Being the third fastest-growing app category behind games and utilities, mHealth apps are changing the healthcare model, as medicine today involves the data they compile and analyse, information known as Big Data. However, the majority of apps are lacking in security when gathering and dealing with the information, which becomes a serious problem. This article presents a guide regarding security solution, intended to be of great use for developers of mHealth apps. In August 2015 current mobile health apps were sought out in virtual stores such as Android Google Play, Apple iTunes App Store etc., in order to classify them in terms of usefulness. After this search, the most widespread weaknesses in the field of security in the development of these mobile apps were examined, based on sources such as the "OWASP Mobile Security Project, the initiative recently launched by the Office of Civil Rights (OCR), and other articles of scientific interest. An informative, elemental guide has been created for the development of mHealth apps. It includes information about elements of security and its implementation on different levels for all types of mobile health apps based on the data that each app manipulates, the associated calculated risk as a result of the likelihood of occurrence and the threat level resulting from its vulnerabilities - high level (apps for monitoring, diagnosis, treatment and care) from 6 ≤ 9, medium level (calculator, localizer and alarm) from 3 ≤ 6 and low level (informative and educational apps) from 0 ≤ 3. The guide aims to guarantee and facilitate security measures in the development of mobile health applications by programmers unconnected to the ITC and professional health areas.
The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.
Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder marked by an ongoing pattern of inattention and/or hyperactivity-impulsivity that affects with development or functioning. It affects 3-5% of all American and European children. The objective of this paper is to develop and test a dual system for the rehabilitation of cognitive functions in children with ADHD. A technological platform has been developed using the ". NET framework", which makes use of two physiological sensors, -an eye-tracker and a hand gesture recognition sensor- in order to provide children with the opportunity to develop their learning and attention skills. The two physiological sensors we utilized for the development are the Tobii X1 Light Eye Tracker and the Leap Motion. SUS and QUIS questionnaires have been carried out. 19 users tested the system and the average age was 10.88 years (SD = 3.14). The results obtained after tests were performed were quite positive and hopeful. The learning of the users caused by the system and the interfaces item got a high punctuation with a mean of 7.34 (SD = 1.06) for SUS questionnaire and 7.73 (SD = 0.6) for QUIS questionnaire. We didn't find differences between boys and girls. The developed multimodal rehabilitation system can help to children with attention deficit and learning issues. Moreover, the teachers may utilize this system to track the progression of their students and see their behavior.
Modern-day society has moved towards a more sedentary lifestyle. Advances in technology and changes in habits in our daily lives have led a large part of the population towards a spiralling sedentary lifestyle and obesity. The main objective of this work is to develop and subsequently assess a mobile app, named DietApp, that provides advice about obtaining a healthy diet according to age, clinical history and physical condition. DietApp has been developed for iOS and Android systems, and a survey comprising 7 simple questions enabled the app to be evaluated on a user level by taking into account aspects such as its usefulness and ease of use. DietApp was assessed by 150 Spanish individuals between 18 and 69 years of age, and 84% of them thought it was easy to use. 80% of users also considered the dietary suggestions provided by the app to be very useful while 62% were of the opinion that it is very useful in general. All of them would recommend the app to other users. During the six months when the app was used, any dietary excess or shortcomings were corrected in 72% of those interviewed. A mobile app has been created that is easy to use and attractive, providing personalised suggestions according to illness that are useful for the individual.
Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one.
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