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Qasrawi R, Badrasawi M, Al-Halawa DA, Polo SV, Khader RA, Al-Taweel H, Alwafa RA, Zahdeh R, Hahn A, Schuchardt JP. Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine. Eur J Nutr 2024:10.1007/s00394-024-03360-8. [PMID: 38512358 DOI: 10.1007/s00394-024-03360-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students. METHODS We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in Palestine. RESULTS Our study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia. CONCLUSIONS Besides the nutrients commonly known to be linked to anemia-like folate, Vit B6, C, B12, or Fe-the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine
- Department of Computer Engineering, Istinye University, Istanbul, Turkey
| | - Manal Badrasawi
- Department of Nutrition and Food Technology, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, Nablus, West Bank, Palestine
| | | | | | - Rami Abu Khader
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine
| | - Haneen Al-Taweel
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine
| | - Reem Abu Alwafa
- Department of Nutrition and Food Technology, Faculty of Agriculture and Veterinary Medicine, An-Najah National University, Nablus, West Bank, Palestine
| | - Rana Zahdeh
- Department of Applied Chemistry and Biology, College of Applied Sciences, Palestine Polytechnic University, Hebron, West Bank, Palestine
| | - Andreas Hahn
- Institute of Food Science and Human Nutrition, Leibniz University Hannover, Hannover, Germany
| | - Jan Philipp Schuchardt
- Institute of Food Science and Human Nutrition, Leibniz University Hannover, Hannover, Germany.
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Qasrawi R, Hoteit M, Tayyem R, Bookari K, Al Sabbah H, Kamel I, Dashti S, Allehdan S, Bawadi H, Waly M, Ibrahim MO, Polo SV, Al-Halawa DA. Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic. BMC Public Health 2023; 23:1805. [PMID: 37716999 PMCID: PMC10505318 DOI: 10.1186/s12889-023-16694-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
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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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
- Department of Computer Engineering, Istinye University, Istanbul, 34010, Turkey.
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
- PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Lebanese University Nutrition Surveillance Center (LUNSC), Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut, Lebanon
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar
- Department of Nutrition and Food Technology, Faculty of Agriculture, University of Jordan, Amman, 11942, Jordan
| | - Khlood Bookari
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Haleama Al Sabbah
- Department of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates
| | | | - Somaia Dashti
- Public Authority for Applied Education and Training, Kuwait City, Kuwait
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar
| | - Mostafa Waly
- Food Science and Nutrition Department, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
| | - Mohammed O Ibrahim
- Department of Nutrition and Food Technology, Faculty of Agriculture, Mu'tah University, Karak, Jordan
| | | | - Diala Abu Al-Halawa
- Department of Faculty of Medicine, Al Quds University, Jerusalem, Palestine.
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Qasrawi R, Vicuna Polo S, Abu Khader R, Abu Al-Halawa D, Hallaq S, Abu Halaweh N, Abdeen Z. Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments. Front Psychiatry 2023; 14:1071622. [PMID: 37304448 PMCID: PMC10250653 DOI: 10.3389/fpsyt.2023.1071622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Introduction Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development. Methods This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Results This study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age. Discussion The findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
- Department of Computer Engineering, Istinye University, Istanbul, Türkiye
| | - Stephanny Vicuna Polo
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | - Rami Abu Khader
- Al-Quds Center for Business Innovation and Entrepreneurship, Al-Quds University, Jerusalem, Palestine
| | | | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Palestine
| | - Nael Abu Halaweh
- Department of Computer Sciences, Al-Quds University, Jerusalem, Palestine
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Palestine
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Al Sabbah H, Assaf EA, Taha Z, Qasrawi R, Ismail LC, Al Dhaheri AS, Hoteit M, Al-Jawaldeh A, Tayyem R, Bawadi H, AlKhalaf M, Bookari K, Kamel I, Dashti S, Allehdan S, Waly M, Al-Halawa DA, Mansour R, Ibrahim M, Al-Mannai M, Survey Group* OBOTRCORONACOOKING. Impact of COVID-19 lockdown on smoking (waterpipe and cigarette) and participants' BMI across various sociodemographic groups in Arab countries in the Mediterranean Region. Tob Induc Dis 2022; 20:98. [PMID: 36419782 PMCID: PMC9650426 DOI: 10.18332/tid/155007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Tobacco smokers are at high risk of developing severe COVID-19. Lockdown was a chosen strategy to deal with the spread of infectious diseases; nonetheless, it influenced people's eating and smoking behaviors. The main objective of this study is to determine the impact of the COVID-19 lockdown on smoking (waterpipe and cigarette) behavior and its associations with sociodemographic characteristics and body mass index. METHODS The data were derived from a large-scale retrospective cross-sectional study using a validated online international survey from 38 countries (n=37207) conducted between 17 April and 25 June 2020. The Eastern Mediterranean Region (WHO-EMR countries) data related to 10 Arabic countries that participated in this survey have been selected for analysis in this study. A total of 12433 participants were included in the analysis of this study, reporting their smoking behavior and their BMI before and during the COVID-19 lockdown. Descriptive and regression analyses were conducted to examine the association between smoking practices and the participant's country of origin, sociodemographic characteristics, and BMI (kg/m2). RESULTS Overall, the prevalence rate of smoking decreased significantly during the lockdown from 29.8% to 23.5% (p<0.05). The percentage of females who smoke was higher than males among the studied population. The highest smoking prevalence was found in Lebanon (33.2%), and the lowest was in Oman (7.9%). In Egypt, Kuwait, Lebanon, and Saudi Arabia, the data showed a significant difference in the education level of smokers before and during the lockdown (p<0.05). Smokers in Lebanon had lower education levels than those in other countries, where the majority of smokers had a Bachelor's degree. The findings show that the BMI rates in Jordan, Lebanon, Oman, and Saudi Arabia significantly increased during the lockdown (p<0.05). The highest percentages of obesity among smokers before the lockdown were in Oman (33.3%), followed by Bahrain (28.4%) and Qatar (26.4%), whereas, during the lockdown, the percentage of obese smokers was highest in Bahrain (32.1%) followed by Qatar (31.3%) and Oman (25%). According to the logistic regression model, the odds ratio of smoking increased during the pandemic, whereas the odds ratio of TV watching decreased. This finding was statistically significant by age, gender, education level, country of residence, and work status. CONCLUSIONS Although the overall rates of smoking among the studied countries decreased during the lockdown period, we cannot attribute this change in smoking behavior to the lockdown. Smoking cessation services need to anticipate that unexpected disruptions, such as pandemic lockdowns, may be associated with changes in daily tobacco consumption. Public health authorities should promote the adoption of healthy lifestyles to reduce the long-term negative effects of the lockdown.
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Affiliation(s)
- Haleama Al Sabbah
- Department of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates
| | - Enas A. Assaf
- Faculty of Nursing, Applied Science Private University, Amman, Jordan
| | - Zainab Taha
- Department of Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates
| | - Radwan Qasrawi
- Department of Computer Science, Al-Quds University, East Jerusalem, Occupied Palestinian Territory,Department of Computer Engineering, Istinye University, Istanbul, Turkey
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, University of Sharjah, Sharjah, United Arab Emirates,Nuffield Department of Women’s and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Ayesha S. Al Dhaheri
- Department of Nutrition and Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
| | - Ayoub Al-Jawaldeh
- World Health Organization - Regional Office for the Eastern Mediterranean, Cairo, Egypt
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha, Qatar,Department of Nutrition and Food Technology, Faculty of Agriculture, University of Jordan, Amman, Jordan
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha, Qatar
| | - Majid AlKhalaf
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia
| | - Khlood Bookari
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia,Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Medina, Saudi Arabia
| | | | - Somaia Dashti
- Public Authority for Applied Education and Training, Kuwait City, Kuwait
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Mostafa Waly
- Food Science and Nutrition Department, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
| | - Diala Abu Al-Halawa
- Faculty of Medicine, Al-Quds University, East Jerusalem, Occupied Palestinian Territory
| | | | - Mohammed Ibrahim
- Department of Nutrition and Food Technology, Faculty of Agriculture, Mu’tah University, Karak, Jordan
| | - Mariam Al-Mannai
- Department of Mathematic, College of Science, University of Bahrain, Zallaq, Bahrain
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Giacaman N, Abu Al-Halawa D, Tos SM, Ibdah MG, Sharaf K. Extreme oncoplastic breast surgery: A case series of three patients from a lower-middle income country. Ann Med Surg (Lond) 2022; 84:104899. [DOI: 10.1016/j.amsu.2022.104899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/23/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022] Open
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Tayyem R, Ibrahim MO, Mortada H, AlKhalaf M, Bookari K, Al Sabbah H, Qasrawi R, Kamel I, Dashti S, Allehdan S, Bawadi H, Waly M, Abuhijleh H, Hammouh F, Al-Awwad N, Al-Bayyari N, Cheikh Ismail L, Abu Al-Halawa D, Othman M, Hoteit M. Sex disparities in food consumption patterns, dietary diversity and determinants of self-reported body weight changes before and amid the COVID-19 pandemic in 10 Arab countries. Front Public Health 2022; 10:1029219. [PMID: 36388291 PMCID: PMC9650450 DOI: 10.3389/fpubh.2022.1029219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/27/2022] [Indexed: 01/29/2023] Open
Abstract
Background The COVID-19 pandemic along with its confinement period boosted lifestyle modifications and impacted women and men differently which exacerbated existing gender inequalities. The main objective of this paper is to assess the gender-based differentials in food consumption patterns, dietary diversity and the determinants favoring weight change before and amid the COVID-19 pandemic among Arab men and women from 10 Arab countries. Methods A cross-sectional study was conducted based on a convenience sample of 12,447 households' family members (mean age: 33.2 ± 12.9; 50.1% females) and information from participants aged 18 years and above was collected about periods before and during the pandemic. Results Findings showed that, during the COVID-19 period, the dietary diversity, declined by 1.9% among females compared to males (0.4%) (p < 0.001) and by 1.5% among overweight participants (p < 0.001) compared to their counterparts. Conclusions To conclude, gender-sensitive strategies and policies to address weight gain and dietary diversity during emergent shocks and pandemics are urgently needed in the region.
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Affiliation(s)
- Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University (QU)-Health, Qatar University, Doha, Qatar,Department of Nutrition and Food Technology, Faculty of Agriculture, University of Jordan, Amman, Jordan,Reema Tayyem
| | - Mohammed O. Ibrahim
- Department of Nutrition and Food Technology, Faculty of Agriculture, Mu'tah University, Karak, Jordan
| | | | - Majid AlKhalaf
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia
| | - Khlood Bookari
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia,Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Haleama Al Sabbah
- Department of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates
| | - Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine,Department of Computer Engineering, Istinye University, Istanbul, Turkey
| | | | - Somaia Dashti
- Public Authority for Applied Education and Training, Kuwait City, Kuwait
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, Qatar University (QU)-Health, Qatar University, Doha, Qatar
| | - Mostafa Waly
- Food Science and Nutrition Department, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
| | - Haya Abuhijleh
- Department of Human Nutrition, College of Health Sciences, Qatar University (QU)-Health, Qatar University, Doha, Qatar
| | - Fadwa Hammouh
- Department of Nutrition and Dietetics, Health Sciences Faculty, American University of Madaba, Madaba, Jordan
| | - Narmeen Al-Awwad
- Department of Clinical Nutrition and Dietetics, Faculty of Applied Health Sciences, Hashemite University, Zarqa, Jordan
| | - Nahla Al-Bayyari
- Department of Nutrition and Food Technology, Faculty of Al-Huson University College, Al-Balqa Applied University, As-Salt, Jordan
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, University of Sharjah, Sharjah, United Arab Emirates,Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom
| | | | - Manal Othman
- Department of Human Nutrition, College of Health Sciences, Qatar University (QU)-Health, Qatar University, Doha, Qatar
| | | | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon,PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut, Lebano,Lebanese University Nutrition Surveillance Center, Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut, Lebanon,University Medical Center, Lebanese University, Beirut, Lebanon,*Correspondence: Maha Hoteit
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Al Sabbah H, Taha Z, Qasrawi R, Assaf EA, Cheikh Ismail L, Al Dhaheri AS, Hoteit M, Al-Jawaldeh A, Tayyem R, Bawadi H, AlKhalaf M, Bookari K, Kamel I, Dashti S, Allehdan S, Alalwan TA, Hammouh F, Waly MI, Al-Halawa DA, Mansour R, Abu Farha A. The Impact of COVID-19 on Physical (In)Activity Behavior in 10 Arab Countries. Int J Environ Res Public Health 2022; 19:ijerph191710832. [PMID: 36078548 PMCID: PMC9518470 DOI: 10.3390/ijerph191710832] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/18/2022] [Accepted: 08/24/2022] [Indexed: 05/07/2023]
Abstract
Insufficient physical activity is considered a strong risk factor associated with non-communicable diseases. This study aimed to assess the impact of COVID-19 on physical (in)activity behavior in 10 Arab countries before and during the lockdown. A cross-sectional study using a validated online survey was launched originally in 38 different countries. The Eastern Mediterranean regional data related to the 10 Arabic countries that participated in the survey were selected for analysis in this study. A total of 12,433 participants were included in this analysis. The mean age of the participants was 30.3 (SD, 11.7) years. Descriptive and regression analyses were conducted to examine the associations between physical activity levels and the participants' sociodemographic characteristics, watching TV, screen time, and computer usage. Physical activity levels decreased significantly during the lockdown. Participants' country of origin, gender, and education were associated with physical activity before and during the lockdown (p < 0.050). Older age, watching TV, and using computers had a negative effect on physical activity before and during the lockdown (p < 0.050). Strategies to improve physical activity and minimize sedentary behavior should be implemented, as well as to reduce unhealthy levels of inactive time, especially during times of crisis. Further research on the influence of a lack of physical activity on overall health status, as well as on the COVID-19 disease effect is recommended.
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Affiliation(s)
- Haleama Al Sabbah
- Department of Health Sciences, Zayed University, Dubai P.O. Box 19282, United Arab Emirates
- Correspondence: ; Tel.: +971-569501179
| | - Zainab Taha
- Department of Health Sciences, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates
| | - Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem 20002, Palestine
- Department of Computer Engineering, Istinye University, Istanbul 34010, Turkey
| | - Enas A. Assaf
- Faculty of Nursing, Applied Science Private University, Amman 11931, Jordan
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, University of Sharjah, Sharjah 27272, United Arab Emirates
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ayesha S. Al Dhaheri
- Department of Nutrition and Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut P.O. Box 11-0236, Lebanon
- PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut P.O. Box 11-0236, Lebanon
- Lebanese University Nutrition Surveillance Center (LUNSC), Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut P.O. Box 11-0236, Lebanon
| | - Ayoub Al-Jawaldeh
- World Health Organization Regional Office for the Eastern Mediterranean, Cairo 11884, Egypt
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha 2713, Qatar
- Department of Nutrition and Food Technology, School of Agriculture, The University of Jordan, Amman 11942, Jordan
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha 2713, Qatar
| | - Majid AlKhalaf
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh 11451, Saudi Arabia
| | - Khlood Bookari
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh 11451, Saudi Arabia
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Madinah 42353, Saudi Arabia
| | - Iman Kamel
- National Research Centre, Cairo 11884, Egypt
| | - Somaia Dashti
- Public Authority for Applied Education and Training, Kuwait City 13092, Kuwait
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Sakhir P.O. Box 32038, Bahrain
| | - Tariq A. Alalwan
- Department of Biology, College of Science, University of Bahrain, Sakhir P.O. Box 32038, Bahrain
| | - Fadwa Hammouh
- Department of Nutrition and Dietetics, Faculty of Health Sciences, American University of Madaba, Amman 11821, Jordan
| | - Mostafa I. Waly
- Food Science and Nutrition Department, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat 123, Oman
| | | | - Rania Mansour
- Doha Institute for Graduate Studies, Doha P.O. Box 200592, Qatar
| | - Allam Abu Farha
- College of Business and Economics, Qatar University, Doha 2713, Qatar
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Qasrawi R, Abu Al-Halawa D. Cluster Analysis and Classification Model of Nutritional Anemia Associated Risk Factors Among Palestinian Schoolchildren, 2014. Front Nutr 2022; 9:838937. [PMID: 35619964 PMCID: PMC9127973 DOI: 10.3389/fnut.2022.838937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
Nutritional inadequacy has been a major health problem worldwide. One of the many health problems that result from it is anemia. Anemia is considered a health concern among all ages, particularly children, as it has been associated with cognitive and developmental delays. Researchers have investigated the association between nutritional deficiencies and anemia through various methods. As novel analytical methods are needed to ascertain the association and reveal indirect ones, we aimed to classify nutritional anemia using the cluster analysis approach. In this study, we included 4,762 students aged between 10 and 17 years attending public and UNRWA schools in the West Bank. Students' 24-h food recall and blood sample data were collected for nutrient intake and hemoglobin analysis. The K-means cluster analysis was used to cluster the hemoglobin levels into two groups. Vitamin B12, folate, and iron intakes were used as the indicators of nutrient intake associated with anemia and were classified as per the Recommended Dietary Allowance (RDA) values. We applied the Classification and Regression Tree (CRT) model for studying the association between hemoglobin clusters and vitamin B12, folate, and iron intakes, sociodemographic variables, and health-related risk factors, accounting for grade and age. Results indicated that 46.4% of the students were classified into the low hemoglobin cluster, and 60.7, 72.5, and 30.3% of vitamin B12, folate, and iron intakes, respectively, were below RDA. The CRT analysis indicated that vitamin B12, iron, and folate intakes are important factors related to anemia in girls associated with age, locality, food consumption patterns, and physical activity levels, while iron and folate intakes were significant factors related to anemia in boys associated with the place of residence and the educational level of their mothers. The deployment of clustering and classification techniques for identifying the association between anemia and nutritional factors might facilitate the development of nutritional anemia prevention and intervention programs that will improve the health and wellbeing of schoolchildren.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine.,Department of Computer Engineering, Istinye University, Istanbul, Turkey
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Qasrawi R, Amro M, VicunaPolo S, Abu Al-Halawa D, Agha H, Abu Seir R, Hoteit M, Hoteit R, Allehdan S, Behzad N, Bookari K, AlKhalaf M, Al-Sabbah H, Badran E, Tayyem R. Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study. F1000Res 2022; 11:390. [PMID: 36111217 PMCID: PMC9445566 DOI: 10.12688/f1000research.110090.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 02/02/2023] Open
Abstract
Background: 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.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
- Dpertment of Computer Engineering, Istinye University, Istanbul, 34010, Turkey
| | - Malak Amro
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Stephanny VicunaPolo
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Diala Abu Al-Halawa
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Hazem Agha
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Rania Abu Seir
- Department of Medical Laboratory Sciences, Al-Quds University, Jerusalem, Palestinian Territory
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
- PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Lebanese University Nutrition Surveillance Center (LUNSC), Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut, Lebanon
| | - Reem Hoteit
- Clinical Research Institute, American University of Beirut, Bliss Street, Riad El Solh 1107 2020, Beirut, Lebanon
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Nouf Behzad
- Salmaniya Medical Complex, Ministry of Health, Manama, Bahrain
| | - Khlood Bookari
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Medna, Saudi Arabia
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Majid AlKhalaf
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Haleama Al-Sabbah
- Department of Health Sciences, Zayed University, Dubai, United Arab Emirates
| | - Eman Badran
- Faculty of Medicine, University of Jordan, Amman, Jordan
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha, Qatar
- Department of Nutrition and Food Technology, Faculty of Agriculture, The University of Jordan, Amman, 11942, Jordan
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Qasrawi R, Vicuna Polo SP, Abu Al-Halawa D, Hallaq S, Abdeen Z. Schoolchildren’ Depression and Anxiety Risk Factors Assessment and Prediction: Machine Learning Techniques Performance Analysis (Preprint). JMIR Form Res 2021; 6:e32736. [PMID: 35665695 PMCID: PMC9475423 DOI: 10.2196/32736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 02/03/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Ramallah, Occupied Palestinian Territory
- Department of Computer Engineering, Istinye University, Istanbul, Turkey
| | - Stephanny Paola Vicuna Polo
- Center for Business Innovation and Technology, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Diala Abu Al-Halawa
- Faculty of Medicine, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Occupied Palestinian Territory
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