Isfahan University of Medical SciencesJournal of Isfahan Medical School1027-75954277320240822The Relationship between Cyberbullying and Psychological Well-Being of Secondary High School Students in Isfahan CityThe Relationship between Cyberbullying and Psychological Well-Being of Secondary High School Students in Isfahan City5455523127810.48305/jims.v42.i773.0545FAMostafaAmini- RaraniAssociate Professor, Social Determinants of Health Research Center, Isfahan University of Medical Sciences, Isfahan, Iran0000-0002-4809-2237ZahraHeidariAssistant Professor, Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran0000-0001-7825-2388MaliheKahiPhD Student, Student Research Committee, School of Medical Information and Management, Isfahan University of Medical Sciences, Isfahan, Iran0009-0000-7374-3479Journal Article20240622<strong>Background:</strong><em> </em>Bullying has become a growing concern among teenagers, particularly in the context of cyberbullying, where electronic devices are used to harass others. Understanding the distinct roles of cyberbullies and victims of cyberbullying concerning various dimensions of psychological well-being is crucial. Therefore, this research was conducted to determine the relationship between cyberbullying and the psychological well-being of secondary high school students in Isfahan City.<br /><strong>Methods:</strong><strong> </strong>This study employed a cross-sectional design, with the research population consisting of high school students in Isfahan in 2021-2022. A stratified sampling method was used to select 1051 students, and data were collected through a checklist and three questionnaires, namely cyberbullying by Antoniado <em>et. al.</em> and an 18-item psychological well-being scale by Ryff.<br /><strong>Findings:</strong><strong> </strong>There was an inverse and significant relationship between cyberbullying and psychological well-being and its dimensions (except independence and positive relationship with others). There was an inverse and significant relationship between cyber victimization and psychological well-being and its dimensions (except purposefulness).<br /><strong>Conclusion:</strong> The study highlights that high school students involved in cyberbullying and cyber victimization are susceptible to adverse consequences, leading to a decline in their psychological well-being. Parents, teachers, and officials of the education organization must be well aware of how teenagers participate in bullying, as well as the consequences and variables affecting it.<strong>Background:</strong><em> </em>Bullying has become a growing concern among teenagers, particularly in the context of cyberbullying, where electronic devices are used to harass others. Understanding the distinct roles of cyberbullies and victims of cyberbullying concerning various dimensions of psychological well-being is crucial. Therefore, this research was conducted to determine the relationship between cyberbullying and the psychological well-being of secondary high school students in Isfahan City.<br /><strong>Methods:</strong><strong> </strong>This study employed a cross-sectional design, with the research population consisting of high school students in Isfahan in 2021-2022. A stratified sampling method was used to select 1051 students, and data were collected through a checklist and three questionnaires, namely cyberbullying by Antoniado <em>et. al.</em> and an 18-item psychological well-being scale by Ryff.<br /><strong>Findings:</strong><strong> </strong>There was an inverse and significant relationship between cyberbullying and psychological well-being and its dimensions (except independence and positive relationship with others). There was an inverse and significant relationship between cyber victimization and psychological well-being and its dimensions (except purposefulness).<br /><strong>Conclusion:</strong> The study highlights that high school students involved in cyberbullying and cyber victimization are susceptible to adverse consequences, leading to a decline in their psychological well-being. Parents, teachers, and officials of the education organization must be well aware of how teenagers participate in bullying, as well as the consequences and variables affecting it.https://jims.mui.ac.ir/article_31278_57cb680116490a4c6c0bb57a8fa476f1.pdfIsfahan University of Medical SciencesJournal of Isfahan Medical School1027-75954277320240822Automatic Diagnosis of Mild Cognitive Impairment from Electroencephalogram Using Joint Wavelet Packet Decomposition and Common Spatial PatternAutomatic Diagnosis of Mild Cognitive Impairment from Electroencephalogram Using Joint Wavelet Packet Decomposition and Common Spatial Pattern5535593127610.48305/jims.v42.i773.0553FAMohammadaliGanjaliPhD Candidate, Department of Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran0009-0009-3461-8099AlirezaMehridehnaviProfessor, Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran0000-0002-7964-0478VahidSadeghiDepartment of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, IranJournal Article20240626<strong>Background:</strong><em> </em>Mild cognitive impairment (MCI) is identified as the initial stage of Alzheimer's disease. This condition presents less severe symptoms compared to Alzheimer's Disease (AD) to the extent that it does not significantly impact daily activities. Due to its subtle symptoms, diagnosing MCI is considerably more challenging than diagnosing Alzheimer's. However, early detection of MCI enhances the chances of treatment and prevention of its progression to Alzheimer's and dementia.
<strong>Methods:</strong><strong> </strong>This study introduced a novel method for diagnosing MCI using an automated signal processing approach for electroencephalogram (EEG) signals. The method employs advanced signal processing techniques, including discrete wavelet transform in preprocessing and wavelet packet decomposition alongside spatial-spectral filters for feature extraction from EEG signals. EEG signals from 29 patients and 32 healthy individuals were utilized in this study.
<strong>Findings:</strong><strong> </strong>The proposed method achieved a classification accuracy of 100% using a random subsampling validation approach. Wavelet packet decomposition effectively isolated frequency sub-bands within the EEG signals, enabling precise extraction. Furthermore, feature extraction using features extracted by the filter bank common spatial pattern (FBCSP) contributed to the increased classification accuracy of the two groups.
<strong>Conclusion:</strong> This study introduces a novel approach for MCI diagnosis by extracting spatial-spectral features from frequency sub-bands of EEG signals obtained through wavelet packet decomposition. The findings underscore the significance of wavelet packet decomposition in separating frequency sub-bands and applying a common spatial pattern filter on these sub-bands for effective feature extraction in distinguishing healthy individuals from those with MCI.<strong>Background:</strong><em> </em>Mild cognitive impairment (MCI) is identified as the initial stage of Alzheimer's disease. This condition presents less severe symptoms compared to Alzheimer's Disease (AD) to the extent that it does not significantly impact daily activities. Due to its subtle symptoms, diagnosing MCI is considerably more challenging than diagnosing Alzheimer's. However, early detection of MCI enhances the chances of treatment and prevention of its progression to Alzheimer's and dementia.
<strong>Methods:</strong><strong> </strong>This study introduced a novel method for diagnosing MCI using an automated signal processing approach for electroencephalogram (EEG) signals. The method employs advanced signal processing techniques, including discrete wavelet transform in preprocessing and wavelet packet decomposition alongside spatial-spectral filters for feature extraction from EEG signals. EEG signals from 29 patients and 32 healthy individuals were utilized in this study.
<strong>Findings:</strong><strong> </strong>The proposed method achieved a classification accuracy of 100% using a random subsampling validation approach. Wavelet packet decomposition effectively isolated frequency sub-bands within the EEG signals, enabling precise extraction. Furthermore, feature extraction using features extracted by the filter bank common spatial pattern (FBCSP) contributed to the increased classification accuracy of the two groups.
<strong>Conclusion:</strong> This study introduces a novel approach for MCI diagnosis by extracting spatial-spectral features from frequency sub-bands of EEG signals obtained through wavelet packet decomposition. The findings underscore the significance of wavelet packet decomposition in separating frequency sub-bands and applying a common spatial pattern filter on these sub-bands for effective feature extraction in distinguishing healthy individuals from those with MCI.https://jims.mui.ac.ir/article_31276_8d16fcbd56cdafde0fc1ba4e68c4636c.pdfIsfahan University of Medical SciencesJournal of Isfahan Medical School1027-75954277320240822Types of Correlation Coefficients in Medical Science Research Using SPSS and R Software: A Review ArticleTypes of Correlation Coefficients in Medical Science Research Using SPSS and R Software: A Review Article5605733127710.48305/jims.v42.i773.0560FABehzadMahakiProfessor, Department of Biostatistics, School of Health, Health Data Science Research Center, Health Research Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran0000-0001-7062-6971SamiraJafariPhD Student, Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran0000-0003-2874-0417MansoorRezaeiProfessor, Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran0000-0002-6446-7289LeilaSoloukiPhD Student, Health Data Science Research Center, Health Research Institute, Department of Biostatistics, School of Health,Kermanshah University of Medical Sciences, Kermanshah, Iran0000-0001-7709-0815Journal Article20240416<strong>Background:</strong><em> </em>The correlation coefficient is a statistical measure that measures the degree of correlation between two variables and takes values between -1 and +1, which indicates the strength and direction of the linear relationship between two variables. Correlation coefficients play a significant role in medical science studies. These coefficients help researchers to identify and measure the relationship between different clinical variables and health outcomes<em>. </em><br /><strong>Description of the Article:</strong> Considering the importance of calculating correlation coefficients in medical research, the purpose of this paper is to introduce various types of correlation coefficients, simple and applied statistical concepts and, methods to investigate the relationship between variables according to their nature and how to calculate them. Types of commonly used correlation coefficients, including Pearson, Spearman, Kendall, Phi, V Kramer, Summers Delta, Gamma, agreement coefficient and, Landa correlation coefficient will be analyzed using SPSS and R software. Also, the conditions of using each of the correlation coefficients will be discussed according to the different assumptions and the unique interpretation of each of the coefficients. Finally, examples of the calculation and interpretation of each of the correlation coefficients in medical sciences are provided<em>.</em><br /><strong>Conclusion:</strong> Correlation is the most widely used statistical measure to evaluate the relationships between variables. However, it should be used with caution. Otherwise, it can lead to erroneous interpretations and results.<strong>Background:</strong><em> </em>The correlation coefficient is a statistical measure that measures the degree of correlation between two variables and takes values between -1 and +1, which indicates the strength and direction of the linear relationship between two variables. Correlation coefficients play a significant role in medical science studies. These coefficients help researchers to identify and measure the relationship between different clinical variables and health outcomes<em>. </em><br /><strong>Description of the Article:</strong> Considering the importance of calculating correlation coefficients in medical research, the purpose of this paper is to introduce various types of correlation coefficients, simple and applied statistical concepts and, methods to investigate the relationship between variables according to their nature and how to calculate them. Types of commonly used correlation coefficients, including Pearson, Spearman, Kendall, Phi, V Kramer, Summers Delta, Gamma, agreement coefficient and, Landa correlation coefficient will be analyzed using SPSS and R software. Also, the conditions of using each of the correlation coefficients will be discussed according to the different assumptions and the unique interpretation of each of the coefficients. Finally, examples of the calculation and interpretation of each of the correlation coefficients in medical sciences are provided<em>.</em><br /><strong>Conclusion:</strong> Correlation is the most widely used statistical measure to evaluate the relationships between variables. However, it should be used with caution. Otherwise, it can lead to erroneous interpretations and results.https://jims.mui.ac.ir/article_31277_9957d0d6c13e2cb00518c92af0df3b96.pdf