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Wearable smart sensors are emerging technology for daily monitoring of vital signs with the reducing discomfort and interference with normal human activities. The main objective of this study was to review the applied wearable smart sensors for disease control and vital signs monitoring in epidemics outbreaks. A comprehensive search was conducted in Web of Science, Scopus, IEEE Library, PubMed and Google Scholar databases to identify relevant studies published until June 2, 2020. Main extracted specifications for each paper are publication details, type of sensor, disease, type of monitored vital sign, function and usage. Of 277 articles, 11 studies were eligible for criteria. 36% of papers were published in 2020. Articles were published in 10 different journals and only in the Journal of Medical Systems more than one article was published. Most sensors were used to monitor body temperature, heart rate and blood pressure. Wearable devices (like a helmet, watch, or cuff) and body area network sensors were popular types which can be used monitoring vital signs for epidemic trending. 65% of total papers (n = 6) were conducted by the USA, Malaysia and India. Applying appropriate technological solutions could improve control and management of epidemic disease as well as the application of sensors for continuous monitoring of vital signs. However, further studies are needed to investigate the real effects of these sensors and their effectiveness.

Low back pain is a global health problem that is a major cause of disability in developing and developed countries. Machine learning and data mining algorithms can be used to help diagnose this disease. This study aimed to determine the performance of different machine learning algorithms. Nine machine learning techniques, including support vector machine, decision tree, Naive Bayes, K-nearest neighbours, neural network, random forest, deep learning, auto-MLP, and rule induction, were used to modelling. This study revealed that the highest accuracy was related to the random forest (83.55%) and support vector machine (82.26%) classifiers. As a result, machine learning algorithms have good accuracy in low back pain diagnosis.

Collecting data related to COVID-19 disease can significantly impact the management of this pandemic. One of the tools that can be helpful in controlling this disease is developing registry. This study aimed to determine the technical and data requirements of the registry for COVID-19. Databases with keywords related to 'COVID-19' and 'registry' were searched. The existing registries in COVID-19 in Iran and other countries were examined. To finalise the data elements, focus group sessions were held with experts. Thirteen main data classes were identified: demographic information, critical dates, type of sample, patient condition, signs and symptoms, history of smoking and consuming narcotics, history of drugs consumption, imaging finding, differential diagnoses of a severe respiratory disease syndrome, underlying conditions, complications of the disease, respiratory support and discharge outcomes. Determining data requirements is the first step in developing a registry that can create a standard infrastructure for consistent data collection.

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge.Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Introduction. Parkinson’s disease (PD) is the second most common neurological disorder. Patients with PD were affected by the COVID-19 pandemic in many different ways. This study’s principal purpose is to assess PD patients' vulnerability to COVID-19 and its consequences. Method. This systematic review was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. A thorough search was conducted in the Medline (through PubMed) and Scopus databases from inception to January 30, 2022. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to evaluate the studies. Results. Most of the studies (38%) had been conducted in Italy. Of the total number of studies, 17 (58%) were cross-sectional, seven (22%) were cohort, four (12%) were quasiexperimental, two (6%) were case-control, and one (3%) was a qualitative study. The PD duration in patients ranged from 3.26 to 13.40 years (IQR1: 5.7 yrs., median: 3.688 yrs., and IQR3: 8.815 yrs.). Meanwhile, the sample size ranged from 12 to 30872 participants (IQR1: 46, median: 96, and IQR3: 211). Despite worsening PD symptoms in the targeted population (persons with COVID-19 and Parkinson’s disease), some studies found PD to be a risk factor for more severe COVID-19 disease. There are many adverse effects during the pandemic period in PD patients such as abnormalities of motor, nonmotor functioning, clinical outcomes, activities of daily living, and other outcomes. Conclusion. This study confirmed the negative effect of the COVID-19 pandemic on health-related quality of life and its determinants in patients with PD and their caregivers. Thus, due to the worsening symptoms of PD patients in the current pandemic, these people should be given more care and supervision to minimize their coronavirus exposure.

AbstractAccess to care services in remote areas is challenging. The use of telemedicine technology in these areas facilitates access to health care. This study aimed to summarize the current research on telemedicine in remote areas such as mountains and forests. A systematic search was conducted in databases including Medline (through PubMed), Scopus, IEEE Xplore Digital Library, and ISI Web of Science to identify relevant studies published until May 12, 2021. Screening of retrieved articles for selection and inclusion in the study was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes extension for Scoping Reviews (PRISMA-ScR) checklist. A total of 807 articles were identified after removing duplicates, from which 20 studies meeting our inclusion criteria were selected. Challenges, opportunities, and equipment required to use telemedicine in remote areas were extracted from the selected studies. The results revealed that telemedicine implementation in remote areas had many challenges, including harsh weather conditions, Internet connectivity problems, difficult equipment transportation, and ethical issues. Telemedicine also has many benefits, such as cost and time savings for patients, improving patients’ quality of life, and improving patient satisfaction. Telemedicine for inhabitants of forested and mountainous areas facilitates rapid access to health care and enhances patient satisfaction. Distinguishing advantages and barriers as well as reducing restrictions will have an essential role in accelerating the use of this technology.