Isfahan University of Medical Sciences Journal of Isfahan Medical School 1027-7595 42 778 2024 09 22 The Effect of Sleep Hygiene Training Based on Cognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patients The Effect of Sleep Hygiene Training Based on Cognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patients 666 673 31296 10.48305/jims.v42.i778.0666 FA Noushin Khandehroo Family Medicine Spatialist, Isfahan University of Medical Sciences, Isfahan, Iran 0000-0001-7446-9109 Seyed Ahmad Mahmoudian Associate Profissor, Internal Medicine Spacialist, Isfahan University of Medical Sciences, Isfahan, Iran 0000-0001-5328-1895 Parastoo Golshiri Professor, Social Medicine Spacialist, Isfahan University of Medical Sciences, Isfahan, Iran 0000-0001-8446-8353 Journal Article 2023 12 01 Background: Cognitive behavioral therapy is used for psychiatric disorders and several medical conditions, including sleep disorders. The aim of the study was to find a significant relationship between improving sleep quality and the severity of migraine headaches using cognitive behavioral therapy. Methods: This is a clinical trial studythat has performed on 80 paitents whom were suffering from in Migrainein 2 Healthy Centerin Esfahan. We divided patients to two groups of Control and Intervention. Every group had 45patients.The patients of Intervention groups received 5 academic meetingson the subject of Cognitive Behavioral Treatment. The methods of academic meetings were including: lecturediscussion and conversation. The aims of meetings were: recognition of sleep mechanism, sleep promotion and well-being. Standard questionnaires of sleep quality of Pittsburgh and Midas were completed by patients before and 3 months after finishing of intervention. Findings: The education of Cognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patientshad a positive virtually effect on all sleep quality components(P < 0.001), Except using of sleeping drugs user, and had a negative virtually effect on number of headache days during 3 months ago and number of attacks during last month (P = 0.02). Conclusion: These study findings have emphasis on effectiveness of:education ofCognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patients, and offer this strategy for dealing with pain, number of attacks and sleep well-beinga. Background: Cognitive behavioral therapy is used for psychiatric disorders and several medical conditions, including sleep disorders. The aim of the study was to find a significant relationship between improving sleep quality and the severity of migraine headaches using cognitive behavioral therapy. Methods: This is a clinical trial studythat has performed on 80 paitents whom were suffering from in Migrainein 2 Healthy Centerin Esfahan. We divided patients to two groups of Control and Intervention. Every group had 45patients.The patients of Intervention groups received 5 academic meetingson the subject of Cognitive Behavioral Treatment. The methods of academic meetings were including: lecturediscussion and conversation. The aims of meetings were: recognition of sleep mechanism, sleep promotion and well-being. Standard questionnaires of sleep quality of Pittsburgh and Midas were completed by patients before and 3 months after finishing of intervention. Findings: The education of Cognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patientshad a positive virtually effect on all sleep quality components(P < 0.001), Except using of sleeping drugs user, and had a negative virtually effect on number of headache days during 3 months ago and number of attacks during last month (P = 0.02). Conclusion: These study findings have emphasis on effectiveness of:education ofCognitive Behavioral Therapy on Sleep Quality and Headache Intensity in Migraine Patients, and offer this strategy for dealing with pain, number of attacks and sleep well-beinga.

https://jims.mui.ac.ir/article_31296_754da7dc2ed681cb2084a83124fc63cf.pdf

Isfahan University of Medical Sciences Journal of Isfahan Medical School 1027-7595 42 778 2024 09 22 Evaluation of Machine Learning Algorithms for Predicting Tumor and Non-tumor Brain Mri Images Evaluation of Machine Learning Algorithms for Predicting Tumor and Non-tumor Brain Mri Images 674 686 31299 10.48305/jims.v42.i778.0674 FA Barat Barati Assistant Professor, Department of Radiology Technology, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran 0000-0002-2975-088X Maryam Erfaninejad Assistant Professor, Department of Basic Sciences, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran 0000-0003-1071-9257 Sima Hashemi Assistant Professor, Department of Nursing Education, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran 0000-0001-6279-5097 Nahid Chegeni Associate Professor, Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 0000-0002-6373-5456 Mohsen Arshadi Shoushtar Faculty of Medical Sciences, Shoushtarو Iran Journal Article 2024 08 12 Background: Early diagnosis of brain tumors using MRI and artificial intelligence algorithms is fundamental in improving treatment results. MRI images serve as the primary tool for identifying brain tumors. This study aims to evaluate machine learning algorithms for diagnosing brain tumors and non-tumors using MRI images. Methods: From kaggle.com a total of 2400 MRI images were collected, and a pre-processing step was performed on them. Algorithms such as logistic regression, decision tree, random forest, simple Bayes method, support vector machine, and K nearest neighbor were also implemented on the images. Findings: After applying all the algorithms, the values of training accuracy, test accuracy, accuracy, readability, F1 score, confusion matrix, and the area under the rocking curve were obtained to evaluate the performance criteria. Conclusion: The investigations indicated that logistic regression and random forest algorithms performed the best. Naive Bayes and decision tree algorithms need improvement. Background: Early diagnosis of brain tumors using MRI and artificial intelligence algorithms is fundamental in improving treatment results. MRI images serve as the primary tool for identifying brain tumors. This study aims to evaluate machine learning algorithms for diagnosing brain tumors and non-tumors using MRI images. Methods: From kaggle.com a total of 2400 MRI images were collected, and a pre-processing step was performed on them. Algorithms such as logistic regression, decision tree, random forest, simple Bayes method, support vector machine, and K nearest neighbor were also implemented on the images. Findings: After applying all the algorithms, the values of training accuracy, test accuracy, accuracy, readability, F1 score, confusion matrix, and the area under the rocking curve were obtained to evaluate the performance criteria. Conclusion: The investigations indicated that logistic regression and random forest algorithms performed the best. Naive Bayes and decision tree algorithms need improvement.

https://jims.mui.ac.ir/article_31299_9a9e0713c813c15dac6e857cd7e33e05.pdf

Isfahan University of Medical Sciences Journal of Isfahan Medical School 1027-7595 42 778 2024 09 22 Investigating the Prevalence of Obesity and Overweight and Its Related Factors in Secondary School Students of Bojnord City (16-18 Years Old) (with Emphasis on the Post-Coronavirus Era) Investigating the Prevalence of Obesity and Overweight and Its Related Factors in Secondary School Students of Bojnord City (16-18 Years Old) (with Emphasis on the Post-Coronavirus Era) 687 695 31297 10.48305/jims.v42.i778.0687 FA Mitra Khademosharie Assistant Professor, Department of Sport Sciences, School of Humanities, Kosar University of Bojnord, Bojnord, Iran 0000-0002-8506-7436 Azam Mollanovruzi Assistant Professor, Department of Sport Sciences, School of Humanities, Kosar University of Bojnord, Bojnord, Iran 0000-0002-8506-7436 Marziye Sadat Azarniveh Assistant Professor, Department of Sport Sciences, School of Literature and Human Sciences, Zabol University, Zabol, Iran 0000-0002-8506-7436 Delavar Avaze Vice President of Education and Physical Education of Bojnord City, Bojnord, Iran Journal Article 2024 07 03 Background: Being overweight in adolescence is an influential factor that determines obesity and overweight in adulthood, and there is a direct relationship between obesity in childhood, adolescence, and adulthood. Therefore, the present study aimed to investigate the prevalence of obesity and overweight and its related factors in male and female secondary school students of Bojnord City. Methods: This descriptive study was conducted on 744 male and female secondary school students of Bojnord City by cluster sampling. The weight and height of the samples were measured, and their body mass index was calculated using standard guidelines. Other information was collected by a researcher-made questionnaire and a physical activity questionnaire for each of the examined samples, and the data was analyzed by Mann-Whitney and Chi-square statistical tests. Findings: The results of this study showed that the prevalence of underweight was significantly higher among girls (P < 0.001) and the prevalence of overweight was significantly higher among boys (P = 0.013). Also, there was no significant difference between the prevalence of obesity in boys and girls (P = 0.085). Conclusion: Considering the results of the prevalence of obesity, overweight, and underweight in Bojnord students and the lack of awareness among students about complications related to obesity and overweight, the implementation of intervention programs to prevent High-risk behaviors of obesity and overweight in students seems necessary. Background: Being overweight in adolescence is an influential factor that determines obesity and overweight in adulthood, and there is a direct relationship between obesity in childhood, adolescence, and adulthood. Therefore, the present study aimed to investigate the prevalence of obesity and overweight and its related factors in male and female secondary school students of Bojnord City. Methods: This descriptive study was conducted on 744 male and female secondary school students of Bojnord City by cluster sampling. The weight and height of the samples were measured, and their body mass index was calculated using standard guidelines. Other information was collected by a researcher-made questionnaire and a physical activity questionnaire for each of the examined samples, and the data was analyzed by Mann-Whitney and Chi-square statistical tests. Findings: The results of this study showed that the prevalence of underweight was significantly higher among girls (P < 0.001) and the prevalence of overweight was significantly higher among boys (P = 0.013). Also, there was no significant difference between the prevalence of obesity in boys and girls (P = 0.085). Conclusion: Considering the results of the prevalence of obesity, overweight, and underweight in Bojnord students and the lack of awareness among students about complications related to obesity and overweight, the implementation of intervention programs to prevent High-risk behaviors of obesity and overweight in students seems necessary.

https://jims.mui.ac.ir/article_31297_0f9ef8cb70bb4135133a24a464ad55e1.pdf