Abstract
A patient's health literacy has a direct impact on their health, but more than a third of the USA population has "basic" or "below basic" levels of health literacy. An individual's wellbeing is also affected by the communication with their physician, as the use of technical terminology may hinder the patient's understanding. A patient's ability to, later on, recall or retrieve helpful information could reduce these comprehension problems and this can be improved by a good management of personal health information. To help overcome some of these problems, we created HealthTalks, a mobile app that empowers the patients, easing their daily health tasks and self-care ability. It does so by recording the audio of a medical appointment, transcribing its dialogue, giving more information about medical concepts employed, and allowing information associated with medical appointments to be easily managed by the patient. Usability tests were conducted with elderly people, ranging from the icons used to the general user experience. Results were very positive, with users accomplishing most tasks successfully and often with the least amount of clicks. We also evaluated the speech recognition software used, Google Cloud Speech API, reaching an error rate of 12 percent in medical texts.

Abstract
If it were possible to automatically detect proficiency in languages using data from eye movements, new levels of customizing computer applications could possibly be achieved. An example in case is web searches where suggestions and results could be adjusted to the user's knowledge of the language. The objective of this study is to compare the reading habits of users with high and low English language proficiency, having in mind the possible automatic detection of the English proficiency level through reading. For this purpose, a study was conducted with two types of user, those with a high level of proficiency (Proficient Users), and those with low proficiency (Basic Users) in the English language. An eye-tracker was used to collect users' eye movements while reading a text in English. Results show that users with high proficiency engage in more careful reading. In contrast, low English proficiency users take more time to read, revisit sentences and paragraphs more often, have more and longer fixations and also a higher number of saccades. As expected, these users have more difficulties in understanding the text.

Abstract

Abstract
To help laypeople surpass the common difficulties they face when searching for health information on the Web, we built Health Suggestions, an extension for Google Chrome to assist users obtaining high-quality search results in the health domain. This is achieved by providing users with suggestions of queries formulated in different terminologies and languages. Translations of health expressions might not be obvious and queries in languages with a strong presence on the Web facilitate the access to high-quality health contents. On the other hand, the use of lay terminology might contribute to increase users' comprehension and medico-scientific terminology to obtain more detailed and technical contents. Results show a good acceptance of the suggestions, confirm the utility of a multilingual and multi-terminology approach and show its usefulness to more successful searches.

Abstract
Nowadays, online communities are becoming an important resource for health consumers who want to retrieve and share information about health subjects. These communities have the potential to influence patients' health behaviors and increase their engagement with therapies. However, the interaction dynamics in this type of media remains poorly understood what might hinder the development of strategies that facilitate and encourage participation. Social Network Analysis is a technique that tries to expose the hidden channels of communication and information flow, leading to a better understanding of how members relate to each other on online social network. In this study we do a systematic review of the literature regarding the apply this technique in the study of online health communities. We show that this type of studies is scarce and that, in this domain, Social Network Analysis is mainly applied to identify influential key members, as well as the most active members in terms of posting or answering questions.

Abstract
Identifying the user's intent behind a query is a key challenge in Information Retrieval. This information may be used to contextualize the search and provide better search results to the user. The automatic identification of queries targeting a search for health information allows the implementation of retrieval strategies specifically focused on the health domain. In this paper, two kinds of automatic methods to identify and classify health queries based on domain-specific terminology are proposed. Besides evaluating these methods, we compare them with a method that is based on co-occurrence statistics of query terms with the word "health". Although the best overall result was achieved with a variant of the co-occurrence method, the method based on domain-specific frequencies that generates a continuous output outperformed most of the other methods. Moreover, this method also allows the association of queries to the semantic tree of the Unified Medical Language System and thereafter their classification into appropriate subcategories. Copyright © 2014, IGI Global.