Mobile Application to Improve the Follow-up and Control Process in Patients with Tuberculosis

Tuberculosis is a severe and life-threatening illness that affects numerous individuals worldwide every day. The key objective of this study was to create a system that could enhance the monitoring and management of tuberculosis patients. To achieve this goal, the Mobile D methodology was utilized because of its effectiveness in project management. This methodology emphasizes test-driven development, continuous integration, and optimization to enhance software processes. The outcome of this research was a prototype of a mobile application specifically designed for individuals with tuberculosis. Professionals and people affected by the disease assessed the quality of the prototype. They evaluated its effectiveness, user-friendliness, design, and functionality and gave ratings of 4.77 and 4.69 on a Likert scale, respectively. These figures indicate that the prototype meets high-quality criteria. In conclusion, this research successfully created an efficient prototype that enhances the monitoring and control of tuberculosis patients. The prototype includes features such as real-time consultations for immediate interaction between physicians and patients, clinical history visualization, and medication reminders, all of which improve the user’s experience

IoT system for vital signs monitoring in suspicious cases of covid-19

ABSTRACT Currently the world is going through a pandemic caused by Covid-19, the World Health Organization recommends to stay isolated from the rest of the people. This research shows the development of a prototype based on the internet of things, which aims to measure three very important aspects: heart rate, blood oxygen saturation and body temperature, these will be measured through sensors that will be connected to a NodeMCU module that integrates a Wi-Fi module, which will transmit the data to an IoT platform through which the data can be displayed, achieving real-time monitoring of the vital signs of the patient suspected of Covid-19

Analysis of mobile applications reporting on nutritional recipes: a review of the scientific literature

ABSTRACT At present the planet faces a pandemic originated by the COVID-19, causing social isolation and decrease in the world economy; limiting more and more the resources of many people, which produces a deficient feeding, In this document a systematic review of literature was made considering scientific articles between the years 2010 and 2020 from sources like, IEEE Xplore, Concytec, Proquest, Scopus, WoS and Scielo, having as objective to know the best characteristics of mobile applications to inform about nutritional recipes. A total of 50 articles were studied and it was concluded that there are databases with nutritional information of foods that help greatly in improving the nutrition of people, also found various techniques for obtaining data from new technologies

Analysis of UV technologies for disinfection of public areas: A systematic literature review

ABSTRACT At present we live a health problem because of the Covid-19, therefore the study carried out is a systematic review of the different technological UV alternatives that have been developed to reduce the spread of Covid-19 and other pathogens harmful to health, since it has been proven that the UV-C range which is considered to have a very powerful radiation. In the present investigation 34 scientific articles were synthesized, taken from databases such as: Scopus/Elsevier, ScienceDirect, IEEE Xplore, Researchgate. Of these, 39% are oriented towards the health area and 21 % are used in the disinfection of public areas. In conclusion, the rates of use of this germicide and how to sterilize by means of ultraviolet radiation were announced

Mobile Applications for the Implementation of Health Control against Covid-19 in Educational Centers, a Systematic Review of the Literature

"—A health crisis caused by the SARS-CoV-2 virus is still ongoing. That is why an important factor for the resumption of on-site classes is the creation of sanitary measures to help control Covid-19. The present research is a literature review, The PRISMA methodology is used and 265 articles are collected from various databases such as EBSCO Host, IEEE Xplore, SAGE, ScienceDirect, and Scopus. According to the inclusion and exclusion criteria, the most relevant articles aligned to the topic were identified, systematizing 119 articles. Showcasing digital technologies used in mobile applications that allow better control, tracking, and monitoring of the health status of students, teachers, and staff of educational centers, in addition to the parameters and quality attributes that must be taken into account for the effective sanitary control of the disease, finally, a development model is proposed.

Educational robot for the care of infectious diseases in children: A review of the scientific literature 2010 - 2020

ABSTRACT Due to the pandemic caused by the COVID-19, we are forced to maintain a social distance, relying on technology such as the use of robots for both commercial and educational activities. This document is a systematic review of scientific literature using Prism methodology and aims to determine the best characteristics for the development of educational robots in children on infectious disease care. We obtained 50 articles associated to the research topic collected from databases such as IEEE Xplore, Scielo, Scopus and WoS. The results were synthesized in different tables and graphs separated in approaches of: robotics in education, robotics in relation to humans, education in diseases, robotics in health and digital applications in education, where the first one is the most treated in the articles found

Analysis of the Impact of the Pandemic on the Growth, Use, and Development of E-Business: A Systematic Review of the Literature

The COVID-19 pandemic has affected various sectors in multiple countries, among them the economic sector has been one of the most affected, so the search for tools or measures for the continuation of sales and processes became recurrent, finding in e-business and its components precise tools to counteract the situation. Therefore, the present research aims to analyze the impact of the COVID-19 pandemic on the use, growth, and development of e-business by conducting a systematic literature review using the PRISMA methodology, collecting scientific articles covering the period of the pandemic from databases such as IEEE Xplore, ScienceDirect, Scopus, EBSCO, and IOPScience. Despite the limitations in access to scientific articles, it could be concluded that within the main characteristics identified, e-business tools in general allowed many businesses to continue subsisting and making sales thanks to the increase in online users due to the COVID-19 lockdowns. Although it was identified that the adoption of these tools lacked policies, limitations, and supports from governments, the perception of their use was positive in that they were considered safe and efficient

Digital platform based on geomarketing as an improvement in micro and small enterprises

After the situation generated by the pandemic caused by COVID-19, micro and small enterprises (MSEs) faced a complex reality, having to cope with business uncertainty. This research proposes a digital platform based on geomarketing as a growth and support strategy for MSEs, with the objective of improving their labor and capital productivity, through the incorporation of the technological factor, which will have a great impact on them, helping them to continue operating and not having to close their businesses. The platform was developed under the agile Scrum methodology because it is adaptable to the constant changes in the mobile application development process, having as indicators labor productivity and capital productivity. Finally, the results revealed that labor productivity increased by 30.86 percent, meaning that, for every hour worked per person, more sales were made. As for capital productivity, it decreased by 1.47 percent, meaning that investment decreased for each value added of each product sold

Free hardware based system for air quality and CO2 monitoring

Due to the increase in air pollution, especially in Latin American countries of low and middle income, great environmental and health risks have been generated, highlighting that there is more pollution in closed environments. Given this problem, it has been proposed to develop a system based on free hardware for monitoring air quality and CO2, in order to reduce the levels of air pollution in a closed environment, improving the quality of life of people and contributing to the awareness of the damage caused to the environment by the hand of man himself. The system is based on V-Model, complemented with a ventilation prototype implemented with sensors and an application for its respective monitoring. The sample collected in the present investigation was non-probabilistic, derived from the reports of air indicators during 15 days with specific schedules of 9am, 1pm and 6pm. The results obtained indicated that the air quality decreased to 670 ppm, as well as the collection time decreased to 5 seconds and finally the presence of CO2 was reduced to 650 ppm after the implementation of the system, achieving to be within the standards recommended by the World Health Organization

Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2

Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus