Yazar "Bookari, Khlood" seçeneğine göre listele
Listeleniyor 1 - 6 / 6
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe Adherence to the United States department of agriculture dietary recommendations pre- and during the coronavirus disease-19 pandemic among pregnant women in Arab countries(2022) Hoteit, Maha; Hoteit, Reem; Al-Jawaldeh, Ayoub; Nasr, Mariane Abou; Obeid, Sara; Fakih, Chadi; El Hajj, Mohamad; Qasrawi, Radwan; Seir, Rania Abu; Allehdan, Sabika; Ismail, Mahmoud Samy; Bookari, Khlood; Arrish, Jamila; Al-Bayyari, Nahla; Tayyem, ReemaDuring pregnancy, woman's diet is one of the most preeminent factors affecting mother and child's health. Prior to the coronavirus disease-19 (COVID-19) pandemic, inadequate maternal diet and low adherence to dietary guidelines was reported among pregnant women in the Arab countries. Nowadays, COVID-19 infection during pregnancy is widely discussed among literature. However, there is limited data on the health impacts of the COVID-19 pandemic on non-infected pregnant women. This substantially larger group also suffered significant lifestyle changes during the lockdown period. The aim of the study is to characterize dietary patterns, intake and adherence to the United States Department of Agriculture (USDA) pregnancy guidelines before and during the COVID-19 pandemic in Arab pregnant women. Using a specially designed questionnaire and using the snowball sampling method, the survey was carried out among a convenient sample of 1,939 pregnant women from five Arab countries. Our study found an increment in the consumption of cereals, fruits, vegetables, dairy products, meats, and nuts that occurred during the pandemic compared to the preceding period. Despite this noticeable increase during the pandemic, the Arab pregnant women in this study had significantly lower adherence to the USDA pregnancy guidelines. The daily consumption of almost all food groups was lower than the USDA's daily recommendations, except for fruits intake, which was higher than the daily standard. Demonstrated poor adherence to prenatal USDA dietary guidelines by Arab pregnant women can lead to numerous deficiencies and health risks among their offspring. In conclusion, our study showed that before and during the COVID-19 pandemic, poor adherence to dietary recommendations occurred in a considerable number of Arab pregnant women. The findings emphasize the need for nutritional education and intervention during prenatal visits.Öğe Dietary intake and lifestyle practices of eastern mediterranean postpartum women before and during COVID-19 pandemic: An internet-based cross sectional survey.(Frontiers, 2022) Qasrawi, Radwan; Tayyem, Reema F.; Al-Bayyari, Nahla; Al-Awwad, Narmeen; Abuhijleh, Haya; Hoteit, Reem; Badran, Eman; Allehdan, Sabika S.; Allehdan, Sabika; Bookari, Khlood; Arrish, Jamila; Abu-Seir, Rania; Hoteit, MahaBackground: During the lockdown period, a substantial group of these women reported lifestyle changes. Aim: The aim of the study is to characterize the dietary patterns, intake and the adherence to the United States Department of Agriculture (USDA) pregnancy guidelines before and during the COVID 19 pandemic in Eastern Mediterranean postartum women. Methods: An internet-based cross-sectional survey was used to collect the data. The survey was carried out among 1,939 postpartum women from five countries from the Eastern Mediterranean region. Change in dietary intake from the five food groups and the adherence to USDA's daily recommendations were assessed. Findings: There was a significant increase in the mean (SD) consumption of all the food groups, including bread, rice, and other cereals, fruits, vegetables, milk and milk products, white and red meat, and nuts during the pandemic. Around 84% of participants reported no/low adherence (0-2) to USDA guidelines, whereas only 15% reported moderate or high adherence (3-5) to the guidelines before the pandemic. However, there was an increase in the proportion of subjects reporting moderate/high adherence (22%) during the pandemic. Discussion and Conclusions: A substantial proportion of our study participants reported a lower dietary intake than the recommended amounts, and low adherence to the five food groups. Reasonable and applicable actions should be taken to protect postpartum women and their children from the effects of low dietary intake, particularly during pandemics and lockdowns. More researches are needed to identify the modifiable factors which could improve the nutritional status of the postpartum women during the pandemic.Öğe Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study(F1000 Research Ltd, 2022) Qasrawi, Radwan; Amro, Malak; VicunaPolo, Stephanny; Abu Al-Halawa, Diala; Agha, Hazem; Abu Seir, Rania; Hoteit, Maha; Hoteit, Reem; Allehdan, Sabika; Behzad, Nouf; Bookari, Khlood; AlKhalaf, Majid; Al-Sabbah, Haleama; Badran, Eman; Tayyem, ReemaBackground: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew's Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features' importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.Öğe 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 AbuBackground 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.Öğe 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, MahaDuring 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?Öğe 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, ReemaBackground: 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?
