economic and health crises: a cross-sectional study

Background Nurses face multiple stressors that can influence their lifestyle, thus affecting their health status. Scarce are the scientific data on the nutritional status of nurses, especially during health crises. The aim of this study was to assess the impact of the COVID-19 pandemic on the eating habits of hospital nurses in the context of an exceptional economic situation in Lebanon. Methods A cross-sectional study was conducted using a web-based questionnaire, targeting a non-random sampling of frontline nurses using the snowball technique. Descriptive and bivariate analyses were carried out. The population of the study included all registered nurses working in the Lebanese hospitals. A total of 533 nurses completed the questionnaire; 500 surveys were selected after excluding the ones presenting conditions that may affect their eating behavior. Results The majority of the respondents were women (78.6%) with a mean age of 33 years [18-60] [SD,7.44 years]. Most of them (57.6%) had a crowding index ≥1. The consumption of different food groups decreased during these crises. There was a significant correlation between stress and deterioration of healthy food consumption, which provides beneficial nutrients and minimizes potentially harmful elements, especially for meat (OR 2.388, CI 1.463 to 3.898, P < 0.001). The decrease in monthly income showed a real impact on the consumption of healthy food such as meat (OR 2.181, CI 1.504 to 3.161, P < 001), fruits (OR 1.930, CI 1.289 to 2.888, P = 0.001), and milk and dairy products (OR 1.544, CI 1.039 to 2.295, P = 0.031). Conclusions The pandemic and in particular the economic crisis has changed the consumption of healthy food among hospital nurses in Lebanon. Similar research and support may be extended to include other frontline health care workers.

Evaluation of the Healthy Eating Food Index (HEFI)-2019 measuring adherence to Canada's Food Guide 2019 recommendations on healthy food choices

The objective of this study was to evaluate the construct validity and reliability of the Healthy Eating Food Index-2019 (HEFI-2019), which was developed to measure adherence to Canada’s Food Guide 2019 (CFG) recommendations on healthy food choices. Dietary intake data from 24-hour dietary recalls in the 2015 Canadian Community Health Survey - Nutrition were used for that purpose. Multidimensionality was examined using principal component analysis. Mean scores were compared among subgroups of the population. The association between scores and energy intake was assessed using Pearson correlations. Cronbach’s alpha was calculated to assess reliability. The estimated mean HEFI-2019 score (/80) was 43.1 (95%CI, 42.7 to 43.6) among Canadians 2 years and older. The first and 99th percentiles were 22.1 and 62.9 points. The mean HEFI-2019 score for smokers was 7.1 points lower than for non-smokers (95%CI, -8.4 to -5.8). The HEFI-2019 was weakly correlated with energy intake (r=-0.13; 95%CI, -0.20 to -0.06). The principal components analysis revealed at least 4 dimensions. Cronbach’s alpha was 0.66 (95%CI, 0.63 to 0.69). Evidence of construct validity and internal consistency support the use of the HEFI-2019 to assess adherence to CFG-2019’s recommendations on healthy food choices. Novelty: • Examination of the Health Eating Food Index (HEFI)-2019’s psychometric properties is needed prior to implementation • Analyses support the construct validity and internal consistency of the HEFI-2019 • Interpretation of the total HEFI-2019 score must be accompanied by its components’ scores, considering it assesses multiple dimensions of food choices

Development of the Healthy Eating Food Index (HEFI)-2019 measuring adherence to Canada's Food Guide 2019 recommendations on healthy food choices

The release of Canada’s Food Guide (CFG) in 2019 by Health Canada prompted the development of indices to measure adherence to these updated dietary recommendations for Canadians. This study describes the development and scoring standards of the Healthy Eating Food Index (HEFI)-2019, which is intended to measure alignment of eating patterns with CFG-2019 recommendations on food choices among Canadians aged 2 years and older. Alignment with the intent of each key recommendation in the CFG-2019 was the primary principle guiding the development of the HEFI-2019. Additional considerations included previously published indices, data on Canadians’ dietary intakes from the 2015 Canadian Community Health Survey (CCHS) – Nutrition, and expert judgement. The HEFI-2019 includes 10 components: Vegetables and fruits (20 points), Whole-grain foods (5 points), Grain foods ratio (5 points), Protein foods (5 points), Plant-based protein foods (5 points), Beverages (10 points), Fatty acids ratio (5 points), Saturated fats (5 points), Free sugars (10 points), and Sodium (10 points). All components are expressed as ratios (e.g., proportions of total foods, total beverages, or total energy). The HEFI-2019 score has a maximum of 80 points. Potential uses of the HEFI-2019 include research as well as monitoring and surveillance of food choices in population-based surveys. Novelty: ● The Healthy Eating Food Index-2019 was developed to measure adherence to the 2019 Canada's Food Guide recommendations on healthy food choices. ● The HEFI-2019 includes 10 components, of which 5 are based on foods, 1 on beverages and 4 on nutrients, for a total of 80 points.

Beyond food swamps and food deserts: Exploring urban Australian food retail environment typologies

Objective: ‘Food deserts’ and ‘food swamps’ are food retail environment typologies associated with unhealthy diet and obesity. This study aimed to identify more complex food retail environment typologies and examine temporal trends. Design: Measures of food retail environment accessibility and relative healthy food availability were defined for small areas (SA2s) of Melbourne, Australia from a census of food outlets operating in 2008, 2012, 2014 and 2016. SA2s were classified into typologies using a two-stage approach: 1) SA2s were sorted into 20 clusters according to accessibility and availability; 2) clusters were grouped using evidence-based thresholds. Setting: This study was set in Melbourne, the capital city of the state of Victoria, Australia. Subjects: Food retail environments in 301 small areas (Statistical Area 2) located in Melbourne in 2008, 2012, 2014 and 2016. Results: Six typologies were identified based on access (low, moderate and high) and healthy food availability including one where zero food outlets were present. Over the study period SA2s experienced an overall increase in accessibility and healthiness. Distribution of typologies varied by geographic location and area-level socioeconomic position. Conclusion: Multiple typologies with contrasting access and healthiness measures exist within Melbourne and these continue to change over time, the majority of SA2s were dominated by the presence of unhealthy relative to healthy outlets; with SA2s experiencing growth and disadvantage having the lowest access and to a greater proportion of unhealthy outlets.

Determinants of Preference and Consumption of Healthy Food in Children

According to recent findings, action is urgently needed to promote healthy eating habits among children, especially to increase daily consumption of fresh fruits and vegetables [...]

Factors Influencing Implementation, Sustainability and Scalability of Healthy Food Retail Interventions: A Systematic Review of Reviews

The aim of this systematic review of reviews was to synthesise the evidence on factors influencing the implementation, sustainability and scalability of food retail interventions to improve the healthiness of food purchased by consumers. A search strategy to identify reviews published up until June 2020 was applied to four databases. The Risk of Bias in Systematic Review tool was used. Review findings were synthesised narratively using the socio-ecological model. A total of 25 reviews met the inclusion criteria. A number of factors influenced implementation; these included retailers’ and consumers’ knowledge and preferences regarding healthy food; establishing trust and relationships; perceived consumer demand for healthy food; profitability; store infrastructure; organizational support, including resources; and enabling policies that promote health. Few reviews reported on factors influencing sustainability or scalability of the interventions. While there is a large and rapidly growing body of evidence on factors influencing implementation of interventions, more work is needed to identify factors associated with their sustainability and scalability. These findings can be used to develop implementation strategies that consider the multiple levels of influence (individual, intrapersonal and environmental) to better support implementation of healthy food retail interventions.