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With the rise of mobile devices and the high number of instant messaging applications available in the stores, it is necessary to evaluate the usability of such applications to provide a more satisfying user experience. To this end, this paper presents a methodical usability evaluation of instant messaging applications both in iOS and Android platforms. A predefined evaluation was used, which was created to detect the main usability issues of mobile applications, regardless of the device used and the evaluated applications. Consequently, two methods were used: the keystroke level model and the mobile heuristic evaluation. Also, the results suggest that the main problems of this type of applications are difficulties in performing tasks (some of them were not agile nor easy to complete), lack of element cohesion (some icons and buttons did not follow the style of the operating system, bad translations, and too much information on the screen), problems with the user interface (pop-ups overlapping the status bar, clipped elements, sometimes the interface did not rotate, and, in other cases, the interface changed considerably when the device was rotated), and lack of information about privacy and security features. Finally, based on the results, we propose a set of recommendations that will be helpful for developers of this kind of applications.

Abstract:In recent years, smartphones have become the main computing tool for most of the population, making them an ideal tool in many areas. Most of these smartphones are equipped with cutting-edge hardware on their digital cameras, sensors and processors. For this reason, this paper discusses the possibility of using smartphones as an accessible and accurate tool, focusing on the measurement of light, which is an element that has a high impact on human behavior, which promotes conformance and safety, or alters human physiology when it is inappropriate. To carry out this study, three different ways to measure light through smartphones have been checked: the ambient light sensor, the digital camera and an external Bluetooth luxmeter connected with the smartphone. As a result, the accuracy of these methods has been compared to check if they can be used as accurate measurement tools.

Clinical Decision Support Systems have the potential to reduce lack of communication and errors in diagnostic steps in primary health care. Literature reports have showed great advances in clinical decision support systems in the recent years, which have proven its usefulness in improving the quality of care. However, most of these systems are focused on specific areas of diseases. In this way, we propose a rule-based expert system, which supports clinicians in primary health care, providing a list of possible diseases regarding patient's laboratory tests results in order to assist previous diagnosis. Our system also allows storing and retrieving patient's data and the history of patient's analyses, establishing a basis for coordination between the various health care levels. A validation step and speed performance tests were made to check the quality of the system. We conclude that our system could improve clinician accuracy and speed, resulting in more efficiency and better quality of service. Finally, we propose some recommendations for further research.