The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.

In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.

Currently, cardiovascular diseases are the deadliest diseases with a total of 17 million deaths worldwide. Hence, they are the focus of many mobile applications for smartphones and tablets. This paper will assess the ex-ante economic impact as well as will determine the cost-effectiveness analysis that the use of one of this app, CardioManager, by patients with heart failure will have in a Spanish community, Castile and Leon. For this, a cost-effectiveness analysis using the hidden Markov model were performed in a hypothetical cohort of patients diagnosed with heart failure, based on the information of epidemiological parameters and the costs derived from the management and care of heart failure patients by the Public Health Care System of Castile and Leon. The costs of patient care were estimated from the perspective of the Ministry of Health of Spain using a discount rate of 3 %. Finally, an estimation of the ex-ante impact that would suppose the introduction of CardioManager in the Health Care System is performed. It is concluded that the introduction of CardioManager may generate a 33 % reduction in the cost of management and treatment of the disease. This means that CardioManager may be able to save more than 9,000 € per patient to the local Health Care System of Castile and Leon, which can be translated in a saving of 0.31 % of the total health expenditure of the region.

The rapid spread of smartphones and tablets in the last years has created a new software industry whose fast growth has propitiated numerous low-quality applications to be revised and improved. The main aim of this paper is to develop a tool to assess the Quality of Experience (QoE) of mobile Health (mHealth) applications in order to improve the quality of the existing apps and the ones to be released. Firstly, a review of the applications of mHealth has been done in order to obtain a general classification. Secondly, the tool for measuring the QoE is developed in the form of a survey with the help of psychologists. Finally, this tool is evaluated using a sample of applications selected with the aid of the classification obtained. A survey with 21 questions using the Likert scale and destined to users has been successfully created, and its evaluation has been positive, resulting in a good method for measuring the QoE of the different features that the applications in the field of health care usually have. The tool created can be very useful for developers in order to assess the quality of their health care apps, indicating their positive aspects and the ones which must be revised and improved, avoiding the releasing of low-quality apps.

Nowadays with the growing of the wireless connections people can access all the resources hosted in the Cloud almost everywhere. In this context, organisms can take advantage of this fact, in terms of e-Health, deploying Cloud-based solutions on e-Health services. In this paper two Cloud-based solutions for different scenarios of Electronic Health Records (EHRs) management system are proposed. We have researched articles published between the years 2005 and 2011 about the implementation of e-Health services based on the Cloud in Medline. In order to analyze the best scenario for the deployment of Cloud Computing two solutions for a large Hospital and a network of Primary Care Health centers have been studied. Economic estimation of the cost of the implementation for both scenarios has been done via the Amazon calculator tool. As a result of this analysis two solutions are suggested depending on the scenario: To deploy a Cloud solution for a large Hospital a typical Cloud solution in which are hired just the needed services has been assumed. On the other hand to work with several Primary Care Centers it's suggested the implementation of a network, which interconnects these centers with just one Cloud environment. Finally it's considered the fact of deploying a hybrid solution: in which EHRs with images will be hosted in the Hospital or Primary Care Centers and the rest of them will be migrated to the Cloud.

IntroductionIncorporating technology in cognitive interventions represents an innovation, making them more accessible, flexible, and cost-effective. This will not be feasible without adequate user-technology fit. Bearing in mind the importance of developing cognitive interventions whose technology is appropriate for elderly people with cognitive impairment, the objective of this systematic review was to find evidence about usability and user experience (UX) measurements and features of stimulation, training, and cognitive rehabilitation technologies for older adults with mild cognitive impairment (MCI) or dementia.MethodThe Medline, PubMed, Scopus, ScienceDirect, and PsycINFO databases were searched for literature published in the last 10 years (2009–2019), and three researchers independently reviewed potentially eligible studies, following specific inclusion criteria. A systematic review of the studies was conducted, presenting a qualitative synthesis of usability and UX measures with their outcomes, study characteristics and features of the cognitive intervention technologies.ResultsTen studies were selected: five were cognitive stimulation and five were cognitive training. Most of them (60%) were computer-based programs with a serious game format. Efficiency and effectiveness were the most frequent measurements used for collecting objective usability data, showing that elderly people with cognitive impairment require more time (45%) and help (40%) but can complete tasks (60%). Regarding UX or subjective usability data, questionnaires and scales were the most used methods, reporting positive experience despite certain difficulties with the interface in five studies.ConclusionMeasuring usability and UX in cognitive intervention technologies for older adults with MCI or dementia provides an integrated view that can contribute to their development according to the needs and characteristics of the target population. More research is required to include this population group in usability and UX studies, as well as standardized tools and consensus on the relationship of these terms to guarantee the future effectiveness of cognitive intervention technologies.Review registrationThis review was registered in the PROSPERO (CRD42020158147) International Register of Systematic Review Protocols.

Currently, many incomplete mobile apps can be found in the commercial stores, apps with bugs or low quality that needs to be seriously improved. The aim of this paper is to use two different tools for assessing the quality of a mHealth app for the self-management of heart diseases by the own patients named Heartkeeper. The first tool measures the compliance with the Android guidelines given by Google and the second measures the users' Quality of Experience (QoE). The results obtained indicated that Heartkeeper follows in many cases the Android guidelines, especially in the structure, and offers a satisfactory QoE for its users, with special mention to aspects such as the learning curve, the availability and the appearance. As a result, Heartkeeper has proved to be a satisfactory app from the point of view of Google and the users. The conclusions obtained are that the type of tools that measure the quality of an app can be very useful for developers in order to find aspects that need improvements before releasing their apps. By doing this, the number of low-quality applications released will decrease dramatically, so these techniques are strongly recommended for all the app developers.

The growing importance that mobile devices have in daily life has also reached health care and medicine. This is making the paradigm of health care change and the concept of mHealth or mobile health more relevant, whose main essence is the apps. This new reality makes it possible for doctors who are not specialist to have easy access to all the information generated in different corners of the world, making them potential keepers of that knowledge. However, the new daily information exceeds the limits of the human intellect, making Decision Support Systems (DSS) necessary for helping doctors to diagnose diseases and also help them to decide the attitude that has to be taken towards these diagnoses. These could improve the health care in remote areas and developing countries. All of this is even more important in diseases that are more prevalent in primary care and that directly affect the people's quality of life, this is the case in ophthalmological problems where in first patient care a specialist in ophthalmology is not involved. The goal of this paper is to analyse the state of the art of DSS in Ophthalmology. Many of them focused on diseases affecting the eye's posterior pole. For achieving the main purpose of this research work, a literature review and commercial apps analysis will be done. The used databases and systems will be IEEE Xplore, Web of Science (WoS), Scopus, and PubMed. The search is limited to articles published from 2000 until now. Later, different Mobile Decision Support System (MDSS) in Ophthalmology will be analyzed in the virtual stores for Android and iOS. 37 articles were selected according their thematic (posterior pole, anterior pole, Electronic Health Records (EHRs), cloud, data mining, algorithms and structures for DSS, and other) from a total of 600 found in the above cited databases. Very few mobile apps were found in the different stores. It can be concluded that almost all existing mobile apps are focused on the eye's posterior pole. Among them, the most intended are for diagnostic of diabetic retinopathy. The primary market niche of the commercial apps is the general physicians.