Background Many elderly people suffer from chronic health conditions and mobility limitations. Therefore, they may benefit from traditional rehabilitation or telerehabilitation interventions as an alternative for this type of services. Objective The purpose of this study was to compare the effectiveness of telerehabilitation interventions with traditional rehabilitation services for therapeutic purposes in the elderly. Methods This systematic review was conducted in 2018. The searched databases were Cochrane Library, PubMed, Scopus, Web of Science, Embase, and ProQuest. The search was conducted with no time or language limitation. The selected papers included the randomized clinical trial studies in which elderly people aged 60 and over used telerehabilitation services for treatment purposes. The quality of the studies was evaluated by using the physiotherapy evidence database (PEDro) scale. Data were extracted by using a data extraction form and findings were narratively synthesized. Results After screening the retrieved papers, eight articles were selected to be included in the study. According to the findings, telerehabilitation was used for the elderly after stroke, chronic obstructive pulmonary disease (COPD), total knee replacement, and in patients with the comorbidity of COPD and chronic heart failure. Overall, in most studies, there was no significant difference between the intervention and control groups and the level of improvements was similar for most outcomes. Conclusion Telerehabilitation services can be regarded as an alternative to traditional rehabilitation approaches to reduce outpatient resource utilization and improve quality of life. However, more rigorous studies are suggested to investigate the effectiveness of telerehabilitation services for specific diseases or health conditions.

Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data mining algorithms could be useful in predicting coronary artery diseases. Therefore, the present study aimed to compare the positive predictive value (PPV) of CAD using artificial neural network (ANN) and SVM algorithms and their distinction in terms of predicting CAD in the selected hospitals. The present study was conducted by using data mining techniques. The research sample was the medical records of the patients with coronary artery disease who were hospitalized in three hospitals affiliated to AJA University of Medical Sciences between March 2016 and March 2017 (n = 1324). The dataset and the predicting variables used in this study was the same for both data mining techniques. Totally, 25 variables affecting CAD were selected and related data were extracted. After normalizing and cleaning the data, they were entered into SPSS (V23.0) and Excel 2013. Then, R 3.3.2 was used for statistical computing. The SVM model had lower MAPE (112.03), higher Hosmer-Lemeshow test’s result (16.71), and higher sensitivity (92.23). Moreover, variables affecting CAD (74.42) yielded better goodness of fit in SVM model and provided more accurate result than the ANN model. On the other hand, since the area under the receiver operating characteristic (ROC) curve in the SVM algorithm was more than this area in ANN model, it could be concluded that SVM model had higher accuracy than the ANN model. According to the results, the SVM algorithm presented higher accuracy and better performance than the ANN model and was characterized with higher power and sensitivity. Overall, it provided a better classification for the prediction of CAD. The use of other data mining algorithms are suggested to improve the positive predictive value of the disease prediction.

Extended reality (XR) technologies which include virtual, augmented, and mixed reality have significant potential in surgical training, because they can help to eliminate the limitations of traditional methods. This umbrella review aimed to investigate factors that influence the acceptance and use of XR in surgical training using the unified theory of acceptance and use of technology (UTAUT) model. An umbrella review was conducted in 2024 by searching various databases until the end of 2023. Studies were selected based on the predefined eligibility criteria and analyzed using the components of the UTAUT model. The quality and risk of bias of the selected studies were assessed, and the findings were reported descriptively. A total of 44 articles were included in this study. In most studies, XR technologies were used for surgical training of orthopedics, neurology, and laparoscopy. Based on the UTAUT model, the findings indicated that XR technologies improved surgical skills and procedural accuracy while simultaneously reducing risks and operating room time (performance expectancy). In terms of effort expectancy, user-friendly systems were accessible for the trainees with various levels of expertise. From a social influence standpoint, XR technologies enhanced learning by providing positive feedback from experienced surgeons during surgical training. In addition, facilitating conditions emphasized the importance of resource availability and addressing technical and financial limitations to maximize the effectiveness of XR technologies in surgical training. XR technologies significantly improve surgical training by increasing skills and procedural accuracy. Although adoption is facilitated by designing user-friendly interfaces and positive social influences, financial and resource challenges must be overcome, too. The successful integration of XR into surgical training necessitates careful curriculum design and resource allocation. Future research should focus on overcoming these barriers, so that XR can fully realize its potential in surgical training.

Although the potential benefits of computer-based records have been identified in different areas of the healthcare environment, in many settings paper-based records and computer-based records are still used in parallel. In this article, emergency department (ED) staff perspectives about the use of paper- or computer-based records are presented. This was a qualitative study in which data were collected using in-depth semi-structured interviews with the ED staff. The interviews were transcribed verbatim and data were analysed using framework analysis. In total, 34 interviews were undertaken. The study identified a number of factors which might encourage or discourage the use of paper-based and computer-based records in the ED. Users also expressed their concerns and expectations. Although there is a tendency towards computerizing healthcare settings, user acceptance of technology should not be underestimated. To improve user acceptance, users’ concerns should be investigated and addressed appropriately.