Cyberchondria as an emerging trans-diagnostic digital compulsive syndrome: An updated systematic review and clinical case report

Non peer reviewe

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

Supporting care by interpretation of expressions about patient experience with machine learning

Our research aims at addressing the needs of developing data analysis about communication in respect to care seeking and primary care, discovering how health expressions evolve along the personal growth and learning process, and how to solve the needs identified in respect to developing measuring the quality of life. We provide an overview of the development of a new research methodology exploiting machine learning for analyzing patient experience expressions to support personalized care and managing in everyday life. Our research relies on an online questionnaire in which the representatives of various population groups perform interpretation tasks. Dependencies between answers about the interpretation tasks and background information are analyzed with machine learning methods. The research creates new ways to interpret and address the meanings of language usage of different groups of patients and impaired carefully and distinctively as a part of everyday life and care events.Peer reviewe

Finding information about mental health in microblogging platforms: a Case study of depression

Searching for online health information has been well studied in web search, but social media, such as public microblogging services, are well known for different types of tacit information: personal experience and shared information. Finding useful information in public microblogging platforms is an on-going hard problem and so to begin to develop a better model of what health information can be found, Twitter posts using the word “depression” were examined as a case study of a search for a prevalent mental health issue. 13,279 public tweets were analysed using a mixed methods approach and compared to a general sample of tweets. First, a linguistic analysis suggested that tweets mentioning depression were typically anxious but not angry, and were less likely to be in the first person, indicating that most were not from individuals discussing their own depression. Second, to un-derstand what types of tweets can be found, an inductive thematic analysis revealed three major themes: 1) dissemi-nating information or link of information, 2) self-disclosing, and 3) the sharing of overall opinion; each had significantly different linguistic patterns. We conclude with a discussion of how different types of posts about mental health may be retrieved from public social media like Twitter

Direction to an internet support group compared with online expressive writing for people with depression and anxiety: a randomized trial

BACKGROUND: Depression and anxiety are common, often comorbid, conditions, and Internet support groups for them are well used. However, little rigorous research has been conducted on the outcome of these groups. OBJECTIVE: This study aimed to evaluate the efficacy of an Internet support group in reducing depression and anxiety, and increasing social support and life satisfaction. METHODS: A randomized trial compared direction to an existing Internet support group for depression and anxiety with an online expressive writing condition. A total of 863 (628 female) United Kingdom, United States, and Canadian volunteers were recruited via the Internet. Online, self-report measures of depression, anxiety, social support, and satisfaction with life were administered at baseline, 3, and 6 months. RESULTS: All four outcomes - depression, anxiety, social support, and satisfaction with life - improved over the 6 months of the study (all P <.001). There was no difference in outcome between the two conditions: participants responded similarly to the expressive writing and the Internet support group. Engagement with the Internet support group was low, it had high 6-month attrition (692/795, 87%) and low adherence, and it received mixed and often negative feedback. The main problems reported were a lack of comfort and connection with others, negative social comparisons, and the potential for receiving bad advice. Expressive writing had lower attrition (194/295, 65%) and participants reported that it was more acceptable. CONCLUSIONS: Until further evidence accumulates, directing people with depression and anxiety to Internet support groups cannot be recommended. On the other hand, online expressive writing seems to have potential, and its use for people with depression and anxiety warrants further investigation. TRIAL REGISTRATION: TRIAL REGISTRATION: Clinicaltrials.gov NCT01149265; https://clinicaltrials.gov/ct2/show/NCT01149265 (Archived by WebCite at http://www.webcitation.org/6hYISlNFT)

Faculty Attitudes Towards Integrating Technology and Innovation

Technological innovation is an important aspect of teaching and learning in the 21st century. This article examines faculty attitudes toward technology use in the classroom at one regional public university in the United States. Building on a faculty-led initiative to develop a Community of Practice for improving education, this study used a mixed-method approach of a faculty-developed, electronic survey to assess this topic. Findings from 72 faculty members revealed an overall positive stance toward technology in the classroom and the average faculty member utilized about six technology tools in their courses. The opportunities, barriers and future uses for technologies in the higher education classroom emerged from the open-ended questions on the survey. One finding of particular concern is that faculty are fearful that technology causes a loss of the humanistic perspective in education. The university is redesigning ten of its most popular courses to increase flexibility, accessibility and student success