Research Results (FEH) / Résultats de recherche (AES)

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    Systems approach to improving children's diets : learning from lived experience
    (2022-07) Hawkes, Corinna; Battersby, Jane; Hunter-Adams, Jo; Ghattas, Hala; Ak, Christelle; D’Aloisio, Julia; Williams, D’Arcy; Jewell, Jo; Joubert, Leonie; Ahmed, Madiha; Hallen, Greg
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    Studying children food exposure and food consumption using deep learning
    (2021-12) Shmayssani, Zoulfikar
    Children's eating behavior is one of the main pillars of a healthy life. Recent studies show that eating unhealthy food is highly associated with many chronic diseases including diabetes, obesity, and cancer. Such dietary habits are often shaped by complex factors influenced by the children's home, school, and neighborhood environments. However, studying the eating behaviors of children and analyzing the factors affecting them is currently done using traditional questionnaire-based methods, which often suffer from recall and bias issues. In this thesis, we developed a comprehensive approach to study children's food exposure and food consumption using deep learning.
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    Understanding the contextual factors that influence children's food choices : a qualitative study
    (2022-08) El Helou, Rim
    Childhood obesity is a serious public health concern. Interventions to address this problem should not focus on biological and individual factors only, but they should target the factors in the child’s environment that affect his eating choices. Recent studies have shown that choice experiments are an important tool to assess children’s choices. The aim of this study is to explore why school aged children living in greater Beirut, make certain food choices, in the context of a real food modeling experiment. It also aims to understand to what degree the choices made in this choice experiment are similar to the real food choices they make in their life. Twenty-seven children in grades four, five and six played a game displaying a choice experiment. Then they were interviewed. Factors that were intended to be studies (food price, food placement, food preparation and mother’s/ peer’s influence), have been shown to affect children’s food choices in addition to new factors that emerged (Expected taste, the degree to which the food is considered by the child and food safety). The findings also revealed that this choice experiment reflects children’s real food choices. These findings can be used to inform policies aiming to address childhood obesity.
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    School and community drivers of child diets in two Arab cities : the SCALE protocol and innovative tools to assess children’s food environments
    (2022-07-20) Ghattas, Hala; Jamaluddine, Zeina; Semaan, Aline; El-Helou, Nehmat; Safadi, Gloria; Elghossain, Tatiana; Saleem, Sheikh Mohd
    In the context of the rapid nutrition transition experienced by middle-income countries of the Arab region, children and adolescent’s food choices and dietary behaviors are early risk factors for the development of non-communicable diseases. Assessment of factors influencing food choices among this age group is challenging and is usually based on self-reported data, which are prone to information and recall bias. As the popularity of technologies and video gaming platforms increases, opportunities arise to use these tools to collect data on variables that affect food choice, dietary intake, and associated outcomes. This protocol paper describes the SCALE study (School and community drivers of child diets in Arab cities; identifying levers for intervention) which aims to explore the environments at the level of households, schools and communities in which children’s food choices are made and consequently identify barriers and enablers to healthy food choices within these environments.
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    DeepNOVA : a deep learning NOVA classifier for food images
    (2022-12-13) Elbassuoni, Shady; Ghattas, Hala; El Ati, Jalila; Shmayssani, Zoulfikar; Katerrji, Sarah
    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.