Search

In recent years the Web has come into its own as a social platform where health consumers are actively creating and consuming Web content. Moreover, as the Web matures, consumers are gaining access to personalized applications adapted to their health needs and interests. The creation of personalized Web applications relies on extracted information about the users and the content to personalize. The Social Web itself provides many sources of information that can be used to extract information for personalization apart from traditional Web forms and questionnaires. This paper provides a review of different approaches for extracting information from the Social Web for health personalization. We reviewed research literature across different fields addressing the disclosure of health information in the Social Web, techniques to extract that information, and examples of personalized health applications. In addition, the paper includes a discussion of technical and socioethical challenges related to the extraction of information for health personalization.

Health consumers have embraced the web to obtain access to health information and to socialize and share knowledge with peers. Additionally, the web has become a more interactive and rich platform with the integration of health applications and services, such as Personal Health Records. Some of these applications provide personalized interactions based on user specific characteristics. In this paper we provide an overview of Personalized Health Applications in the Web 2.0. We reviewed the health applications integrated in Google Health, Microsoft HealthVault and Facebook. We studied the goals of the applications and also the personalized feedback they provided.

The use of computers in health education started more than a decade ago, mainly for tailoring health educational resources. Nowadays, many of the computer-tailoring health education systems are using the Internet for delivering different types of health education. Traditionally, these systems are designed for a specific health problem, with a predefined library of educational resources. These systems do not take advantage of the increasing amount of educational resources available on the Internet. One of the reasons is that the high availability of content is making it more difficult to find the relevant one. The problem of information overload has been addressed for many years in the field of recommender systems. This paper is focused on the challenges and opportunities of merging recommender systems with personalized health education. It also discusses the usage of social networks and semantic technologies within this approach.

Background: Zika virus, an emerging serious infectious disease, is a threat to persons living or travelling to regions where it is currently endemic, and also to contacts of infected individuals. The aim of this study was to assess knowledge about this new public health threat to persons residing in a Middle Eastern country.Methods: We conducted a survey at several international universities in Qatar to assess knowledge and awareness about this disease. An adapted version of the survey was also conducted using online channels from Qatar.Results: The median age of the 446 participants, was 25 years, 280 (63%) were females, and 32% were from Gulf Cooperation Council (GCC) or other Middle East countries. Based upon their knowledge about availability of a vaccine, role of mosquitoes and other modes of transmission, and disease complications, we classified respondent's knowledge as "poor" (66%), "basic" (27%) or "broad" (7%). Forty-five (16%) persons with poor knowledge considered themselves to be well-informed.Conclusions: This report from a sample of persons associated with Middle East educational complex, reveals inadequate knowledge about Zika virus, a serious emerging infectious disease. Although few cases have been reported from the region, future cases are possible, since this area is a transit hub connecting currently infected regions to North America, Europe and Asia. As a preventive measure, an educational program about Zika virus would be valuable, especially for individuals or family members travelling to afflicted regions. [ABSTRACT FROM AUTHOR]

The use of computers in health education started more than a decade ago, mainly for tailoring health educational resources. Nowadays, many of the computer-tailoring health education systems are using the Internet for delivering different types of health education. Traditionally, these systems are designed for a specific health problem, with a predefined library of educational resources. These systems do not take advantage of the increasing amount of educational resources available on the Internet. One of the reasons is that the high availability of content is making it more difficult to find the relevant one. The problem of information overload has been addressed for many years in the field of recommender systems. This paper is focused on the challenges and opportunities of merging recommender systems with personalized health education. It also discusses the usage of social networks and semantic technologies within this approach. [ABSTRACT FROM AUTHOR]

Background: Recent publications on YouTube have advocated its potential for patient education. However, a reliable description of what could be considered quality information for patient education on YouTube is missing. Objective: To identify topics associated with the concept of quality information for patient education on YouTube in the scientific literature. Methods: A literature review was performed in MEDLINE, ISI Web of Knowledge, Scopus, and PsychINFO. Abstract selection was first conducted by two independent reviewers; discrepancies were discussed in a second abstract review with two additional independent reviewers. Full text of selected papers were analyzed looking for concepts, definitions, and topics used by its authors that focused on the quality of information on YouTube for patient education. Results: In total, 456 abstracts were extracted and 13 papers meeting eligibility criteria were analyzed. Concepts identified related to quality of information for patient education are categorized as expert-driven, popularity-driven, or heuristic-driven measures. These include (in descending order): (1) quality of content in 10/13 (77%), (2) view count in 9/13 (69%), (3) health professional opinion in 8/13 (62%), (4) adequate length or duration in 6/13 (46%), (5) public ratings in 5/13 (39%), (6) adequate title, tags, and description in 5/13 (39%), (7) good description or a comprehensive narrative in 4/13 (31%), (8) evidence-based practices included in video in 4/13 (31%), (9) suitability as a teaching tool in 4/13 (31%), (10) technical quality in 4/13 (31%), (11) credentials provided in video in 4/13 (31%), (12) enough amount of content to identify its objective in 3/13 (23%), and (13) viewership share in 2/13 (15%). Conclusions: Our review confirms that the current topics linked to quality of information for patient education on YouTube are unclear and not standardized. Although expert-driven, popularity-driven, or heuristic-driven measures are used as proxies to estimate the quality of video information, caution should be applied when using YouTube for health promotion and patient educational material. [ABSTRACT FROM AUTHOR]