Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: As the quality of online health information remains questionable, there is a pressing need to understand how consumers evaluate this information. Past reviews identified content-, source-, and individual-related factors that influence consumer judgment in this area. However, systematic knowledge concerning the evaluation process, that is, why and how these factors influence the evaluation behavior, is lacking. Objective: This review aims to (1) identify criteria (rules that reflect notions of value and worth) that consumers use to evaluate the quality of online health information, and the indicators (properties of information objects to which criteria are applied to form judgments) that they use to support the evaluation, to achieve a better understanding of the process of information quality evaluation and (2) explicate the relationship between indicators and criteria to provide clearer guidelines for designers of consumer health information systems. Methods: A systematic literature search was performed in seven digital reference databases covering medicine, psychology, communication, and library and information science to identify empirical studies which report on how consumers directly and explicitly describe their evaluation of online health information quality. Thirty-seven articles met the inclusion criteria. A qualitative content analysis was performed to identify quality evaluation criteria, indicators, and their relationships. Results: 25 criteria and 165 indicators were identified. The most widely reported criteria used by consumers are trustworthiness, expertise, and objectivity. The indicators were related to source, content, and design. Among them 114 are positive indicators (entailing positive quality judgments), 35 are negative (entailing negative judgments), and 16 entail both positive and negative quality judgments, depending on contextual factors (e.g., source and individual differences) and criteria applied. The most widely reported indicators are the identity of site owners, consensus among multiple sources, the use of professional medical terms and technical vocabularies, and advertisements. Conclusions: Consumer evaluation of online health information is a complex cost-benefit analysis process that involves the use of a wide range of criteria and a much wider range of quality indicators. There are some commonalities in the use of criteria across user groups and source types, but the differences are hard to ignore. It is obvious that consumers’ health information evaluation can be characterized as highly subjective and contextualized, and sometimes misinformed. These findings invite more research into how different user groups evaluate different types of online sources and a personalized approach to educate users about evaluating online health information quality.

Assessment of mental workload using physiological measures, especially electroencephalography (EEG) signals, is an active area. Recently, a number of wireless acquisition systems to measure EEG and other physiological signals have become available. Few studies have applied such wireless systems to assess cognitive workload and evaluate their performance. This paper presents an initial step to explore the feasibility of a popular wireless system (Emotiv EPOC headset) to assess memory workload levels in a well-known n-back task. We developed a signal processing and classification framework, which integrated an automatic artifact removal algorithm, a broad spectrum of feature extraction techniques, a personalized feature scaling method, an information-theory-based feature selection approach, and a proximal-support-vector-machine-based classification model. The experimental results show that the wirelessly collected EEG signals can be used to classify different memory workload levels for nine participants. The classification accuracies between the lowest workload level (0-back) and active workload levels (1-, 2-, 3-back) were close to 100%. The best classification accuracy for 1- versus 2-back was 80%, and 1- versus 3-back was 84%. This study indicates that the wireless acquisition system and the advanced data analytics and pattern recognition techniques are promising to achieve real-time monitoring and identification of mental workload levels for humans engaged in a wide variety of cognitive activities in the modern society.

Email's conduit function means the inbox, folders, search, and sort are used to support core PIM functions of task management, personal archiving, and contact management.

Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors. Task types have been shown to influence search behaviors including usefulness judgments. This paper reports on an investigation of user behaviors associated with different task types. Twenty-two undergraduate journalism students participated in a controlled lab experiment, each searching on four tasks which varied on four dimensions: complexity, task product, task goal and task level. Results indicate regular differences associated with different task characteristics in several search behaviors, including task completion time, decision time (the time taken to decide whether a document is useful or not), and eye fixations, etc. We suggest these behaviors can be used as implicit indicators of the user's task type.