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Mina Shirvani, Mostafa Roshanzadeh, Maryam Rabiey Faradonbeh, Razieh Mirzaeian,
Volume 18, Issue 2 (5-2024)
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Nursing students are exposed to various educational, family, and social stresses and various factors can affect their mental health. Therefore, in order to pay attention to different dimensions of health and investigate the effects of spirituality on health, the present study was conducted to determine the effect of fasting on the mental health of nursing students of Borujen Faculty of Medical Sciences.
Materials and Methods: The present semi-experimental study with a pre-test-post-test design was conducted in 2022 at Shahrekord University of Medical Sciences. Ninety nursing students of Borujen Faculty of Medical Sciences were selected by available methods and assigned to two intervention and control groups by a simple random method. The intervention in this study included at least 23 days of fasting during Ramadan. The data before and after the intervention were collected by the demographic information questionnaire and the 21-question depression, anxiety, and stress standard tool (DASS). The validity and reliability of this questionnaire were conducted for the first time in Iran by Sahebi et al. in 2005. SPSS was used for analysis. Descriptive statistical tests including frequency percentage, mean and standard deviation, and inferential statistical tests including t-test, paired t-test, analysis of variance, and chi-square were used.
Results: There was no significant difference in the total mental health score between intervention (32.32±11.62) and control (29.87±14.09) groups before the intervention (P=0.08). There was a significant difference in this score between intervention (20.6±5.71) and control (29.49±8.9) groups after the intervention (P=0.04). The total mental health score in the control group before (29.87±14.09) and after (29.49±8.9) the intervention had no significant difference (P=0.15); while in the intervention group before (32.32±11.62) and after (20.6±5.71) the intervention had a significant difference (P=0.001). Mental health dimensions before and after intervention, indicated that anxiety (P=0.04) and stress (P=0.003) decreased significantly after the intervention in the intervention group. However, there was no significant difference in the depression dimension (P=0.06).
Conclusion: According to the results, it should be said that regular and periodic examination of the health level, and the promotion of educational and training programs on the subject of fasting to improve mental health, should be considered.
Materials and Methods: The present semi-experimental study with a pre-test-post-test design was conducted in 2022 at Shahrekord University of Medical Sciences. Ninety nursing students of Borujen Faculty of Medical Sciences were selected by available methods and assigned to two intervention and control groups by a simple random method. The intervention in this study included at least 23 days of fasting during Ramadan. The data before and after the intervention were collected by the demographic information questionnaire and the 21-question depression, anxiety, and stress standard tool (DASS). The validity and reliability of this questionnaire were conducted for the first time in Iran by Sahebi et al. in 2005. SPSS was used for analysis. Descriptive statistical tests including frequency percentage, mean and standard deviation, and inferential statistical tests including t-test, paired t-test, analysis of variance, and chi-square were used.
Results: There was no significant difference in the total mental health score between intervention (32.32±11.62) and control (29.87±14.09) groups before the intervention (P=0.08). There was a significant difference in this score between intervention (20.6±5.71) and control (29.49±8.9) groups after the intervention (P=0.04). The total mental health score in the control group before (29.87±14.09) and after (29.49±8.9) the intervention had no significant difference (P=0.15); while in the intervention group before (32.32±11.62) and after (20.6±5.71) the intervention had a significant difference (P=0.001). Mental health dimensions before and after intervention, indicated that anxiety (P=0.04) and stress (P=0.003) decreased significantly after the intervention in the intervention group. However, there was no significant difference in the depression dimension (P=0.06).
Conclusion: According to the results, it should be said that regular and periodic examination of the health level, and the promotion of educational and training programs on the subject of fasting to improve mental health, should be considered.
Sara Hashemi, Shahla Faramarzi, Laya Rahmani Pirouz, Azita Yazdani,
Volume 18, Issue 2 (5-2024)
Volume 18, Issue 2 (5-2024)
Abstract
Background and Aim: Burn injury are one of the most common traumas worldwide and the sixth leading cause of death in Iran. The challenges related to the survival rate of burn patients, as well as the associated mortality cases, have led to advancements in the identification of risk factors. Early detection and recognition of these risk factors are essential, and the provision of predictive models can be beneficial. This research was conducted with the aim of reviewing the effectiveness of artificial intelligence in predicting survival in burn patients.
Materials and Methods: This study was a systematic review. A comprehensive search of Scopus, PubMed, IEEE, and Web of Science databases was conducted from inception to July 2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Mesh terms related to burn, artificial intelligence, survival and prediction were used in the search.
Results: Out of 3599 identified studies, only nine were included in the analysis. Based on the articles reviewed, the effective factors in predicting survival or mortality in burn patients were classified into four main categories: demographic, clinical, tests and co-morbidities. Some of the known effective factors in patient survival, which have been examined in over 40% of studies, include age, gender, total body surface area, inhalation injury, and type of burn. The results showed that in the studies reviewed, the volume of the smallest dataset used in the analyses was 92 samples. In contrast, the volume of the largest dataset used was reported to be 66,611 samples. Among these studies, 33% have indicated that artificial neural network algorithms and random forest show the best performance. The criteria used to evaluate the models in the retrieved studies are diverse.
Conclusion: The use of machine learning algorithms in predicting the survival of burn patients is promising. The results obtained from identified influential factors can assist data science researchers in the data understanding phase and can serve as a roadmap in collecting the initial dataset.
Materials and Methods: This study was a systematic review. A comprehensive search of Scopus, PubMed, IEEE, and Web of Science databases was conducted from inception to July 2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Mesh terms related to burn, artificial intelligence, survival and prediction were used in the search.
Results: Out of 3599 identified studies, only nine were included in the analysis. Based on the articles reviewed, the effective factors in predicting survival or mortality in burn patients were classified into four main categories: demographic, clinical, tests and co-morbidities. Some of the known effective factors in patient survival, which have been examined in over 40% of studies, include age, gender, total body surface area, inhalation injury, and type of burn. The results showed that in the studies reviewed, the volume of the smallest dataset used in the analyses was 92 samples. In contrast, the volume of the largest dataset used was reported to be 66,611 samples. Among these studies, 33% have indicated that artificial neural network algorithms and random forest show the best performance. The criteria used to evaluate the models in the retrieved studies are diverse.
Conclusion: The use of machine learning algorithms in predicting the survival of burn patients is promising. The results obtained from identified influential factors can assist data science researchers in the data understanding phase and can serve as a roadmap in collecting the initial dataset.
Majid Jangi, Azade Shayan Babokan, Nasim Ghalili Najafabadi, Sedigheh Torki Harchegani,
Volume 18, Issue 3 (7-2024)
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Considering the limitation of resourses, improvement of the hospital efficiency is an absolute necessity. The Covid19 pandemic had a considerable effect on performance indicators of hospitals. This study aimed to investigate changes of indicators of hospitals affiliated to Isfahan University of Medical Sciences before and after Covid19.
Materials and Methods: This study was descriptive-cross sectional. The statistical population included all hospitals under the coverage of Isfahan University of Medical Sciences (38). The input data were related to the three years 2019 to 2021 (the year 2019 as the year before outbreak of Covid-19, the year 2020 as the first year of outbreak and the year 2021 as the second year of outbreak), which were collected using the researcher’s form based on reports extracted from the statistics and hospital information system available in the statistics and information technology management and finally the data analyzed through the PabonLasso model.
Results: Process of indicators during the years 2019-2021 shows that mean of indicators of bed occupancy rate and bed turnover rate as the first year of outbreak of Covid19 (2020) that was the peak of the disease has decreased as compared to the year 2019 and average length of stay has increased. In years 2019, 2020 and 2021, 24.32, 23.68 and 24.32 percent of hospitals were in the third area (efficient area). From 2019 to 2020, efficiency change was observed in 27 percent of the hospitals where 6 hospitals had positive trend and 4 hospitals had negative trend and from 2020 to 2021, efficiency change was observed in 16 percent of hospitals where 2 hospitals had positive and 4 had negative trend.
Conclusion: Considering the low efficiency of hospitals, it is suggested to carry out continuous and annual assessment of efficiency changes in hospitals in order to identify the causes of inefficiency early and preventing its drop and it is necessary for health managers and policy makers to take appropriate measures in the conditions of the outbreak of unexpected disease such as the outbreak of Covid-19 in order to use hospital resources more optimally.
Materials and Methods: This study was descriptive-cross sectional. The statistical population included all hospitals under the coverage of Isfahan University of Medical Sciences (38). The input data were related to the three years 2019 to 2021 (the year 2019 as the year before outbreak of Covid-19, the year 2020 as the first year of outbreak and the year 2021 as the second year of outbreak), which were collected using the researcher’s form based on reports extracted from the statistics and hospital information system available in the statistics and information technology management and finally the data analyzed through the PabonLasso model.
Results: Process of indicators during the years 2019-2021 shows that mean of indicators of bed occupancy rate and bed turnover rate as the first year of outbreak of Covid19 (2020) that was the peak of the disease has decreased as compared to the year 2019 and average length of stay has increased. In years 2019, 2020 and 2021, 24.32, 23.68 and 24.32 percent of hospitals were in the third area (efficient area). From 2019 to 2020, efficiency change was observed in 27 percent of the hospitals where 6 hospitals had positive trend and 4 hospitals had negative trend and from 2020 to 2021, efficiency change was observed in 16 percent of hospitals where 2 hospitals had positive and 4 had negative trend.
Conclusion: Considering the low efficiency of hospitals, it is suggested to carry out continuous and annual assessment of efficiency changes in hospitals in order to identify the causes of inefficiency early and preventing its drop and it is necessary for health managers and policy makers to take appropriate measures in the conditions of the outbreak of unexpected disease such as the outbreak of Covid-19 in order to use hospital resources more optimally.
Somayyeh Zakerabasali, Farnaz Salehian,
Volume 18, Issue 3 (7-2024)
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Today, information dashboards are the main tools for understanding and extracting knowledge from large data sets and can be used in various forms by healthcare providers. At the same time as the COVID-19 epidemic, several information dashboards were designed and developed. Still, due to the speed of the spread of this virus, there was no opportunity to evaluate them. Therefore, this research was conducted to evaluate the usability of the Covid-19 management dashboard.
Materials and Methods: This descriptive-cross-sectional study was conducted on the management dashboard of Shiraz University of Medical Sciences. The dashboard was evaluated using an exploratory evaluation method with the participation of three medical informatics experts. Each of the evaluators evaluated the system independently and identified its problems by using thirteen principle checklist. Then, with the presence of all evaluators, the list of identified problems was combined, repeated problems were removed from the list and a single list was prepared. In this joint meeting, any disagreements about the problems found by the evaluators were discussed and reached a common opinion. Finally, the evaluators determined and reported the severity of the problems.
Results: In this evaluation, a total of 79 usability problems were identified. The highest number of problems was related to the feature “Help and Documentation” (12 problems), and the lowest number of problems was related to the features “Aesthetic and Minimalist Design” (2 problems) and “Privacy” (1 problem). 45.58% of the identified problems were in the category of major problems. The average degree of severity of the problems was from 2.05 (minor problem) related to the feature of “Pleasurable and Respectful Interaction with the User” to 2.99 (major problem) related to the feature of “User Control and Freedom”. Also, the average severity of problems was calculated as 2.5, classified in the range of minor problems.
Conclusion: The heuristic evaluation method identifies user interface problems of information systems and dashboards using predetermined standards. If these problems are not resolved, they will cause users’ time wasted, errors to increase, information quality to decrease, and users’ dissatisfaction and confusion.
Materials and Methods: This descriptive-cross-sectional study was conducted on the management dashboard of Shiraz University of Medical Sciences. The dashboard was evaluated using an exploratory evaluation method with the participation of three medical informatics experts. Each of the evaluators evaluated the system independently and identified its problems by using thirteen principle checklist. Then, with the presence of all evaluators, the list of identified problems was combined, repeated problems were removed from the list and a single list was prepared. In this joint meeting, any disagreements about the problems found by the evaluators were discussed and reached a common opinion. Finally, the evaluators determined and reported the severity of the problems.
Results: In this evaluation, a total of 79 usability problems were identified. The highest number of problems was related to the feature “Help and Documentation” (12 problems), and the lowest number of problems was related to the features “Aesthetic and Minimalist Design” (2 problems) and “Privacy” (1 problem). 45.58% of the identified problems were in the category of major problems. The average degree of severity of the problems was from 2.05 (minor problem) related to the feature of “Pleasurable and Respectful Interaction with the User” to 2.99 (major problem) related to the feature of “User Control and Freedom”. Also, the average severity of problems was calculated as 2.5, classified in the range of minor problems.
Conclusion: The heuristic evaluation method identifies user interface problems of information systems and dashboards using predetermined standards. If these problems are not resolved, they will cause users’ time wasted, errors to increase, information quality to decrease, and users’ dissatisfaction and confusion.
Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan,
Volume 18, Issue 3 (7-2024)
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Cataracts are recognized as the cause of 51% of blindness worldwide. Following the promising initial results of artificial intelligence systems in eye diseases, AI algorithms have been applied in the diagnosis of cataracts, grading the severity of cataracts, intraocular lens calculations, and even as an assistive tool in cataract surgery. This study presents a systematic review of AI techniques in the management of cataract disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.
Seyyedeh Fatemeh Mousavi Baigi, Reyhaneh Norouzi Aval, Masoumeh Sarbaz, Khalil Kimiafar,
Volume 18, Issue 4 (10-2024)
Volume 18, Issue 4 (10-2024)
Abstract
Background and Aim: Proficiency in medical terminology is a basic competency of most medical students, which ensures communication with other healthcare providers. Facing the lack of motivation and involvement of students, applications, and games based on smartphones are considered as a possible educational option. Due to the rapid expansion of these applications, a correct evaluation of their quality is often not provided. This study investigated and evaluated the quality of smartphone applications and games for teaching medical terminology.
Materials and Methods: A systematic review was conducted in August 2024, in the official stores of Bazaar and Google Play applications. The two main keywords “medical terminology” and “medical vocabulary” were searched in Persian and English. Two evaluators independently downloaded and evaluated smartphone-based applications and games for teaching medical terminology. The same checklist was used for data extraction. The quality of apps was measured using the Mobile App Rating Scale (MARS). The points of each section, the final score of the retrieved applications, and the mean and standard deviation were obtained.
Results: In total, eighteen programs were included in this study, four of which were games. The average quality of the programs was between 2.70 and 4.30 (average 3.80) on a scale from 1 (inadequate) to 5 (excellent). The best scores are in performance (mean: 3.91), followed by information quality (mean: 3.15). Aesthetics (mean: 2.56) and mental quality of the program (mean: 2.58) had the lowest scores. Two applications offered data privacy, and four had privacy statements. The game ‘Dean Vaughn’ and the application ‘MediTerm’ received the highest overall scores.
Conclusion: This study provides an analysis and description of smartphone-based applications and games for teaching medical terminology to help students and users choose high-quality applications that suit their needs and tastes, and on the other hand, it provides the possibility of identifying research and operational gaps to strengthen and design more effective and better applications for the developers of these applications. The applications evaluated were generally of good overall quality, particularly in terms of functionality and information. However, some applications need to improve aspects such as aesthetics and subjective quality to increase their impact on users and ensure better security and privacy.
Materials and Methods: A systematic review was conducted in August 2024, in the official stores of Bazaar and Google Play applications. The two main keywords “medical terminology” and “medical vocabulary” were searched in Persian and English. Two evaluators independently downloaded and evaluated smartphone-based applications and games for teaching medical terminology. The same checklist was used for data extraction. The quality of apps was measured using the Mobile App Rating Scale (MARS). The points of each section, the final score of the retrieved applications, and the mean and standard deviation were obtained.
Results: In total, eighteen programs were included in this study, four of which were games. The average quality of the programs was between 2.70 and 4.30 (average 3.80) on a scale from 1 (inadequate) to 5 (excellent). The best scores are in performance (mean: 3.91), followed by information quality (mean: 3.15). Aesthetics (mean: 2.56) and mental quality of the program (mean: 2.58) had the lowest scores. Two applications offered data privacy, and four had privacy statements. The game ‘Dean Vaughn’ and the application ‘MediTerm’ received the highest overall scores.
Conclusion: This study provides an analysis and description of smartphone-based applications and games for teaching medical terminology to help students and users choose high-quality applications that suit their needs and tastes, and on the other hand, it provides the possibility of identifying research and operational gaps to strengthen and design more effective and better applications for the developers of these applications. The applications evaluated were generally of good overall quality, particularly in terms of functionality and information. However, some applications need to improve aspects such as aesthetics and subjective quality to increase their impact on users and ensure better security and privacy.
Taleb Khodaveisi, Hamid Bouraghi, Tooba Mehrabi, Javad Faradmal, Mahdiye Shojaei Baghini, Ali Mohammadpour,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: Identifying the educational needs of health information technology staff is essential before implementing any continuous education programs. This comprehensive study investigates these needs among health information technology personnel working in hospitals in the Hamadan province, considering both the general and specialized aspects of the field.
Materials and Methods: This descriptive cross-sectional study was conducted across 11 hospitals affiliated with Hamadan University of Medical Sciences. The study population comprised staff from the reception, medical records, statistics, and coding departments. Data were gathered using a validated and reliable standardized questionnaire. Collection methods included both in-person and remote approaches. Data analysis was performed using SPSS software, with results reported through descriptive and inferential statistics, specifically utilizing the Kruskal-Wallis test.
Results: The results of this study showed that among the generally accepted needs, items such as information technology (96.7%), legal aspects of medical records (87.6%), and communication skills (76.7%) had the highest percentage. Additionally, educational needs varied across different units: Coding unit staff required more training in the principles of diagnosis documentation (92.9%), familiarity with the coding guidelines for causes of death (85.7%), and familiarity with the coding guidelines for procedures (85.7%), statistics unit staff needed training in statistical software, and reception and medical records staff required education on relevant regulations. There was also a significant correlation between educational needs and certain individual characteristics such as work experience, education level, gender, and field of study.
Conclusion: The study results indicate that designing effective educational programs for health information technology staff requires consideration of individual characteristics, such as gender, work experience, and education level. Additionally, the training should be continuous, tailored to the distinct needs of each group, and delivered at appropriate intervals.
Materials and Methods: This descriptive cross-sectional study was conducted across 11 hospitals affiliated with Hamadan University of Medical Sciences. The study population comprised staff from the reception, medical records, statistics, and coding departments. Data were gathered using a validated and reliable standardized questionnaire. Collection methods included both in-person and remote approaches. Data analysis was performed using SPSS software, with results reported through descriptive and inferential statistics, specifically utilizing the Kruskal-Wallis test.
Results: The results of this study showed that among the generally accepted needs, items such as information technology (96.7%), legal aspects of medical records (87.6%), and communication skills (76.7%) had the highest percentage. Additionally, educational needs varied across different units: Coding unit staff required more training in the principles of diagnosis documentation (92.9%), familiarity with the coding guidelines for causes of death (85.7%), and familiarity with the coding guidelines for procedures (85.7%), statistics unit staff needed training in statistical software, and reception and medical records staff required education on relevant regulations. There was also a significant correlation between educational needs and certain individual characteristics such as work experience, education level, gender, and field of study.
Conclusion: The study results indicate that designing effective educational programs for health information technology staff requires consideration of individual characteristics, such as gender, work experience, and education level. Additionally, the training should be continuous, tailored to the distinct needs of each group, and delivered at appropriate intervals.
Zohre Abbaszade Molaei, Aeen Mohammadi, Manijeh Hooshmandja,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: With the advancement of information technology in the new century, changes are experienced in all aspects of life. One of the reflections of these changes in education is conducting exams electronically instead of paper-and-pencil examinations. The success of virtual education is not achievable without considering the students’ viewpoints towards it. This study aimed to investigate the attitude and performance of students in online exams and their relationship with academic achievement.
Materials and Methods: This research is a mixed-method study (qualitative-quantitative). First, all related articles published, between 2000 to 2022, were extracted from ERIC, PubMed, ScienceDirect databases, and the Google Scholar search engine. Then, the attitude and performance questionnaire was designed and validated based on the literature review results. All BSc and MSc. nursing and midwifery students of Sarivar Nassibeh School filled out the questionnaire. Two hundred and five questionnaires were analyzed with SPSS using descriptive (mean and standard deviation) and inferential (t-test) statistics.
Results: Based on a content analysis of 15 selected articles, the extracted components were structured into 12 items. A preliminary questionnaire was designed with 28 questions across these 12 extracted components. Face validity was assessed using expert opinions, and necessary revisions were made. Both the Content Validity Index (CVI) and Content Validity Ratio (CVR) coefficients were employed for content validity. The final questionnaire comprised 22 items using a five-point Likert scale, ranging from strongly agree (score 5) to strongly disagree (score 1), with an internal consistency of 0.69. Exploratory factor analysis revealed that the questionnaire has six factors: “validity and accuracy”, “technical problems”, “types of questions and announcing the results”, “technique and simplicity”, “motivation and anxiety”, and “speed and error recording” that explain 60.88 percent of the total variance. Correlation results indicated no relationship between students’ attitudes and performance toward electronic examinations and academic achievement (r = 0.055, P-value = 0.432). There was a significant difference between male and female groups, BSc. and MSc. nursing and midwifery students, and semester of study.
Conclusion: The tool for measuring students’ attitudes and performance towards electronic exams has acceptable validity and reliability. This tool can be used to assess online exams and improvement plans.
Materials and Methods: This research is a mixed-method study (qualitative-quantitative). First, all related articles published, between 2000 to 2022, were extracted from ERIC, PubMed, ScienceDirect databases, and the Google Scholar search engine. Then, the attitude and performance questionnaire was designed and validated based on the literature review results. All BSc and MSc. nursing and midwifery students of Sarivar Nassibeh School filled out the questionnaire. Two hundred and five questionnaires were analyzed with SPSS using descriptive (mean and standard deviation) and inferential (t-test) statistics.
Results: Based on a content analysis of 15 selected articles, the extracted components were structured into 12 items. A preliminary questionnaire was designed with 28 questions across these 12 extracted components. Face validity was assessed using expert opinions, and necessary revisions were made. Both the Content Validity Index (CVI) and Content Validity Ratio (CVR) coefficients were employed for content validity. The final questionnaire comprised 22 items using a five-point Likert scale, ranging from strongly agree (score 5) to strongly disagree (score 1), with an internal consistency of 0.69. Exploratory factor analysis revealed that the questionnaire has six factors: “validity and accuracy”, “technical problems”, “types of questions and announcing the results”, “technique and simplicity”, “motivation and anxiety”, and “speed and error recording” that explain 60.88 percent of the total variance. Correlation results indicated no relationship between students’ attitudes and performance toward electronic examinations and academic achievement (r = 0.055, P-value = 0.432). There was a significant difference between male and female groups, BSc. and MSc. nursing and midwifery students, and semester of study.
Conclusion: The tool for measuring students’ attitudes and performance towards electronic exams has acceptable validity and reliability. This tool can be used to assess online exams and improvement plans.
Maryam Jahanbakhsh, Mahnaz Noroozi, Majid Jangi, Fateme Ghadiri Kofrani,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: Education on sexually transmitted diseases and functional disorders in Iranian women’s society are two important issues that should be addressed as aspects of sexual health. The evidence suggests that mobile phone-based applications can be a suitable tool to improve education in the field of sexual health. Therefore, in the current research, the design of the content model of the mobile phone-based application with an emphasis on diseases transmitted through sexual contact and functional disorders of women has been discussed.
Materials and Methods: The present study is applied-descriptive and was conducted in 3 stages as follows: determining the requirements of the application content model, designing it, and evaluating it. First, information needs were identified and extracted through a civilian review and a review of the App Store, Google Play, and Cafe Bazaar application stores. Then, the results were scientifically organized and reviewed and presented in the form of the application content form to a panel of 7 sexual health experts. The content model was reviewed by the experts and designed through UML diagrams and approved by technical specialists.
Results: The findings of the needs assessment phase consisted of compiling the content requirements of the application in the form of 6 areas: 1- sexual attitude and knowledge 2- improving the quality of sexual life 3- sexually transmitted diseases 4- HIV/AIDS 5- genital infections 6- dysfunction disorders and 41 sub-areas were approved by experts. The compiled model was drawn through the diagrams of the application, sequence, business process and state diagrams and was confirmed and developed during the evaluation with activity diagrams and display screens.
Conclusion: Mobile applications, which are not only more accessible than other technologies, but also provide a space for education, free from any shame and worry due to the one-way nature of the communication, are a suitable platform for increasing Iranian women’s attitudes and knowledge about their sexual health. The designed content model can serve as a Persian, scientific, and native prototype for the development and design of an application that can be implemented on mobile phones to educate women’s sexual health.
Results: The findings of the needs assessment phase consisted of compiling the content requirements of the application in the form of 6 areas: 1- sexual attitude and knowledge 2- improving the quality of sexual life 3- sexually transmitted diseases 4- HIV/AIDS 5- genital infections 6- dysfunction disorders and 41 sub-areas were approved by experts. The compiled model was drawn through the diagrams of the application, sequence, business process and state diagrams and was confirmed and developed during the evaluation with activity diagrams and display screens.
Conclusion: Mobile applications, which are not only more accessible than other technologies, but also provide a space for education, free from any shame and worry due to the one-way nature of the communication, are a suitable platform for increasing Iranian women’s attitudes and knowledge about their sexual health. The designed content model can serve as a Persian, scientific, and native prototype for the development and design of an application that can be implemented on mobile phones to educate women’s sexual health.
Elham Shakiba, Mahboobeh Sadat Fadavi, Mohammad Ali Nadi,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: Smart power is the solidarity between science, knowledge and communication. With the advancement of technology, new space has been created in medical sciences. The aim of this research is Explaining the concept of Smart power and identifying its components in Universities of Medical Sciences
Materials and Methods: The current research was done qualitatively in 2023 using the grounded theory method. Deep and semi-structured interview was done with 13 faculty members of Medical Sciences Universities of the country selected through the purposeful and snowball sampling method and reached theoretical saturation. Strauss -Corbin method based on open, axial and selective coding was used for data analysis. To determine the accuracy and validity of the data, after coding the data, the opinions of six interview participants and six university professors who were familiar with the subject and method of the present research were sought.
Results: After the initial concepts were formulated, 77 open codes, 15 core codes, and five selective codes were identified for smart power. Strengthening artificial intelligence, e-health transformation, cross-border activities together constitute health technological responsiveness, are causal factors that affect the phenomenon of smart power. Actions that should be taken for smart power in medical universities, as strategies include technological innovation that is carried out with systematic technology, technological education, and research capacity building. Cyber management with technology-based performance, communication capability enhancement, information management are contextual factors that create special conditions for effective strategies. The conflict between tradition and modernization, information anxiety as organizational risks are intervention factors that interfere with and limit strategies. If the special conditions of strategies are provided and intervention factors are controlled, the outcome of strategies will be technological progress, development of communication network, integration of hard and soft power, and overall comprehensive health.
Conclusion: Using and developing this power, the policy makers of the health system will be able to solve problems such as the non-uniformity of health facilities in different regions, also the use of virtual university and electronic education, which will remove the time and place limitations and provide the opportunity for education to applicants in different parts of the country.
Materials and Methods: The current research was done qualitatively in 2023 using the grounded theory method. Deep and semi-structured interview was done with 13 faculty members of Medical Sciences Universities of the country selected through the purposeful and snowball sampling method and reached theoretical saturation. Strauss -Corbin method based on open, axial and selective coding was used for data analysis. To determine the accuracy and validity of the data, after coding the data, the opinions of six interview participants and six university professors who were familiar with the subject and method of the present research were sought.
Results: After the initial concepts were formulated, 77 open codes, 15 core codes, and five selective codes were identified for smart power. Strengthening artificial intelligence, e-health transformation, cross-border activities together constitute health technological responsiveness, are causal factors that affect the phenomenon of smart power. Actions that should be taken for smart power in medical universities, as strategies include technological innovation that is carried out with systematic technology, technological education, and research capacity building. Cyber management with technology-based performance, communication capability enhancement, information management are contextual factors that create special conditions for effective strategies. The conflict between tradition and modernization, information anxiety as organizational risks are intervention factors that interfere with and limit strategies. If the special conditions of strategies are provided and intervention factors are controlled, the outcome of strategies will be technological progress, development of communication network, integration of hard and soft power, and overall comprehensive health.
Conclusion: Using and developing this power, the policy makers of the health system will be able to solve problems such as the non-uniformity of health facilities in different regions, also the use of virtual university and electronic education, which will remove the time and place limitations and provide the opportunity for education to applicants in different parts of the country.
Mojtaba Salimi Bani, Mehdi Ghassabi Chorsi, Roghayeh Ershad Sarabi,
Volume 18, Issue 6 (2-2025)
Volume 18, Issue 6 (2-2025)
Abstract
Background and Aim: Malaria is one of the health challenges in many countries worldwide. Iran is among the countries that have prioritized a malaria elimination program, aiming to interrupt local transmission of the disease by 2025. Health workeres (community health workers) play an important role in primary healthcare for identifying, controlling, and preventing malaria. Keeping their knowledge and skills up-to-date through continuous training can be effective in the success of this program. Virtual training is a modern educational method that facilitates such training courses. This study aimed to investigate the impact of virtual retraining courses on the knowledge, attitude, and performance of konarak health workers in implementing the malaria elimination program in 2022.
Materials and Methods: This research was a quasi-experimental study with a single-group pre-test and post-test design. The population included 69 individuals who were enrolled using a census method. Initially, a pre-test was conducted to assess the baseline level of knowledge, attitude, and performance of participants regarding malaria elimination strategies. Then, the educational intervention was delivered virtually, followed by a post-test to evaluate the outcomes. The educational content was provided in eight 45-minute sessions by an instructor from the Health worker Training Center using the Sky Room platform. Data collection was performed using a researcher-made questionnaire. Content validity of the questionnaire was confirmed, and its reliability was assessed in a pilot study prior to the training by the responsible expert; the Cronbach’s alpha coefficient of the questionnaire items was calculated at an acceptable level (r=0.83). Data were analyzed using SPSS software and paired t-tests.
Results: Out of 69 participants, 40 (58%) were male and 29 (42%) female. Comparison of pre-test and post-test results showed that the mean scores of knowledge, attitude, and performance of health workeres increased by 1.05, 1.2, and 1.17 units respectively after the training, and these differences were statistically significant (P=0.000).
Conclusion: Based on the results, considering the advantages of virtual training such as easy access, lower cost, and wide coverage, this method is recommended as a strategy for educational programs for healthcare staff.
Materials and Methods: This research was a quasi-experimental study with a single-group pre-test and post-test design. The population included 69 individuals who were enrolled using a census method. Initially, a pre-test was conducted to assess the baseline level of knowledge, attitude, and performance of participants regarding malaria elimination strategies. Then, the educational intervention was delivered virtually, followed by a post-test to evaluate the outcomes. The educational content was provided in eight 45-minute sessions by an instructor from the Health worker Training Center using the Sky Room platform. Data collection was performed using a researcher-made questionnaire. Content validity of the questionnaire was confirmed, and its reliability was assessed in a pilot study prior to the training by the responsible expert; the Cronbach’s alpha coefficient of the questionnaire items was calculated at an acceptable level (r=0.83). Data were analyzed using SPSS software and paired t-tests.
Results: Out of 69 participants, 40 (58%) were male and 29 (42%) female. Comparison of pre-test and post-test results showed that the mean scores of knowledge, attitude, and performance of health workeres increased by 1.05, 1.2, and 1.17 units respectively after the training, and these differences were statistically significant (P=0.000).
Conclusion: Based on the results, considering the advantages of virtual training such as easy access, lower cost, and wide coverage, this method is recommended as a strategy for educational programs for healthcare staff.
Ayoub Mohamadian, Ali Moeini, Mahnaz Sanjari, Zahra Abdullahzade,
Volume 18, Issue 6 (2-2025)
Volume 18, Issue 6 (2-2025)
Abstract
Background and Aim: Smart health, due to its capacity in disease prevention, is a suitable solution for providing osteoporosis fracture prevention services. Also, the existence of close relationships between active organizations for the prevention of this disease requires this area to be examined from the perspective of the ecosystem. Therefore, the purpose of this study is to identify the factors and players of the ecosystem of preventing fractures caused by osteoporosis in smart health.
Materials and Methods: A qualitative systematic review of meta-synthesis was conducted to find resources related to the prevention of osteoporosis-related fractures. For this purpose, scientific databases of Web of Science, Scopus and PubMed were examined and 155 were selected from 10344 sources found. At the end, by using the Shannon entropy method, the categories of each dimension were ranked.
Results: This systematic review demonstrated that the ecosystem for preventing fractures caused by osteoporosis comprises four main categories of factors: lifestyle (nutrition, exercise, fall prevention, cessation of tobacco, alcohol, and caffeine consumption), clinical (screening, diagnosis, and drug therapy), technological (infrastructure, platform, and application), and contextual (cultural, social participation, policy, economic, and education). The application and infrastructure secured the first and second positions in the ranking, while the platform and education collectively ranked third. Ecosystem participants were also categorized into three core layers: the fracture prevention and treatment team members, firms related to fracture prevention and treatment, and other health stakeholders; the extended layer, which includes affected or at-risk individuals, education stakeholders, cultural stakeholders, social stakeholders, and health stakeholders; and the external layer, comprising international organizations and national ministries. In the ranking, affected or at-risk individuals, other health stakeholders, and fracture prevention and treatment team members earned first to third positions, respectively.
Conclusion: The research results showed that “technological”, “contextual”, “lifestyle change” and “clinical” factors are in the first to fourth places, respectively. Also, among the players, the first place was assigned to the extended layer, the main core took the second place, and the external layer took the third place.
Materials and Methods: A qualitative systematic review of meta-synthesis was conducted to find resources related to the prevention of osteoporosis-related fractures. For this purpose, scientific databases of Web of Science, Scopus and PubMed were examined and 155 were selected from 10344 sources found. At the end, by using the Shannon entropy method, the categories of each dimension were ranked.
Results: This systematic review demonstrated that the ecosystem for preventing fractures caused by osteoporosis comprises four main categories of factors: lifestyle (nutrition, exercise, fall prevention, cessation of tobacco, alcohol, and caffeine consumption), clinical (screening, diagnosis, and drug therapy), technological (infrastructure, platform, and application), and contextual (cultural, social participation, policy, economic, and education). The application and infrastructure secured the first and second positions in the ranking, while the platform and education collectively ranked third. Ecosystem participants were also categorized into three core layers: the fracture prevention and treatment team members, firms related to fracture prevention and treatment, and other health stakeholders; the extended layer, which includes affected or at-risk individuals, education stakeholders, cultural stakeholders, social stakeholders, and health stakeholders; and the external layer, comprising international organizations and national ministries. In the ranking, affected or at-risk individuals, other health stakeholders, and fracture prevention and treatment team members earned first to third positions, respectively.
Conclusion: The research results showed that “technological”, “contextual”, “lifestyle change” and “clinical” factors are in the first to fourth places, respectively. Also, among the players, the first place was assigned to the extended layer, the main core took the second place, and the external layer took the third place.
Samin Rezapour, Mohamad Jebraeily, Esmaeil Mehraeen, Haleh Ayatollahi,
Volume 19, Issue 1 (4-2025)
Volume 19, Issue 1 (4-2025)
Abstract
Background and Aim: Breast self-examination is a recommended screening method that can be used by any woman at any age. Smartphone applications can be a low-cost and effective strategy to prevent breast cancer by changing behavior and encouraging women to be aware of their breast health. The purpose of this research is to determine the learning needs and preferences of women in the development of a BSE smartphone application.
Materials and Methods: This descriptive-cross-sectional study was conducted in 2023, The statistical population of the study included 120 women working in the faculties of Urmia University of Medical Sciences, who were selected through stratified proportional sampling. To collect data, a questionnaire was designed, the validity of which was confirmed based on the content validity method and expert opinion. The test-retest method was also used to determine the reliability of the questionnaire, which resulted in a Pearson correlation coefficient of 85%. The rating of each item in the questionnaire was determined based on a five-point Likert scale (1-5). The Statistical analysis of the data was performed using SPSS software.
Results: From the perspective of women, the most important learning needs are related to breast cancer risk factors (4.68), the importance of early detection of breast cancer (4.33), how to perform breast self-examination (4.38), the role of breast self-examination in breast cancer prevention (4.18), ways to detect breast cancer early (4.11), types of physical activities (4.16) and healthy diet (4.08) related to breast cancer prevention. In terms of the technical capabilities of the application, the most important features include multimedia educational content (4.61), self-examination training (4.52), information about warning signs (4.33), self-examination time reminder (4.29) and ease of use (4.20).
Conclusion: The findings of the present study showed that educational content should present risk factors and the role of diet and physical activity in preventing breast cancer, and teach how to accurately perform breast self-examination with multimedia content. Also, the technical capabilities of the smartphone application should be designed in a way that suits women’s learning needs while maintaining the confidentiality of information and the privacy of individuals.
Materials and Methods: This descriptive-cross-sectional study was conducted in 2023, The statistical population of the study included 120 women working in the faculties of Urmia University of Medical Sciences, who were selected through stratified proportional sampling. To collect data, a questionnaire was designed, the validity of which was confirmed based on the content validity method and expert opinion. The test-retest method was also used to determine the reliability of the questionnaire, which resulted in a Pearson correlation coefficient of 85%. The rating of each item in the questionnaire was determined based on a five-point Likert scale (1-5). The Statistical analysis of the data was performed using SPSS software.
Results: From the perspective of women, the most important learning needs are related to breast cancer risk factors (4.68), the importance of early detection of breast cancer (4.33), how to perform breast self-examination (4.38), the role of breast self-examination in breast cancer prevention (4.18), ways to detect breast cancer early (4.11), types of physical activities (4.16) and healthy diet (4.08) related to breast cancer prevention. In terms of the technical capabilities of the application, the most important features include multimedia educational content (4.61), self-examination training (4.52), information about warning signs (4.33), self-examination time reminder (4.29) and ease of use (4.20).
Conclusion: The findings of the present study showed that educational content should present risk factors and the role of diet and physical activity in preventing breast cancer, and teach how to accurately perform breast self-examination with multimedia content. Also, the technical capabilities of the smartphone application should be designed in a way that suits women’s learning needs while maintaining the confidentiality of information and the privacy of individuals.
Mohammad Mehdi Sepehri, Minoo Fathi, Nasrin Taherkhani, Roghaye Khasha,
Volume 19, Issue 1 (4-2025)
Volume 19, Issue 1 (4-2025)
Abstract
Background and Aim: The development of self-management application for gestational Diabetes based on mobile health, can increase the quality of life of pregnant mothers and reduce the cost of health care and treatment. In order to develop such an application, it is necessary to identify the key players of this system and examine the relationships between them. Then a gestational Diabetes self-management network based on mobile health tools can be presented.
Materials and Methods: The study was conducted in four phases. In the first phase, key players and roles were identified through literature review. In the second phase, interviews with experts were conducted to assess the identified players and their roles. The third phase involved identifying the relationships between players and their roles, accomplished by designing and completing questionnaires that explored the existence or absence of connections between them. In the final phase, the most critical roles and players were determined using social network analysis, employing three centrality indices: degree centrality, betweenness centrality, and closeness centrality.
Results: A total of 22 role players and 17 roles were identified. Based on the results, the Ministry of Health, with a degree centrality index of 41.12, was found to be the most influential and powerful role player in this network. The endocrinologist, nutritionist and obstetrician, with degree centrality indices of 38.52, 36.79, and 31.60. were ranked next. This indicates that the acceptance of this network by the medical community plays a critical role. Additionally, all three centrality indices showed that the role of patient education had the highest values, followed by roles such as education for specialists and healthcare staff, supporting patients in self-care behaviors, and ensuring patient safety and privacy, which were identified as the key roles.
Conclusion: This study aimed to identify various aspects of network design and the influential roles impacting the self-management of gestational Diabetes through mobile health. The Ministry of Health and the National Prevention Committee exhibited the most connections with each other in fulfilling their shared roles. Therefore, the Ministry of Health can fully delegate some roles to the National Prevention. Additionally, recognizing key roles underscores the necessity of prioritizing education and resource allocation for these roles.
Materials and Methods: The study was conducted in four phases. In the first phase, key players and roles were identified through literature review. In the second phase, interviews with experts were conducted to assess the identified players and their roles. The third phase involved identifying the relationships between players and their roles, accomplished by designing and completing questionnaires that explored the existence or absence of connections between them. In the final phase, the most critical roles and players were determined using social network analysis, employing three centrality indices: degree centrality, betweenness centrality, and closeness centrality.
Results: A total of 22 role players and 17 roles were identified. Based on the results, the Ministry of Health, with a degree centrality index of 41.12, was found to be the most influential and powerful role player in this network. The endocrinologist, nutritionist and obstetrician, with degree centrality indices of 38.52, 36.79, and 31.60. were ranked next. This indicates that the acceptance of this network by the medical community plays a critical role. Additionally, all three centrality indices showed that the role of patient education had the highest values, followed by roles such as education for specialists and healthcare staff, supporting patients in self-care behaviors, and ensuring patient safety and privacy, which were identified as the key roles.
Conclusion: This study aimed to identify various aspects of network design and the influential roles impacting the self-management of gestational Diabetes through mobile health. The Ministry of Health and the National Prevention Committee exhibited the most connections with each other in fulfilling their shared roles. Therefore, the Ministry of Health can fully delegate some roles to the National Prevention. Additionally, recognizing key roles underscores the necessity of prioritizing education and resource allocation for these roles.
Niloofar Mohammadzadeh, Zohreh Javanmard, Fatemeh Bahador,
Volume 19, Issue 2 (7-2025)
Volume 19, Issue 2 (7-2025)
Abstract
Background and Aim: Today, with the digitalization of many healthcare processes, healthcare organizations strive to implement electronic health records (EHR) as effectively as possible. In this regard, the Meaningful Use (MU) program of EHRs was introduced in the United States. However, due to the existing challenges in this program and in order to accelerate the adoption of EHRs and reduce barriers, the Promoting Interoperability (PI) program was introduced by the Centers for Medicare and Medicaid Services (CMS). This study was conducted with the aim of reviewing the various dimensions of the PI within the EHR roadmap and examining strategies to overcome the obstacles of the MU program.
Materials and Methods: This scoping review was conducted in 2024. To assess the PI program, relevant articles were searched on PubMed, Scopus, and Web of Science databases, as well as electronic documents from CMS, without any time restrictions until March 2024. The searches employed the primary keywords “EHR,” “Meaningful Use,” “Promoting Interoperability,” and their synonyms. Additionally, a manual search was performed using the Google Scholar search engine to ensure comprehensive retrieval of all pertinent literature. Subsequently, articles and documents meeting inclusion criteria were selected, and their main characteristics were extracted.
Results: The review revealed that the PI program introduces substantial changes in EHR program requirements, objectives, and scoring methods. The core objectives of this program include: 1. electronic prescribing, 2. health information exchange, 3. provider-to-patient information transfer, and 4. public health and clinical data exchange. Furthermore, the program emphasizes additional requirements to enhance the quality of implementation, promote better sharing of EHR data, and improve clinical quality.
Conclusion: The Promoting Interoperability program has the potential to enhance patient health outcomes and reduce healthcare costs. Moreover, it is expected to gain increasing significance for hospitals as they adopt innovative healthcare delivery and payment models.
Materials and Methods: This scoping review was conducted in 2024. To assess the PI program, relevant articles were searched on PubMed, Scopus, and Web of Science databases, as well as electronic documents from CMS, without any time restrictions until March 2024. The searches employed the primary keywords “EHR,” “Meaningful Use,” “Promoting Interoperability,” and their synonyms. Additionally, a manual search was performed using the Google Scholar search engine to ensure comprehensive retrieval of all pertinent literature. Subsequently, articles and documents meeting inclusion criteria were selected, and their main characteristics were extracted.
Results: The review revealed that the PI program introduces substantial changes in EHR program requirements, objectives, and scoring methods. The core objectives of this program include: 1. electronic prescribing, 2. health information exchange, 3. provider-to-patient information transfer, and 4. public health and clinical data exchange. Furthermore, the program emphasizes additional requirements to enhance the quality of implementation, promote better sharing of EHR data, and improve clinical quality.
Conclusion: The Promoting Interoperability program has the potential to enhance patient health outcomes and reduce healthcare costs. Moreover, it is expected to gain increasing significance for hospitals as they adopt innovative healthcare delivery and payment models.
Farzin Halabchi, Reza Safdari, Shahrbanoo Pahlevanynejad, Sahba Kazemipour,
Volume 19, Issue 2 (7-2025)
Volume 19, Issue 2 (7-2025)
Abstract
Background and Aim: The World Health Organization defines physical inactivity as engaging in less than 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity physical activity per week for adults, which is recognized as a serious global health challenge with dangerous consequences for public health. Global statistics indicate that this issue is more prominent among women; in Iran, 61.9% of women do not engage in sufficient physical activity. The adoption and expansion of health-related technologies indicate their high potential in supporting self-care. This study aims to identify the necessary data elements for designing a personalized self-care fitness mobile application for women.
Materials and Methods: This descriptive study was conducted in two phases: literature review and data element needs assessment. In the first phase, relevant data elements for creating a personalized self-care fitness application for women were identified through scientific articles in databases and library resources, and a data elements checklist was prepared. In the second phase, based on the checklist, a questionnaire was designed by the researcher. Its validity was confirmed by the research team, and its reliability was calculated with a Cronbach’s alpha coefficient of 91.3%.
Results: The aforementioned questionnaire was provided to 20 physicians from the sports medicine department at Mahdi Clinic, Imam Khomeini Hospital Complex, Tehran, to thoroughly evaluate the proposed data elements in terms of their importance, measurability, and relevance. In total, 49 data elements were identified across seven sections: demographic information, health information, disease information, inappropriate behavioral habits, anthropometric data, reports, and lifestyle. Of these, 4 elements were removed due to incompatibility with the study objectives and low importance scores. Additionally, to facilitate future analyses, the remaining elements were re-categorized into 6 groups.
Conclusion: In this study, the key data elements required for designing and providing exercise programs specifically for women were identified and determined. This process aimed to enhance the level of physical activity and address the specific needs of women, thereby establishing a scientific and precise foundation for developing programs tailored to the physical and psychological characteristics of this group.
Materials and Methods: This descriptive study was conducted in two phases: literature review and data element needs assessment. In the first phase, relevant data elements for creating a personalized self-care fitness application for women were identified through scientific articles in databases and library resources, and a data elements checklist was prepared. In the second phase, based on the checklist, a questionnaire was designed by the researcher. Its validity was confirmed by the research team, and its reliability was calculated with a Cronbach’s alpha coefficient of 91.3%.
Results: The aforementioned questionnaire was provided to 20 physicians from the sports medicine department at Mahdi Clinic, Imam Khomeini Hospital Complex, Tehran, to thoroughly evaluate the proposed data elements in terms of their importance, measurability, and relevance. In total, 49 data elements were identified across seven sections: demographic information, health information, disease information, inappropriate behavioral habits, anthropometric data, reports, and lifestyle. Of these, 4 elements were removed due to incompatibility with the study objectives and low importance scores. Additionally, to facilitate future analyses, the remaining elements were re-categorized into 6 groups.
Conclusion: In this study, the key data elements required for designing and providing exercise programs specifically for women were identified and determined. This process aimed to enhance the level of physical activity and address the specific needs of women, thereby establishing a scientific and precise foundation for developing programs tailored to the physical and psychological characteristics of this group.
Mozhgan Farazmand, Mandana Asgari, Hamid Bouraghi, Taleb Khodaveisi, Ali Mohammadpour, Soheila Saeedi,
Volume 19, Issue 3 (9-2025)
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: Cardiovascular diseases have a very high prevalence globally and are recognized as one of the main causes of mortality worldwide. Artificial intelligence, as a novel technology, has garnered attention in recent years in Iran and other parts of the world for the management of a wide variety of diseases. The present study aimed to systematically review research studies conducted in the field of applying artificial intelligence in cardiovascular diseases.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.
Atefeh Abbasi, Somayeh Nasiri, Sayyed Mostafa Mostafavi, Abbas Habibolahi,
Volume 19, Issue 4 (11-2025)
Volume 19, Issue 4 (11-2025)
Abstract
Background and Aim: Neonatal hypoxic-ischemic encephalopathy (HIE) is a clinical syndrome characterized by impaired brain function resulting from oxygen deprivation and reduced cerebral blood flow. Developing predictive models can serve as valuable tools for physicians in forecasting disease outcomes and facilitating early interventions. The present study was conducted with the aim of constructing a predictive model for neonatal hypoxic-ischemic encephalopathy using data mining algorithms.
Materials and Methods: This applied study was conducted using a descriptive approach. In the first stage, the factors influencing the prediction of neonatal hypoxic-ischemic encephalopathy were identified through expert surveys. In the second stage, data pertaining to 4,000 neonates were collected from the Iman system, available in the database of the Ministry of Health and Medical Education, during the years 2020–2021. Following preprocessing, a dataset comprising 3,962 records with 13 features was extracted. Subsequently, predictive models were developed using algorithms including artificial neural networks, decision tree variants, random forest, support vector machines, logistic regression, and Bayesian networks. Model construction was performed using the Python programming language within the Anaconda environment. Finally, performance evaluation and comparison were carried out using metrics such as accuracy, precision, specificity, F1-score, and the Area Under the Curve (AUC).
Results: The findings of the study revealed that the Area Under the Receiver Operating Characteristic Curve (AUROC) for models developed using logistic regression, artificial neural networks, random forest, Bayesian networks, support vector machines, and decision trees were 86%, 86%, 84%, 82%, 76%, and 74%, respectively. The highest performance was achieved by the logistic regression algorithm, with an accuracy of 81%, sensitivity of 85%, and specificity of 96%. The greatest sensitivity was observed in logistic regression, artificial neural networks, and support vector machines, whereas the naïve Bayesian algorithm demonstrated the lowest performance metrics. In the predictive model for hypoxic-ischemic encephalopathy, the most influential feature was the first-minute Apgar score, while the least influential factor was delivery outside the hospital.
Conclusion: The findings of the present study indicated that the predictive model for neonatal hypoxic-ischemic encephalopathy based on the logistic regression algorithm demonstrated superior performance. It is anticipated that the application of practical data-driven algorithms for neonates with hypoxic-ischemic encephalopathy will play a crucial role in the rapid identification of the condition and the provision of appropriate treatment. Such approaches can enable healthcare professionals to act within the critical window of opportunity, thereby improving the quality of care, preventing disease progression, and reducing the severity of adverse outcomes.
Materials and Methods: This applied study was conducted using a descriptive approach. In the first stage, the factors influencing the prediction of neonatal hypoxic-ischemic encephalopathy were identified through expert surveys. In the second stage, data pertaining to 4,000 neonates were collected from the Iman system, available in the database of the Ministry of Health and Medical Education, during the years 2020–2021. Following preprocessing, a dataset comprising 3,962 records with 13 features was extracted. Subsequently, predictive models were developed using algorithms including artificial neural networks, decision tree variants, random forest, support vector machines, logistic regression, and Bayesian networks. Model construction was performed using the Python programming language within the Anaconda environment. Finally, performance evaluation and comparison were carried out using metrics such as accuracy, precision, specificity, F1-score, and the Area Under the Curve (AUC).
Results: The findings of the study revealed that the Area Under the Receiver Operating Characteristic Curve (AUROC) for models developed using logistic regression, artificial neural networks, random forest, Bayesian networks, support vector machines, and decision trees were 86%, 86%, 84%, 82%, 76%, and 74%, respectively. The highest performance was achieved by the logistic regression algorithm, with an accuracy of 81%, sensitivity of 85%, and specificity of 96%. The greatest sensitivity was observed in logistic regression, artificial neural networks, and support vector machines, whereas the naïve Bayesian algorithm demonstrated the lowest performance metrics. In the predictive model for hypoxic-ischemic encephalopathy, the most influential feature was the first-minute Apgar score, while the least influential factor was delivery outside the hospital.
Conclusion: The findings of the present study indicated that the predictive model for neonatal hypoxic-ischemic encephalopathy based on the logistic regression algorithm demonstrated superior performance. It is anticipated that the application of practical data-driven algorithms for neonates with hypoxic-ischemic encephalopathy will play a crucial role in the rapid identification of the condition and the provision of appropriate treatment. Such approaches can enable healthcare professionals to act within the critical window of opportunity, thereby improving the quality of care, preventing disease progression, and reducing the severity of adverse outcomes.
Abbas Sheikhtaheri, Elaheh Jamshidi, Ali Mohammadi, Vahid Feyzollahi,
Volume 19, Issue 5 (12-2025)
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: Knee ligament rupture is a common knee injury, especially among athletes. Considering the importance of treatment quality in the affected population, there is a crucial need for the collection of high-quality, standardized national-level data. This can be achieved by establishing a Minimum Data Set (MDS). The present study aimed to design a Minimum Data Set for the Knee Ligament Rupture Reconstruction Registry System in Athletes.
Materials and Methods: This applied research was conducted in 2024 using a quantitative method (descriptive-comparative and Delphi technique) across three phases. In the first phase, using a descriptive-comparative approach, the required data elements from the national registry systems of selected countries (Norway, Sweden, Denmark, UK) were extracted and analyzed in comparative tables. In the second phase, the data elements currently recorded for patients undergoing knee ligament rupture reconstruction surgery in Iran were identified using a descriptive data collection form. In the third phase, based on the findings from the first two phases, a preliminary MDS was designed as a questionnaire. Its validity was then assessed over two rounds using the Delphi method by a panel of experts (24 in the first round, 18 in the second). Finally, items that achieved a consensus of 75% or higher were included in the final MDS.
Results: In the review conducted on the registry systems of selected countries, including Norway, Sweden, Denmark, and England, the data elements recorded in these systems were first extracted. Subsequently, in the first phase of the study, the extracted data elements were categorized into two main categories: Administrative and clinical. The findings of this phase were obtained through their comparison in comparative tables. The findings of the second phase of the study consisted of data elements extracted from the medical records of patients who had undergone knee ligament rupture reconstruction surgery in Iran. In the third phase of the study, the final minimum data set for patients undergoing knee ligament rupture reconstruction surgery was developed based on the findings of the first and second phases of the study as well as expert opinions. This data set comprised 78 data elements organized into two sections: administrative (9 data elements) and clinical (69 data elements). In the administrative section, data classes were categorized into demographic, socioeconomic, and visit-related groups. In the clinical section, data classes were categorized into diagnostic, anthropometric, surgical, follow-up, and outcome groups.
Conclusion: The Minimum Data Set for knee ligament rupture reconstruction surgery can play a significant role in collecting high-quality data, evaluating and managing treatment quality and outcomes, and informing planning and policymaking in this field by ensuring the collection of integrated and high-quality data.
Materials and Methods: This applied research was conducted in 2024 using a quantitative method (descriptive-comparative and Delphi technique) across three phases. In the first phase, using a descriptive-comparative approach, the required data elements from the national registry systems of selected countries (Norway, Sweden, Denmark, UK) were extracted and analyzed in comparative tables. In the second phase, the data elements currently recorded for patients undergoing knee ligament rupture reconstruction surgery in Iran were identified using a descriptive data collection form. In the third phase, based on the findings from the first two phases, a preliminary MDS was designed as a questionnaire. Its validity was then assessed over two rounds using the Delphi method by a panel of experts (24 in the first round, 18 in the second). Finally, items that achieved a consensus of 75% or higher were included in the final MDS.
Results: In the review conducted on the registry systems of selected countries, including Norway, Sweden, Denmark, and England, the data elements recorded in these systems were first extracted. Subsequently, in the first phase of the study, the extracted data elements were categorized into two main categories: Administrative and clinical. The findings of this phase were obtained through their comparison in comparative tables. The findings of the second phase of the study consisted of data elements extracted from the medical records of patients who had undergone knee ligament rupture reconstruction surgery in Iran. In the third phase of the study, the final minimum data set for patients undergoing knee ligament rupture reconstruction surgery was developed based on the findings of the first and second phases of the study as well as expert opinions. This data set comprised 78 data elements organized into two sections: administrative (9 data elements) and clinical (69 data elements). In the administrative section, data classes were categorized into demographic, socioeconomic, and visit-related groups. In the clinical section, data classes were categorized into diagnostic, anthropometric, surgical, follow-up, and outcome groups.
Conclusion: The Minimum Data Set for knee ligament rupture reconstruction surgery can play a significant role in collecting high-quality data, evaluating and managing treatment quality and outcomes, and informing planning and policymaking in this field by ensuring the collection of integrated and high-quality data.
Elham Maserat, Zeinab Mohammadzadeh, Zahra Mahmoudvand, Hasan Siamian, Pourya Taghizadeh, Azadeh Yazdanian,
Volume 19, Issue 5 (12-2025)
Volume 19, Issue 5 (12-2025)
Abstract
Background and Aim: As a pandemic, the COVID-19 epidemic has had widespread impacts on society and has highlighted the need for effective management through timely case detection, early isolation, and treatment. Web portals have emerged as an effective information technology intervention and a solution for crisis management. This study aims to review various web portals implemented in the context of COVID-19.
Materials and Methods: In 2025, a systematic review was conducted to identify articles related to the use of web portals in the COVID-19 context. Keywords such as information technology, portal, COVID-19, and university were used to search multiple databases and search engines including Scopus, PubMed, Science Direct, Web of Knowledge, Ovid Medline, and Google Scholar. Published texts from 2019 to 2025 were included in the search.
Results: Initially, 1,058 articles were retrieved, and after careful evaluation, 40 articles directly relevant to the research topic were selected for inclusion. The analysis identified several notable web portals deployed during the COVID-19 pandemic, including platforms such as COVIDome, Over COVID, interactive visualization portals, country-specific information portals, prediction-based systems, electronic portals for specific medical conditions, data platforms, drug repurposing portals, patient triage and scheduling tools, health mapping portals, telemetry capabilities, and epidemiology applications. The results showed that the highest number of related articles were published in 2020, primarily concentrated in the United States, Saudi Arabia, and Canada. In-depth reviews indicated that WPs such as COVIDome and MyChart significantly facilitated patient access to medical information and healthcare services. These portals not only provided timely information regarding vaccination and outbreaks but also played a crucial role in facilitating effective communication between patients and Healthcare Providers. Furthermore, the overall use of portals increased 10-fold during the pandemic, a trend that persisted afterward. Findings also highlight existing digital divides, as individuals with higher education and income levels benefited more from these portals.
Conclusion: Successful implementation of web portals requires proper management and planning, increased awareness among stakeholders including policymakers, healthcare professionals, and the general public, user training, comprehensive data integration, adherence to standards, and periodic evaluations. These measures are essential to optimize the effectiveness and utility of the portals.
Materials and Methods: In 2025, a systematic review was conducted to identify articles related to the use of web portals in the COVID-19 context. Keywords such as information technology, portal, COVID-19, and university were used to search multiple databases and search engines including Scopus, PubMed, Science Direct, Web of Knowledge, Ovid Medline, and Google Scholar. Published texts from 2019 to 2025 were included in the search.
Results: Initially, 1,058 articles were retrieved, and after careful evaluation, 40 articles directly relevant to the research topic were selected for inclusion. The analysis identified several notable web portals deployed during the COVID-19 pandemic, including platforms such as COVIDome, Over COVID, interactive visualization portals, country-specific information portals, prediction-based systems, electronic portals for specific medical conditions, data platforms, drug repurposing portals, patient triage and scheduling tools, health mapping portals, telemetry capabilities, and epidemiology applications. The results showed that the highest number of related articles were published in 2020, primarily concentrated in the United States, Saudi Arabia, and Canada. In-depth reviews indicated that WPs such as COVIDome and MyChart significantly facilitated patient access to medical information and healthcare services. These portals not only provided timely information regarding vaccination and outbreaks but also played a crucial role in facilitating effective communication between patients and Healthcare Providers. Furthermore, the overall use of portals increased 10-fold during the pandemic, a trend that persisted afterward. Findings also highlight existing digital divides, as individuals with higher education and income levels benefited more from these portals.
Conclusion: Successful implementation of web portals requires proper management and planning, increased awareness among stakeholders including policymakers, healthcare professionals, and the general public, user training, comprehensive data integration, adherence to standards, and periodic evaluations. These measures are essential to optimize the effectiveness and utility of the portals.
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