Effect of a gamified mobile-based self-management application on disease activity index, quality of life, and mental health in adults with inflammatory bowel disease: A protocol of a randomized controlled trial study.

Background and Aims: Inflammatory bowel disease (IBD) is a chronic inflammatory gastrointestinal tract disease subdivided into Crohn's disease (CD) and ulcerative colitis (UC). There is currently no cure for IBD, and individuals with IBD frequently experience a lower health-related quality of life (HRQOL) than the general population. Gamification has become an increasingly popular topic in recent years. Adapting game design concepts to nongaming contexts represents a novel and potential approach to changing user engagement. This study will be conducted with the aim of evaluating the effect of a gamified mobile-based self-management application on disease activity index, quality of life, and mental health in adults with IBD. Methods: A multicenter, parallel, two-arm, exploratory randomized controlled trial with a 6-month follow-up per patient will be designed to compare the impact of the gamified mobile-based tele-management system on primary and secondary health outcomes and outpatient visits in 210 patients with all types of IBD which are divided equally into a control group with standard care and an intervention group which will use the developed mobile application named MY IBD BUDDY. All patients will attend study visits at baseline, 12 and 24 weeks, and routine IBD clinic visits or telephone consultations based on randomization group assignment. Disease activity or disease activity index, mental health (anxiety and depression) symptoms, quality of life, self-efficacy, and IBD-specific knowledge will be measured at baseline with two follow-ups at 12 and 24 weeks. Conclusions: In sum, the outcomes of our trial will demonstrate the impact of the gamified mobile-based self-management system on disease activity, quality of life, and anxiety and depression by means of interactive care and patient empowerment. Trial Registration: IRCT: IRCT20200613047757N1. Registered November 16, 2021. Prospectively registered and visible at OSF (https://doi.org/10.17605/OSF.IO/AWFY9).

An inversion-based clustering approach for complex clusters.

BACKGROUND: The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. METHODS: In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. RESULTS: The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. CONCLUSIONS: The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Inflammatory bowel disease patients' perspectives of non-medical needs.

BACKGROUND: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). METHODS: IBD patients were recruited from different centers. Considering inclusion and exclusion criteria, 521 participants were filled a predefined questionnaire. This questionnaire was prepared through literature review of the recent well-known guidelines on the needs of IBD patients, which was further approved by the experts of IBD area in three rounds of Delphi consensus. It includes 56 items in four sections of informational needs (25), supportive needs (15), sources of information (7), and methods of obtaining information (9). RESULTS: In particular, EFA was used to apply data reduction and structure detection. Given that this study tries to identify patterns, structures as well as inter-relationships and classification of the variables, EFA was utilized to simplify presentation of the variables in a way that large amounts of observations transform into fewer ones. Accordingly, the EFA identified five factors out of 25 items in the information needs section, three factors out of 15 items in the supportive needs section, two factors out of 7 items in the information sources section, and two factors out of 9 items in the information presentation methods. Through the CFA, all 4 models were supported by Root Mean Squared Error of Approximation (RMSEA); Incremental Fit Index (IFI); Comparative Fit Index (CFI); Tucker-Lewis Index (TLI); and SRMR. These values were within acceptable ranges, indicating that the twelve factors achieved from EFA were validated. CONCLUSIONS: This study introduced a reliable 12-factor model as an efficient tool to comprehensively identify preferences of IBD patients in informational and supportive needs along with sources and methods of obtaining information. An in-depth understanding of the needs of IBD patients facilitates informing and supporting health service provision. It also assists patients in a fundamental way to improve adaptation and increase the quality of life. We suggest that health care providers consider the use of this tool in clinical settings in order to precisely assess its efficacy.

A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.

BACKGROUND: The pharmaceutical industry is continually striving to innovate drug development and formulation processes. Orally disintegrating tablets (ODTs) have gained popularity due to their quick release and patient-friendly characteristics. The choice of excipients in tablet formulations plays a critical role in ensuring product quality, highlighting its importance in tablet creation. The traditional trial-and-error approach to this process is both expensive and time-intensive. To tackle these obstacles, we introduce a fresh approach leveraging machine learning and deep learning methods to automate and enhance pre-formulation drug design. METHODS: We collected a comprehensive dataset of 1983 formulations, including excipient names, quantities, active ingredient details, and various physicochemical attributes. Our study focused on predicting two critical control test parameters: tablet disintegration time and hardness. We compared a range of models like deep learning, artificial neural networks, support vector machines, decision trees, multiple linear regression, and random forests. RESULTS: A 12-layer deep neural network, as a form of deep learning, surpassed alternative techniques by achieving 73% accuracy for disintegration time and 99% for tablet hardness. This success underscores its efficacy in predicting complex pharmaceutical factors. Such an approach streamlines the drug formulation process, reducing iterations and material consumption. CONCLUSIONS: Our findings highlight the deep learning potential in pharmaceutical formulations, particularly for tablet hardness prediction. Future work should focus on enlarging the dataset to improve model effectiveness and extend its application in pharmaceutical product development and assessment.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data.

BACKGROUND: Controlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini-Hochberg (BH) and Benjamini-Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates. RESULTS: When there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures. CONCLUSION: The modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

Prevalence of obsessive-compulsive symptoms and their psychosocial correlates among medical students during COVID-19 pandemic.

This study aimed to assess the prevalence of obsessive-compulsive symptoms (OCS) among medical students during COVID-19 pandemic and to evaluate their association with related sociodemographic features and other psychological symptoms. In this cross-sectional study, students from Mashhad University of Medical Sciences with no major exam in the preceding or following month were surveyed during April to August 2021 through stratified available sampling. Data were collected by a structured online questionnaire distributed through social media platforms. OCS were assessed using Obsessive-Compulsive Inventory-Revised (OCI-R) and COVID-related stress was evaluated using COVID Stress Scale (CSS). Overall, 347 students with a mean age of 22.67 ±â€…2.56 years were included in this study, of whom 30.3% had probable obsessive-compulsive disorder (OCD; OCI-R score ≥21). Mean CSS scores in students with and without probable OCD were 38.64 ±â€…19.82 and 26.72 ±â€…16.63, respectively ( P  < 0.005). Total CSS score was significantly correlated with OCI-R score ( r  = 0.38, P  = 0.001). Around one-third of the medical students reported significant OCS during COVID-19 pandemic, which was associated with higher COVID-19-related stress. Further research provides insight into management of OCD and related disorders during the COVID-19 pandemic.

Development and Validation of an Educational Book on Self-Management in Inflammatory Bowel Disease Based on Patient Preferences and Expert Opinions: A Methodological Study.

BACKGROUND: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient preferences and expert opinions. RESEARCH DESIGN AND METHODS: The method of this study includes two main phases of development and validation in five steps in the following order: (1) identification of information needs for patients with IBD; (2) content development with a comprehensive literature review and scientific texts related to IBD; (3) measuring the face validity of the content based on the expert opinions in the field of IBD; (4) validation of the content with the experts in the field of IBD; and (5) validation by target audiences. RESULTS: The expert panel comprises ten gastroenterologists, nutritionists, psychologists, gynecologists, and nurses. The total suitability score is 79.5%. The final draft version of the educational self-management material was presented to 30 IBD patients who were satisfied (n = 24; 80%) with the material. CONCLUSIONS: This study shows the development process and is validated for face and content validity by the academic multidisciplinary expert panel and target group. Patients and their caregivers can use this content to cope with their disease.

Application of distributed lag models and spatial analysis for comparing the performance of the COVID-19 control decisions in European countries.

Over the past three years, the COVID-19 outbreak has become a major worldwide problem, affecting the health systems and economies of countries. The mean delays, the expected time to observe the average effect of the number of new cases on the number of deaths, are gold times for decision-making regarding disease control and treatment facilities to reduce the fatality rate. The interest of the present study is estimating the mean delays and adjusted fatality rates of COVID-19 with the new application of Distributed Lag Models (DLM) and their spatial distributions. The daily cases and deaths data of COVID-19 for 39 European countries was obtained from two sources; the "European Centre for Disease Prevention and Control" and the "Our World in Data" database. The mean delay and the Adjusted Fatality Rate (AFR) for each country at three-time intervals; the first and subsequent peaks before and after vaccination were estimated by the Distributed Lag Models. The spatial analysis was applied to find the spatial correlation of the mean delays and adjusted fatality rates among European countries. In the three-time intervals, the first and the subsequent peaks before vaccination, and after vaccination, the median and interquartile range of the mean delays; and AFRs were: 1.1 (0.4, 3.2); 0.024 (0.016, 0.044), 9.2 (6.2, 12.40); 0.013 (0.005, 0.020) and 7.3 (4.4, 11.0); 0.001 (0.001, 0.005), respectively. In the subsequent peaks before vaccination, the mean delays considerably increased, and the AFRs decreased for most European countries. After vaccination, the AFRs decreased considerably. Except for the first peak, the spatial correlations of AFRs were not significant among neighboring countries. Consecutive outcomes will occur with delays in outbreaks of infectious disease. Also, the fatality rates for these outcomes should be adjusted on delays. Estimating the mean delays and adjusted fatality rates by Distributed lag Models and the spatial distributions of theme in outbreaks showed that prevention and medical policies after the first peak as well as vaccination were effective to reduce the fatality rate of COVID-19, but these effects were different between countries. These results recommended policymakers and governments assign prevention and medical resources more effectively.

Expert opinions on informational and supportive needs and sources of obtaining information in patients with inflammatory bowel disease: a Delphi consensus study.

Background: The present study introduces informational and supportive needs and sources of obtaining information in patients with inflammatory bowel disease (IBD) through a three-round Expert Delphi Consensus Opinions method. Methods: According to our previous scoping review, important items in the area of informational and supportive needs and sources of obtaining information were elucidated. After omitting duplicates, 56 items in informational needs, 36 items in supportive needs, and 36 items in sources of obtaining information were retrieved. Both open- and close-ended questions were designed for each category in the form of three questionnaires. The questionnaires were sent to selected experts from different specialties. Experts responded to the questions in the first round. Based on the feedback, questions were modified and sent back to the experts in the second round. This procedure was repeated up to the third round. Results: In the first round, five items from informational needs, one item from supportive needs, and seven items from sources of obtaining information were identified as unimportant and omitted. Moreover, two extra items were proposed by the experts, which were added to the informational needs category. In the second round, seven, three, and seven items from informational needs, supportive needs, and sources of obtaining information were omitted due to the items being unimportant. In the third round, all the included items gained scores equal to or greater than the average and were identified as important. Kendall coordination coefficient W was calculated to be 0.344 for information needs, 0.330 for supportive needs, and 0.325 for sources of obtaining information, indicating a fair level of agreement between experts. Conclusions: Out of 128 items in the first round, the omission of 30 items and the addition of two items generated a 100-item questionnaire for three sections of informational needs, supportive needs, and sources of obtaining information with a high level of convergence between experts' viewpoints.

Breast cancer prediction using different machine learning methods applying multi factors.

OBJECTIVE: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors. METHODS: The population sample contained 810 records (115 cancer patients and 695 healthy individuals). 45 attributes out of 85 were selected based on the opinion of experts. These selected attributes are in genetic, biochemical, biomarker, gender, demographic and pathological factors. 13 Machine learning models were trained with proposed attributes and coefficient of attributes and internal relationships were calculated. RESULT: Compared to other methods random forest (RF) has higher performance (accuracy 99.26%, precision 99%, and area under the curve (AUC) 99%). The results of assessing the impact and correlation of variables using the RF method based on PCA indicated that pathology, biomarker, biochemistry, gene, and demographic factors with a coefficient of 0.35, 0.23, 0.15, 0.14, and 0.13 respectively, affected the risk of BC (r2 = 0.54). CONCLUSION: Breast cancer has several risk factors. Medical experts use these risk factors for early diagnosis. Therefore, identifying related risk factors and their effect can increase the accuracy of diagnosis. Considering the broad features for predicting breast cancer leads to the development of a comprehensive prediction model. In this study, using RF technique a breast cancer prediction model with 99.3% accuracy was developed based on multifactorial features.

The effect of propolis supplementation on cardiovascular risk factors in women with rheumatoid arthritis: A double-blind, placebo, controlled randomized clinical trial.

Propolis has gained popularity in recent years because of its beneficial properties, which make it a possible preventative and therapeutic agent as well as a valuable food and cosmetic ingredient. The objective of this study was to evaluate the effects of propolis supplementation on cardiovascular risk factors in women with rheumatoid arthritis. This randomized, double-blind, placebo-controlled clinical trial was performed among 48 patients diagnosed with rheumatoid arthritis. Subjects were randomly assigned to placebo and intervention groups, supplemented with 1000 mg/day of propolis for 12 weeks. Cardiovascular risk factors including, high-sensitivity C-reactive protein (hs-CRP), monocyte chemoattractant protein (MCP-1), Nitric oxide, blood pressure, and lipid profile were assessed pre-and post-intervention. The atherogenic index of plasma value, as well as total cholesterol/high-density lipoprotein cholesterol (HDL-C), triglyceride/HDL-C, and non-HDL-C/HDL-C ratios, were significantly reduced in the intervention group, compared with the placebo group post-intervention (p < 0.05). Moreover, there was a significant reduction in the serum level of hs-CRP in the intervention group when compared with the placebo group (p = 0.001). Furthermore, propolis supplementation could marginally reduce MCP-1 (p = 0.051). These data indicate that propolis supplementation may be a promising treatment strategy for cardiovascular complications among rheumatoid arthritis patients.

Comparing morphologic features and complications of main clear corneal incision between junior and senior residents observed using anterior segment optical coherence tomography.

Background: Wound construction is a critical step in phacoemulsification. Using anterior segment optical coherence tomography (AS-OCT), we compared the morphological features and complications of main incisions made by junior or senior residents during phacoemulsification. Methods: This cross-sectional comparative study included eyes with senile cataracts that underwent uneventful phacoemulsification with a clear corneal incision made by seven senior and eight junior ophthalmology residents. All eyes underwent postoperative image acquisition using AS-OCT on day one and at three months, examining for morphological features and potential complications of the main incision. Results: We included 50 eyes of 50 patients with a male-to-female ratio of 22 (44%) to 28 (56%); 26 (52%) were operated on by junior residents and 24 (48%) by seniors. The mean geometric features of the main incisions and the frequency of early and late wound complications were comparable between the two groups (all P > 0.05). A significant correlation was found between the incision length and angle with the superior (r = + 0.80; P < 0.001 and r = - 0.63; P < 0.001, respectively) and inferior (r = + 0.84; P < 0.001 and r = - 0.68; P < 0.001, respectively) areas of the incision, as well as between the length and angle of incision (r = - 0.74; P < 0.001). The number of planes in the wound architecture was not significantly different according to senior or junior resident status (P > 0.05). Although the number of eyes with stromal hydration was significantly greater for junior residents than for seniors (P < 0.001), the corneal thickness at the entrance to the cornea or the anterior chamber, presence of endothelial wound gaping, and Descemet's membrane detachment were comparable between eyes with and without stromal hydration (all P > 0.05). At three months, 29 (58%) patients returned for examination, in whom seven (24%) had late wound complications. Conclusions: This study found no significant differences in the performances of junior and senior residents in terms of wound construction or its associated complications. However, considering the overall rate of some observed wound-related complications, we recommended revision of the resident educational curriculum concerning the structure and complications of the main incision.

Dataset development of pre-formulation tests on fast disintegrating tablets (FDT): data aggregation.

OBJECTIVES: Tablet manufacturing development is costly, laborious, and time-consuming. Technologies related to artificial intelligence like ,predictive model ,can be used in the control process to facilitate and accelerate the tablet manufacturing process. predictive models have become popular recently. However, predictive models need a comprehensive dataset of related data in the field, due to the lack of a dataset of tablet formulations, the aim of this study is to aggregate and integrate fast disintegration tablet's formulation into a comprehensive dataset. DATA DESCRIPTION: The search strategy has been prepared between the years of 2010 to 2020, consisting of the keyword's 'formulation' ,'disintegrating' and 'Tablet', as well as their synonyms. By searching four databases, 1503 articles were retrieved, from these articles only 232 articles met all of the study's criteria. By reviewing 232 articles, 1982 formulations have been extracted, afterward pre-processing and cleaning data, contain steps of unifying the name and units, removing inappropriate formulations by an expert, and finally, data tidying was done on data. The developed dataset contains valuable information from various FDT's formulations, which can be used in pharmaceutical studies that are critical to the discovery and development of new drugs. this method can be applied to aggregate datasets from the other dosage forms.

Burnout syndrome and related factors among health team employees.

BACKGROUND: Burnout is not only related to mental health but also to efficiency. Thus, recognizing effective coping strategies has a significant role in improving mental health, the efficiency and productivity of human resources, and making better the level of quality of service. OBJECTIVE: To determine burnout syndrome and examine related factors among the employees of Mashhad University of Medical Sciences. METHOD: This cross-sectional study was conducted among 600 employees at Mashhad University of Medical Sciences. They were selected by a stratified sampling method. The data collection tool was the demographic information and the Burnout Self-Test Maslach Burnout Inventory (MBI) questionnaire. Data were analyzed through SPSS software version 20, using descriptive statistics and independent samples t-tests, one-way ANOVA, and Pearson and Spearman regression. RESULTS: The findings showed that emotional exhaustion (EE) and depersonalization (DP) in the majority of employees were high and personal accomplishment (PA) was low at 88.33% of cases. All participants presented burnout. However, participants aged 35-40 years, those with professional and Ph.D. degrees, and research staff reported higher burnout levels. CONCLUSION: Job burnout and its subscale levels among the employees were high. Job burnout is associated with socioeconomic status that can be affected by individual, organizational, management, and environmental factors. Therefore, this study suggests that employees need to get out of EE and DP conditions for higher job performance. Additionally, further research is required to examine the long-term effects of workplace burnout.

The Responsiveness of Three Persian Outcome Measures Following Physiotherapy Intervention in Patients with Chronic Non-Specific Neck Pain.

Objectives: A prospective cohort study to evaluate and compare the responsiveness of the Persian version of the neck disability index (NDI), neck pain & disability scale (NPDS), neck outcome score (NOOS), and to determine the minimal clinically important difference (MCID) and minimal detectable change (MDC). To date, no studies have made a direct comparison between the responsiveness of the Persian version of NPDS, NDI, and NOOS questionnaires. Methods: At the end of the study, 55 patients with chronic non-specific neck pain completed the NPDS, NDI, and NOOS questionnaires at the beginning and end of three weeks of physiotherapy treatment. Additionally, patients completed the global rating of change scale to differentiate between improved and unimproved patients. Comparison of responsiveness was performed using anchor-based methods (receiver operating characteristic (ROC) curve and correlation analysis). MCID and MDC were assessed to investigate relevant changes for each questionnaire. Results: ROC curves analysis showed areas under the curves of 0.70, 0.64, and 0.43 to 0.63 for the NPDS, NDI, and NOOS subscales, respectively. The correlation coefficients between the global rating of the change scale and the change scores of the NPDS and NDI were 0.38 (P<0.01) and 0.30 (P<0.05), respectively. There were no significant correlations between NOOS subscales and global rating of change score (r=0.001- 0.21, P>0.05). The MCID for the NPDS, NDI, and NOOS subscales were 28.09 (score 0-100), 7.5 (score 0-50), and 13.75 to 28.64 (score 0-100), respectively. The MDCs were found to be in the following order: 47.1 points for NPDS, 36.1 for NDI, and 23.5 to 39.7 for NOOS subscales. Conclusion: The Persian NPDS seems more responsive than the NDI and NOOS questionnaires. The level of clinically meaningful change in NDI, NPDS, and NOOS questionnaires is in the range of measurement error.

Impact assessment of e-trainings in occupational safety and health: a literature review.

BACKGROUND: Implementing workplace preventive interventions reduces occupational accidents and injuries, as well as the negative consequences of those accidents and injuries. Online occupational safety and health training is one of the most effective preventive interventions. This study aims to present current knowledge on e-training interventions, make recommendations on the flexibility, accessibility, and cost-effectiveness of online training, and identify research gaps and obstacles. METHOD: All studies that addressed occupational safety and health e-training interventions designed to address worker injuries, accidents, and diseases were chosen from PubMed and Scopus until 2021. Two independent reviewers conducted the screening process for titles, abstracts, and full texts, and disagreements on the inclusion or exclusion of an article were resolved by consensus and, if necessary, by a third reviewer. The included articles were analyzed and synthesized using the constant comparative analysis method. RESULT: The search identified 7,497 articles and 7,325 unique records. Following the title, abstract, and full-text screening, 25 studies met the review criteria. Of the 25 studies, 23 were conducted in developed and two in developing countries. The interventions were carried out on either the mobile platform, the website platform, or both. The study designs and the number of outcomes of the interventions varied significantly (multi-outcomes vs. single-outcome). Obesity, hypertension, neck/shoulder pain, office ergonomics issues, sedentary behaviors, heart disease, physical inactivity, dairy farm injuries, nutrition, respiratory problems, and diabetes were all addressed in the articles. CONCLUSION: According to the findings of this literature study, e-trainings can significantly improve occupational safety and health. E-training is adaptable, affordable, and can increase workers' knowledge and abilities, resulting in fewer workplace injuries and accidents. Furthermore, e-training platforms can assist businesses in tracking employee development and ensuring that training needs are completed. Overall, this analysis reveals that e-training has enormous promise in the field of occupational safety and health for both businesses and employees.

Identification of the informational and supportive needs of patients diagnosed with inflammatory bowel disease: a scoping review.

Background: Inflammatory Bowel Disease (IBD) affects the quality of life. Patient education and support needs are crucial components of comprehensive chronic illness care. The main purposes of this review were to (i) explore the informational and supportive needs of these patients to improve the quality of life in the existing literature and (ii) identify the gaps related to the needs of the patients in articles. Methods: The scoping review is based on the Daudt methodological framework, a modified version of Arksey and O'Malley. Electronic databases were extensively searched from January 01, 2000 to April 30, 2022. Four electronic databases (PubMed/Medline, CINAHL, APA PsycInfo, Psychology and Behavioral Sciences Collection, APA PsycArticles, and ProQuest) were searched using controlled vocabulary, and specific keywords. The searched terms were matched to each database. We manually searched two key journals, namely the Journal of Inflammatory Bowel Disease and the Journal of Crohn's and Colitis. Results: In the review, 75 studies on the assessment of the information and support needs of patients with IBD were reviewed. In this regard, 62 and 53 studies were regarding information needs and support needs, respectively. Most of the information needs of patients with IBD reported in the studies were related to diet needs, and educational needs were the most essential support needs. Conclusions: Health policymakers and managers can develop care and educational programs related to this disease in health centers according to the needs of the patients. Health professionals, especially gastroenterologists, are the primary referral sources for information on patients. Therefore, gastroenterologists can take the lead in planning and educating the patients and sharing their decisions. Systematic review registration: OSF, https://doi.org/10.17605/OSF.IO/3MWGJ.

Effect of a Text Message-Based Support Program on Outcomes of Patients After Flexor Tendon Injury Repair.

PURPOSE: Although the effectiveness of using text messages in home-based rehabilitation programs has been investigated, its ability to engage patients in home rehabilitation exercises and, as a result, improve hand outcomes, specifically in patients with flexor tendon injuries, has not been evaluated. The aim of this study was to determine whether the addition of a text message-based intervention to usual care is effective in improving hand outcomes in patients with flexor tendon injuries after repair. METHODS: In this 2-arm parallel randomized controlled trial, 40 patients were randomly assigned to either the intervention group (usual care plus the support program) or the control group (usual care only). Intervention included an automated package of instructional text messages containing links to a secure website for instructional rehabilitation videos delivered over 12 weeks. The Quick Disabilities of the Arm, Shoulder, and Hand and visual analog scale for pain scores were assessed at 6 and 12 weeks. Physician-reported grip strength and total active motion were assessed after 12 weeks. RESULTS: The study was completed by 90% (36 of 40) of the patients who were enrolled. There were statistically significant differences between the 2 groups with respect to Quick Disabilities of the Arm, Shoulder, and Hand and visual analog scale scores at the 6-week and 12-week assessments. In addition, there were statistically significant differences between the 2 groups with respect to total active motion and grip strength at 12 weeks. Finally, a high level of satisfaction with the intervention was reported. CONCLUSIONS: The text message-based program was associated with improved outcomes over the first 12 weeks after flexor tendon repair. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic II.

Predicting the Physician's Specialty Using a Medical Prescription Database.

Purpose: The present study is aimed at predicting the physician's specialty based on the most frequent two medications prescribed simultaneously. The results of this study could be utilized in the imputation of the missing data in similar databases. Patients and Methods. The research is done through the KAy-means for MIxed LArge datasets (KAMILA) clustering and random forest (RF) model. The data used in the study were retrieved from outpatients' prescriptions in the second populous province of Iran (Khorasan Razavi) from April 2015 to March 2017. Results: The main findings of the study represent the importance of each combination in predicting the specialty. The final results showed that the combination of amoxicillin-metronidazole has the highest importance in making an accurate prediction. The findings are provided in a user-friendly R-shiny web application, which can be applied to any medical prescription database. Conclusion: Nowadays, a huge amount of data is produced in the field of medical prescriptions, which a significant section of that is missing in the specialty. Thus, imputing the missing variables can lead to valuable results for planning a medication with higher quality, improving healthcare quality, and decreasing expenses.