Mobile health apps for pregnant women usability and quality rating scales: a systematic review.

OBJECTIVE: This study is to identify the apps used by pregnant women during the technology era and to choose the best app from the point of view of pregnant women and experts. METHODS: The article is a research article that uses PRISMA flowchart. Given that there are many apps in the field of pregnancy and due to technological advances, the articles of the last 13 years that have been scientifically published in the databases of Google Scholar, PubMed, and Science Direct have been analyzed. The most widely used and, at the same time, the best app is introduced in terms of its high usability in users' attitude. Finally, Apps will be compared in terms of accuracy, precision, and usability of the dimensions of Jacob Nielsen's five principles. RESULTS: According to the search strategy, 23 articles were identified qualitatively by reviewing both authors. Then, the types of apps were divided into three general categories, pregnant entertainment apps, pregnant information apps, and monitoring apps for mothers' physical health. Finally, 10 apps were selected and the Amila app was introduced as the best due to its high usability (Effectiveness %66.66) and users' satisfaction or women's choice (%98). CONCLUSION: Using trusted apps to maintain their health and reduce traffic will be very important. Given that this research article was written with the aim of choosing the best app, that not only provides the required information to mothers, but also the ability to interact with doctors and specialists.

Persistence of SARS-CoV-2-antibodies against N, S and RBD after natural infection.

Background and Objectives: Coronavirus disease 2019 (COVID-19) pandemic has affected most countries in the world. Monitoring the humoral immune responses during the natural course of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection and the duration of them provide useful information for the development of vaccination strategies against this virus and its emerging variants. The importance of the antibody response especially neutralizing antibodies in long-term immunity to SARS-CoV-2 is significant. Materials and Methods: The present study is a cross-sectional study of sero-epidemiological type that has been proposed to compare the persistence of Immunoglobulin G (IgG) against N (nucleocapsid), S (spike) and RBD (receptor-binding domain) proteins in the community after the time of primary disease. A total of 652 serum samples were collected from hospital staff working in COVID wards, as well as a number of community members with different occupations, among those with positive antibody titers, 86 participated in the resampling test before vaccination. Results: There was no association between antibody titer and disease severity (p>0.05). A significant decrease in Ab levels was observed in the paired second samples. The highest rate of decrease was related to anti-N, then anti-RBD and anti-S IgG levels, respectively. There is a significant relationship between the initial antibody titer and its reduction over time (p-value <0.05). Conclusion: Our data revealed that humoral immunity following natural infection of SARS-CoV-2 is detectable for at least 4 months, regardless of disease severity. The most decrease in antibody titer over time was related to anti-N IgG levels.

Design and evaluation of an educational mobile program for liver transplant patients.

BACKGROUND: Liver transplantation, the last treatment for advanced liver failure, necessitates patient education due to its wide range of complications and subsequent disabilities. The present study was development-applied research and aimed to design a mobile-based educational program to provide liver transplant patients with critical health information. METHODS: In the first phase of the study, the crucial educational components were collected from the literature and organized in the form of a questionnaire using library studies and available global guidelines. The validity and reliability of this researcher-made questionnaire were confirmed by a panel of experts (n = 15), including gastroenterologists and liver specialists working in the Motahari liver clinic and AbuAli Sina Hospital in Shiraz. The application was designed followed by analyzing the data gathered from the first phase. To evaluate the mobile phone program's usability, to evaluate the application, 30 liver transplant patients were randomly selected. RESULTS: Most educational components covered in the questionnaire were deemed necessary by experts in the first phase. As a result, the educational contents were classified under 10 categories. The application had a good level of usability since the participants' satisfaction score was 8.1 (out of 9 points). CONCLUSIONS: Due to the increase in liver transplantation and the use of mobile phones, applications increase the patient's role in their health, and their awareness. It also leads to a better interaction and follow-up of the patient, the treatment staff of the medical centers.

Scoring systems of kidney donation from deceased donors: A systematic review.

Renal disease is the most prevalent disease. Kidney failure can cause physical problems. Hence, patients need to use dialysis therapy or kidney transplantation, and actually, people are in the waiting list for a transplant. This research aimed to extract the prognostic models that evaluate the preparation of kidney donors diagnosed with brain death (DBD). This research was a systematic review of PubMed, Science Direct, and general explorers up until 2020. It followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-P protocol. The assessment of the articles was done by the authors. This evaluation was supposed to be in the form of scoring, prioritizing, and ranking the donors in terms of their preparation. Eleven sources of information included 9 academic articles along with 2 Grey Sources from 7 different countries. 9 algorithms and models were extracted which included, overall 10 factors. All the models were comprised of 4 factors and about 90% of these models considered 4 or 5 factors to evaluate the preparation of kidney donors DBD. Over 60% of the models had taken into account age, blood pressure history, and creatinine factors. Disease prognosis facilitates a doctor's decision-making on the emergence of the disease. Prognostic models of renal diseases can be a great help to patients. A review of the related literature revealed that all the models received a high score in terms of the two factors they included, age and history of blood pressure.

A systematic review on reminder systems in physical therapy.

BACKGROUND: The main goal of physical therapy is to help the patient gain a better health status. Several studies have investigated the use of reminders to prevent such failures on the patients' side. This article presents a systematic review of the literature concerning reminders in physical therapy. METHODS: Databases were searched until May 2017 and literatures were found from April 1992 until 2017. The literature recruitment strategy was based on applying several keywords and Medical Subject Heading (MeSH) combination running against title and abstract, including concepts such as reminder, physical therapy. The finally selected articles were categorized through reminder aspects such as how, who feedback. Data were extracted according to PRISMA guidelines. RESULTS: In 47% of studies, the reminder was sent to the patients, 29% to the physical therapists and 12% to the caretaker team. In 24% of the studies, paper-based letters were main medium for reminders while the rest were various types of media like emails and SMS mobile text messages. 35% of the articles showed positive effects of the reminders. CONCLUSIONS: Many reminder methods consisted of SMS, phone calls, letters, emails and notices on the wall were used in physical therapy. Reminders may be used to improve patients' adherence to exercise programs.

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review.

INTRODUCTION: Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. OBJECTIVE: This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. METHODS: PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. RESULT: In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. DISCUSSION: This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. CONCLUSION: The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.