Background Serious educational games have shown effectiveness in improving various health outcomes. Previous reviews of health education games have focused on specific diseases, certain medical subjects, fixed target groups, or limited outcomes of interest. Given the recent surge in health game studies, a scoping review of health education games is needed to provide an updated overview of various aspects of such serious games. Objective This study aimed to conduct a scoping review of the design and evaluation of serious educational games for health targeting health care providers, patients, and public (health) users. Methods We identified 2313 studies using a unique combination of keywords in the PubMed and ScienceDirect databases. A total of 161 studies were included in this review after removing duplicates (n=55) and excluding studies not meeting our inclusion criteria (1917 based on title and abstract and 180 after reviewing the full text). The results were stratified based on games targeting health care providers, patients, and public users. Results Most health education games were developed and evaluated in America (82/161, 50.9%) and Europe (64/161, 39.8%), with a considerable number of studies published after 2012. We discovered 58.4% (94/161) of studies aiming to improve knowledge learning and 41.6% (67/161) to enhance skill development. The studies targeted various categories of end users: health care providers (42/161, 26.1%), patients (38/161, 23.6%), public users (75/161, 46.6%), and a mix of users (6/161, 3.7%). Among games targeting patients, only 13% (6/44) targeted a specific disease, whereas a growing majority targeted lifestyle behaviors, social interactions, cognition, and generic health issues (eg, safety and nutrition). Among 101 studies reporting gameplay specifications, the most common gameplay duration was 30 to 45 min. Of the 61 studies reporting game repetition, only 14% (9/61) of the games allowed the users to play the game with unlimited repetitions. From 32 studies that measured follow-up duration after the game intervention, only 1 study reported a 2-year postintervention follow-up. More than 57.7% (93/161) of the games did not have a multidisciplinary team to design, develop, or assess the game. Conclusions Serious games are increasingly used for health education targeting a variety of end users. This study offers an updated scoping review of the studies assessing the value of serious games in improving health education. The results showed a promising trend in diversifying the application of health education games that go beyond a specific medical condition. However, our findings indicate the need for health education game development and adoption in developing countries and the need to focus on multidisciplinary teamwork in designing effective health education games. Furthermore, future health games should expand the duration and repetition of games and increase the length of the follow-up assessments to provide evidence on long-term effectiveness.

ObjectivesTo explore the scope of the published literature on computer-tailoring, considering both the development and the evaluation aspects, with the aim of identifying and categorising main approaches and detecting research gaps, tendencies and trends.SettingOriginal researches from any country and healthcare setting.ParticipantsPatients or health consumers with any health condition regardless of their specific characteristics.MethodA systematic scoping review was undertaken based on the York’s five-stage framework outlined by Arksey and O’Malley. Five leading databases were searched: PubMed, Scopus, Science Direct, EBSCO and IEEE for articles published between 1990 and 2017. Tailoring concept was investigated for three aspects: system design, information delivery and evaluation. Both quantitative (ie, frequencies) and qualitative (ie, theme analysis) methods have been used to synthesis the data.ResultsAfter reviewing 1320 studies, 360 articles were identified for inclusion. Two main routes were identified in tailoring literature including public health research (64%) and computer science research (17%). The most common facets used for tailoring were sociodemographic (73 %), target behaviour status (59%) and psycho-behavioural determinants (56%), respectively. The analysis showed that only 13% of the studies described the tailoring algorithm they used, from which two approaches revealed: information retrieval (12%) and natural language generation (1%). The systematic mapping of the delivery channel indicated that nearly half of the articles used the web (57%) to deliver the tailored information; printout (19%) and email (10%) came next. Analysis of the evaluation approaches showed that nearly half of the articles (53%) used an outcome-based approach, 44% used process evaluation and 3% assessed cost-effectiveness.ConclusionsThis scoping review can inform researchers to identify the methodological approaches of computer tailoring. Improvements in reporting and conduct are imperative. Further research on tailoring methodology is warranted, and in particular, there is a need for a guideline to standardise reporting.

IntroductionTailoring health information to the needs of individuals has become an important part of modern health communications. Tailoring has been addressed by researchers from different disciplines leading to the emergence of a wide range of approaches, making the newcomers confused. In order to address this, a comprehensive overview of the field with the indications of research gaps, tendencies and trends will be helpful. As a result, a systematic protocol was outlined to conduct a scoping review within the field of computer-based health information tailoring.Methods and analysisThis protocol is based on the York’s five-stage framework outlined by Arksey and O’Malley. A field-specific structure was defined as a basis for undertaking each stage. The structure comprised three main aspects: system design, information communication and evaluation. Five leading databases were searched: PubMed, Scopus, Science Direct, EBSCO and IEEE and a broad search strategy was used with less strict inclusion criteria to cover the breadth of evidence. Theoretical frameworks were used to develop the data extraction form and a rigorous approach was introduced to identify the categories from data. Several explanatory-descriptive methods were considered to analyse the data, from which some were proposed to be employed for the first time in scoping studies.Ethics and disseminationThis study investigates the breadth and depth of existing literature on computer-tailoring and as a secondary analysis, does not require ethics approval. We anticipate that the results will identify research gaps and novel ideas for future studies and provide direction to combine methods from different disciplines. The research findings will be submitted for publication to relevant peer-reviewed journals and conferences targeting health promotion and patient education.

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Introduction: Health care data is increasing. The correct analysis of such data will improve the quality of care and reduce costs. This kind of data has certain features such as high volume, variety, high-speed production, etc. It makes it impossible to analyze with ordinary hardware and software platforms. Choosing the right platform for managing this kind of data is very important. The purpose of this study is to introduce and compare the most popular and most widely used platform for processing big data, Apache Hadoop MapReduce, and the two Apache Spark and Apache Flink platforms, which have recently been featured with great prominence.Material and Methods: This study is a survey whose content is based on the subject matter search of the Proquest, PubMed, Google Scholar, Science Direct, Scopus, IranMedex, Irandoc, Magiran, ParsMedline and Scientific Information Database (SID) databases, as well as Web reviews, specialized books with related keywords and standard. Finally, 80 articles related to the subject of the study were reviewed.Results: The findings showed that each of the studied platforms has features, such as data processing, support for different languages, processing speed, computational model, memory management, optimization, delay, error tolerance, scalability, performance, compatibility, Security and so on. Overall, the findings showed that the Apache Hadoop environment has simplicity, error detection, and scalability management based on clusters, but because its processing is based on batch processing, it works for slow complex analyzes and does not support flow processing, Apache Spark is also distributed as a computational platform that can process a big data set in memory with a very fast response time, the Apache Flink allows users to store data in memory and load them multiple times and provide a complex Fault Tolerance mechanism Continuously retrieves data flow status.Conclusion: The application of big data analysis and processing platforms varies according to the needs. In other words, it can be said that each technology is complementary, each of which is applicable in a particular field and cannot be separated from one another and depending on the purpose and the expected expectation, and the platform must be selected for analysis or whether custom tools are designed on these platforms.