Capturing children food exposure using wearable cameras and deep learning

Children’s dietary habits are influenced by complex factors within their home, school and neighborhood environments. Identifying such influencers and assessing their effects is traditionally based on self- reported data which can be prone to recall bias. We developed a culturally acceptable machine-learning-based data-collection system to objectively capture school-children’s exposure to food (including food items, food advertisements, and food outlets) in two urban Arab centers: Greater Beirut, in Lebanon, and Greater Tunis, in Tunisia. Our machine-learning-based system consists of 1) a wearable camera that captures continuous footage of children’s environment during a typical school day, 2) a machine learning model that automatically identifies images related to food from the collected data and discards any other footage, 3) a second machine learning model that classifies food-related images into images that contain actual food items, images that contain food advertisements, and images that contain food outlets, and 4) a third machine learning model that classifies images that contain food items into two classes, corresponding to whether the food items are being consumed by the child wearing the camera or whether they are consumed by others. This manuscript reports on a user-centered design study to assess the acceptability of using wearable cameras to capture food exposure among school children in Greater Beirut and Greater Tunis. We then describe how we trained our first machine learning model to detect food exposure images using data collected from the Web and utilizing the latest trends in deep learning for computer vision. Next, we describe how we trained our other machine learning models to classify food-related images into their respective categories using a combination of public data and data acquired via crowdsourcing. Finally, we describe how the different components of our system were packed together and deployed in a real-world case study and we report on its performance

Influence of socio-economic and lifestyle factors on overweight and nutrition-related diseases among Tunisian migrants versus non-migrant Tunisians and French

<p>Abstract</p> <p>Background</p> <p>Migrant studies in France revealed that Mediterranean migrant men have lower mortality and morbidity than local-born populations for non-communicable diseases (NCDs). We studied overweight and NCDs among Tunisian migrants compared to the population of the host country and to the population of their country of origin. We also studied the potential influence of socio-economic and lifestyle factors on differential health status.</p> <p>Methods</p> <p>A retrospective cohort study was conducted to compare Tunisian migrant men with two non-migrant male groups: local-born French and Tunisians living in Tunisia, using frequency matching. We performed quota sampling (n = 147) based on age and place of residence. We used embedded logistic regression models to test socio-economic and lifestyle factors as potential mediators for the effect of migration on overweight, hypertension and reported morbidity (hypercholesterolemia, type-2 diabetes, cardiovascular diseases (CVD)).</p> <p>Results</p> <p>Migrants were less overweight than French (OR = 0.53 [0.33–0.84]) and had less diabetes and CVD than Tunisians (0.18 [0.06–0.54] and 0.25 [0.07–0.88]). Prevalence of hypertension (grade-1 and -2) and prevalence of hypercholesterolemia were significantly lower among migrants than among French (respectively 0.06 [0.03–0.14]; 0.04 [0.01–0.15]; 0.11 [0.04–0.34]) and Tunisians (respectively OR = 0.07 [0.03–0.18]; OR = 0.06 [0.02–0.20]; OR = 0.23 [0.08–0.63]).</p> <p>The effect of migration on overweight was mediated by alcohol consumption. Healthcare utilisation, smoking and physical activity were mediators for the effect of migration on diabetes. The effect of migration on CVD was mediated by healthcare utilisation and energy intake. No obvious mediating effect was found for hypertension and hypercholesterolemia.</p> <p>Conclusion</p> <p>Our study clearly shows that lifestyle (smoking) and cultural background (alcohol) are involved in the observed protective effect of migration.</p

DeepNOVA : a deep learning NOVA classifier for food images

Assessing the healthiness of food items in images has gained attention in both the computer vision and the nutrition fields. However, such task is generally a difficult one as food images are captured in various settings and thus are usually non-homogeneous. Moreover, assessing how healthy a food item is requires nutritional expertise and knowledge of the constituents of the food item and how it is processed. In this manuscript, we propose an end-to-end deep learning approach that can detect and localize various food items in a given food image using a customized object detection model. Our approach then assesses how healthy each detected food item is by classifying it into one or more of the four NOVA groups (Unprocessed Food, Processed Culinary Ingredients, Processed Food, and Ultra-processed Food). To train our food item detection model, we used two public datasets and a custom one we created ourselves and which contains images of food taken using wearable cameras. To train the NOVA food classifier, we use the custom dataset we created ourselves and that was manually labeled by expert nutritionists. Our food item detection model achieved a mAP of 0.90 and the NOVA food classifier achieved an average F1-score of 0.86 on test data

Nutrition transition among adolescents of a south-Mediterranean country: dietary patterns, association with socio-economic factors, overweight and blood pressure. A cross-sectional study in Tunisia

Background: The increase in the burden of chronic diseases linked to the nutrition transition and associated dietary and lifestyle changes is of growing concern in south and east Mediterranean countries and adolescents are at the forefront of these changes. This study assessed dietary intake and association with socio-economic factors and health outcomes among adolescents in Tunisia. Methods: Cross-sectional survey (year 2005); 1019 subjects 15-19 y. from a clustered random sample. Dietary intake was assessed by a validated semi-quantitative frequency questionnaire (134 items) as was physical activity; the Diet Quality Index International measured diet quality; dietary patterns were derived by multiple correspondence analysis from intakes of 43 food groups. Body Mass Index (BMI) >= 85(th) and 95(th) percentile defined overweight and obesity. Waist Circumference (WC) assessed abdominal fat. High blood pressure was systolic (SBP) or diastolic blood pressure (DBP) >= 90(th) of the international reference for 15-17 y., and SBP/DBP >= 120/80 mm Hg for 18-19 y. Results: Energy intake levels were quite high, especially for females. The macro-nutrient structure was close to recommendations but only 38% had a satisfactory diet quality. A main traditional to modern dietary gradient, linked to urbanisation and increased economic level, featured an increasing consumption of white bread, dairy products, sugars, added fats and fruits and decreasing consumption of oils, grains, legumes and vegetables; regarding nutrients this modern diet score featured a decreasing relationship with total fat and an increase of calcium intake, but with an increase of energy, sugars and saturated fat, while vitamin C, potassium and fibre decreased. Adjusted for age, energy and physical activity, this modern pattern was associated with increased overweight in males (2(nd) vs. 1(st) tertile: Prevalence Odds-Ratio (POR) = 4.0[1.7-9.3], 3(rd) vs. 1st: POR = 3.3[1.3-8.7]) and a higher WC. Adjusting also for BMI and WC, among females, it was associated with decreased prevalence of high blood pressure (2(nd) vs. 1(st) tertile: POR = 0.5[0.3-0.8], 3(rd) vs. 1(st) tertile: POR = 0.4[0.2-0.8]). Conclusion: The dietary intake contrasts among Tunisian adolescents, linked to socio-economic differentials are characteristic of a nutrition transition situation. The observed gradient of modernisation of dietary intake features associations with several nutrients involving a higher risk of chronic diseases but might have not only negative characteristics regarding health outcomes

Importance and use of reliable food composition data generation by nutrition/dietetic professionals towards solving Africa’s nutrition problem : constraints and the role of FAO/INFOODS/AFROFOODS and other stakeholders in future initiatives

Despite the rich biodiversity of the African continent and the tremendous progress so far made in food production, Africa is still struggling with the problems of food insecurity, hunger and malnutrition. To combat these problems, the production and consumption of nutritious and safe foods need to be promoted. This cannot be achieved without reliable data on the quantity and quality of nutrients and other components provided through these foods. Food composition data (FCD) are compiled as food composition tables (FCT) or food composition databases (FCDB). These are subsequently used for a variety of purposes, ranging from clinical practice, research, public health/education, food industry to planning and policy, as well as nutrition monitoring and surveillance. To perform these functions effectively, the importance of reliable FCT/ FCDB cannot be overemphasised. Poor quality FCT/FCDB have serious consequences on the health of the population, and provide skew evidence towards developing nutrition and healthrelated policies. The present paper reviews different methods to generate FCT/FCDB, their importance and use in assisting nutrition/dietetic professionals in solving Africa’s nutrition problems; current status of FCT/FCDB generation, compilation and dissemination in Africa, constraint to their use by professionals and the role of FAO/INFOODS/AFROFOODS and other stakeholders towards improvement and future initiatives. The information provided will create awareness on the need for up-to-date and high-quality FCT/FCDB and facilitate the identification of data gaps and prioritisation of future efforts in FCD generation, compilation and dissemination in Africa and subsequent strategies for the alleviation of the food and nutrition problems in Africa.https://www.cambridge.org/core/journals/proceedings-of-the-nutrition-societyam2020Animal and Wildlife Science