Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies.

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

Treatment of Depression in Primary Care with Computerized Psychological Therapies: Systematic Reviews.

Depression is one of the most important causes of disability due to illness in our environment. The primary care health system receives a high percentage of this consultation about psychological distress. Often this end in a pharmacological overtreatment in patients with mild depression, due to a lack of access to alternative tools for management. To analyze the evidence that exists by now about the effectiveness of computerized psychological therapies, in people with depression in primary care setting. The search process was mainly done through MEDLINE and Cochrane using keywords such as: "depression", "treatment", "primary care", "online", "internet", "computerized", "Cognitive Behavioral Therapy" and delimiting the search by years and types of studies. The Oxman quality scale was used to analyze quality of Systematic Reviews (SR). 11 previous SR were analyzed. Almost all research is experimental and has not been implemented in the public health network except in the United Kingdom, where there is a tradition in the use of the Beating the Blues program. It requires research in our country and development of programs in Spanish, or adaptation of those of other countries, to test the effectiveness in our health system and to study, in turn, the cost-efficiency. But it is proven to be effective in reducing depressive symptoms and must be study as a possible tool to be introduced in the management of depression in non-specialized care.

Social Robots for People with Aging and Dementia: A Systematic Review of Literature.

Background: Social robots are currently a form of assistive technology for the elderly, healthy, or with cognitive impairment, helping to maintain their independence and improve their well-being. Objective: The main aim of this article is to present a review of the existing research in the literature, referring to the use of social robots for people with dementia and/or aging. Methods: Academic databases that were used to perform the searches are IEEE Xplore, PubMed, Science Direct, and Google Scholar, taking into account as date of publication the last 10 years, from 2007 to the present. Several search criteria were established such as "robot" AND "dementia," "robot" AND "cognitive impairment," "robot" AND "social" AND "aging," and so on., selecting the articles of greatest interest regarding the use of social robots in elderly people with or without dementia. Results: This search found a total of 96 articles on social robots in healthy people and with dementia, of which 38 have been identified as relevant work. Many of the articles show the acceptance of older people toward social robots. Conclusion: From the review of the research articles analyzed, it can be said that use of social robots in elderly people without cognitive impairment and with dementia, help in a positive way to work independently in basic activities and mobility, provide security, and reduce stress.

IoT-Based Services and Applications for Mental Health in the Literature.

Internet of Things (IoT) has emerged as a new paradigm today, connecting a variety of physical and virtual elements integrated with electronic components, sensors, actuators and software to collect and exchange data. IoT is gaining increasing attention as a priority research topic in the Health sector in general and in specific areas such as Mental Health. The main objective of this paper is to show a review of the existing research works in the literature, referring to the main IoT services and applications in Mental Health diseases. The scientific databases used to carry out the review are Google Scholar, IEEE Xplore, PubMed, Science Direct, and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as "IoT OR Internet of Things AND (Application OR Service) AND Mental Health" selecting the most interesting articles. A total of 51 articles were found on IoT-based services and applications in Mental Health, of which 14 have been identified as relevant works in mental health. Many of the publications (more than 60%) found show the applications developed for monitoring patients with mental disorders through sensors and networked devices. The inclusion of the new IoT technology in Health brings many benefits in terms of monitoring, welfare interventions and providing alert and information services. In pathologies such as Mental Health is a vital factor to improve the patient life quality and effectiveness of the medical service.

Systematic Review about QoS and QoE in Telemedicine and eHealth Services and Applications.

The provision of Quality of Service (QoS) and Quality of Experience (QoE) is a mandatory requirement when transmitting telemedicine traffic, due to information relevance to maintain the patient's health. The main objective of this paper is to present a review of existing research works in the literature, referring to QoS and QoE in telemedicine and eHealth applications. The academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct and Web of Science, taking into account the date of publication from 2008 to the present. These databases cover the most information of scientific texts in multidisciplinary fields, engineering and medicine. Several search criteria were established such as 'QoS' AND 'eHealth' OR 'Telemedicine', 'QoE' AND 'eHealth' AND 'Telemedicine' etc. selecting the items of greatest interest. A total of 248 papers related to QoS and QoE in telemedicine and eHealth have been found, of which 39 papers have been identified as relevant works. The results show that the percentage of studies related to QoS in literature is higher with 74.36% to QoE with 25.64%. From the review of the research articles analyzed, it can be said that QoS and QoE in telemedicine and eHealth are important and necessary factors to guarantee the privacy, reliability, quality and security of data in health care systems.

Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.

Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.

A Systematic Literature Review of Technologies for Suicidal Behavior Prevention.

Suicide is the second cause of death in young people. The use of technologies as tools facilitates the detection of individuals at risk of suicide thus allowing early intervention and efficacy. Suicide can be prevented in many cases. Technology can help people at risk of suicide and their families. It could prevent situations of risk of suicide with the technological evolution that is increasing. This work is a systematic review of research papers published in the last ten years on technology for suicide prevention. In September 2017, the consultation was carried out in the scientific databases PubMed, ScienceDirect, PsycINFO, The Cochrane Library and Google Scholar. A general search was conducted with the terms "prevention" AND "suicide" AND "technology. More specific searches included technologies such as "Web", "mobile", "social networks", and others terms related to technologies. The number of articles found following the methodology proposed was 90, but only 30 are focused on the objective of this work. Most of them were Web technologies (51.61%), mobile solutions (22.58%), social networks (12.90%), machine learning (3.23%) and other technologies (9.68%). According to the results obtained, although there are technological solutions that help the prevention of suicide, much remains to be done in this field. Collaboration among technologists, psychiatrists, patients, and family members is key to advancing the development of new technology-based solutions that can help save lives.

A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector.

The main objective of this paper is to present a review of existing researches in the literature, referring to Big Data sources and techniques in health sector and to identify which of these techniques are the most used in the prediction of chronic diseases. Academic databases and systems such as IEEE Xplore, Scopus, PubMed and Science Direct were searched, considering the date of publication from 2006 until the present time. Several search criteria were established as 'techniques' OR 'sources' AND 'Big Data' AND 'medicine' OR 'health', 'techniques' AND 'Big Data' AND 'chronic diseases', etc. Selecting the paper considered of interest regarding the description of the techniques and sources of Big Data in healthcare. It found a total of 110 articles on techniques and sources of Big Data on health from which only 32 have been identified as relevant work. Many of the articles show the platforms of Big Data, sources, databases used and identify the techniques most used in the prediction of chronic diseases. From the review of the analyzed research articles, it can be noticed that the sources and techniques of Big Data used in the health sector represent a relevant factor in terms of effectiveness, since it allows the application of predictive analysis techniques in tasks such as: identification of patients at risk of reentry or prevention of hospital or chronic diseases infections, obtaining predictive models of quality.

Primary Prevention of Asymptomatic Cardiovascular Disease Using Physiological Sensors Connected to an iOS App.

Cardiovascular disease is the first cause of death and disease and one of the leading causes of disability in developed countries. The prevalence of this disease is expected to increase in coming years although the death rate may be lower due to better treatment. To present the design and development of a technology solution for primary prevention of cardiovascular disease in asymptomatic patients. The system aims to raise the population's awareness of the importance of adopting healthy heart habits by using self-feedback techniques. A series of sensors which makes it possible to detect cardiovascular risk factors in asymptomatic patients were used. These sensors enable evaluation of heart rate, blood pressure, SpO2 -oxygen saturation in blood- and body temperature. This work has developed a modular solution centred on four parts: iOS app, sensors, server and web. The CoreBluetooth library, which carries out Bluetooth 4.0 communication, was used for the connection between the app and the sensors. The data files are stored on the iPad and the server by using CoreData and SQL mechanisms. The system was validated with 20 healthy volunteers and 10 patients with established structural heart disease. Once the samples had been obtained, a comparison of all the significant data was run, in addition to a statistical analysis. The result of this calculation was a total of 32 cases of first level significance correlations (p < 0.01), for example, the inverse relationship between the daily step count and high blood pressure (p = 0.008) and 24 s level cases (p < 0.05) such as the significant correlation between risk and age (p = 0.013). The system designed in this paper has made it possible to create an application capable of collecting data on cardiovascular risk factors through a sensor system that measures physiological variables and records physical activity and diet.