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    Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID?19 pandemic
    (BioMed Central, 2023) Qasrawi, Radwan; Hoteit, Maha; Tayyem, Reema; Bookari, Khlood; Al Sabbah, Haleama; Kamel, Iman; Dashti, Somaia; Allehdan, Sabika; Bawadi, Hiba; Waly, Mostafa; Ibrahim, Mohammed O.; The Regional Corona Cooking Survey Group; Polo, Stephanny Vicuna; Al?Halawa, Diala Abu
    Background A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.
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    Perspectives and practices of dietitians with regards to social/mass media use during the transitions from face-to-face to telenutrition in the time of COVID-19: A cross-sectional survey in 10 Arab countries
    (Frontiers, 2023) Bookari, Khlood; Arrish, Jamila M.; Alkhalaf, Majid H.; Alharbi, Mudi; Zaher, Sara M.; Alotaibi, Hawazin; Tayyem, Reema; Al-Awwad, Narmeen; Qasrawi, Radwan; Allehdan, Sabika; Al Sabbah, Haleama; AlMajed, Sana; Al Hinai, Eiman; Kamel, Iman; El Ati, Jalila; Harb, Ziad; Hoteit, Maha
    During the COVID-19 pandemic, most healthcare professionals switched from face-to-face clinical encounters to telehealth. This study sought to investigate the dietitians’ perceptions and practices toward the use of social/mass media platforms amid the transition from face-to-face to telenutrition in the time of COVID-19. This cross-sectional study involving a convenient sample of 2,542 dietitians (mean age?=?31.7?±?9.5; females: 88.2%) was launched in 10 Arab countries between November 2020 and January 2021. Data were collected using an online self-administrated questionnaire. Study findings showed that dietitians’ reliance on telenutrition increased by 11% during the pandemic, p?=?0.001. Furthermore, 63.0% of them reported adopting telenutrition to cover consultation activities. Instagram was the platform that was most frequently used by 51.7% of dietitians. Dietitians shouldered new difficulties in dispelling nutrition myths during the pandemic (58.2% reported doing so vs. 51.4% pre-pandemic, p?
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    Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries
    (Frontiers Media, 2023) Hoteit, Maha; Mansour, Rania; Mohsen, Hala; Bookari, Khlood; Hammouh, Fadwa; Allehdan, Sabika; AlKazemi, Dalal; Al Sabbah, Haleama; Benkirane, Hasnae; Kamel, Iman; Qasrawi, Radwan; Tayyem, Reema
    Background: Food literacy is capturing the attention worldwide and gaining traction in the Arab countries. Strengthening food and nutrition literacy among Arab teenagers are important promising empowering tools which can protect them from malnutrition. This study aims to assess the nutrition literacy status of adolescents with the food literacy of their parents in 10 Arab countries. Methods: This cross-sectional study involving a convenient sample of 5,401 adolescent-parent dyads (adolescents: mean age?±?SD: 15.9?±?3.0, females: 46.8%; parents: mean age?±?SD: 45.0?±?9.1, mothers: 67.8%) was launched between 29 April and 6 June 2022 in 10 Arab nations. The Adolescent Nutrition Literacy Scale (ANLS) and the Short Food Literacy Questionnaire (SFLQ) were used to meet the study aims. Results: More than one-quarter (28%) of adolescents had poor nutrition literacy, with 60% of their parents being food illiterate. The top three countries with nutritionally” less literate” adolescents were Qatar (44%), Lebanon (37.4%), and Saudi Arabia (34.9%). Adolescents’ age, gender, education level, primary caregivers, employment status, and the inclusion of nutrition education in the schools’ curriculum predicted the nutrition literacy levels of Arab adolescents. Besides, parental weight status, health status, parent’s food literacy level, and the number of children per household were significant determinants too. Adolescents studying at a university and having parents with adequate food literacy had the highest odds of being nutritionally literate (OR?=?4.5, CI?=?1.8–11.5, p?=?0.001, OR?=?1.8, CI?= 1.6–2.1, p?