Gunther Eysenbach

Bio: Gunther Eysenbach is an academic researcher from University of Toronto. The author has contributed to research in topics: The Internet & eHealth. The author has an hindex of 51, co-authored 144 publications receiving 25273 citations. Previous affiliations of Gunther Eysenbach include Toronto General Hospital & University of Erlangen-Nuremberg.

Improving the Quality of Web Surveys: The Checklist for Reporting Results of Internet E-Surveys (CHERRIES)

Abstract: An error in the CHERRIES statement has been corrected (J Med Internet Res 2004;6[3]:e34). In the original paper, in table 1, denominator and numerator were flipped in the recommendations on how response rates (view rate, participation rate, and completion rate) should be calculated. The view rate should be the ratio of unique survey visitors divided by unique site visitors. The participation rate should be the ratio of those who agreed to participate divided by unique first survey page visitors. The completion rate is the ratio of the number of people who finished the survey divided by those who agreed to participate. The corrections have been made in the table in both columns. [J Med Internet Res 2012;14(1):e8]

The Law of Attrition

Abstract: In an ongoing effort of this Journal to develop and further the theories, models, and best practices around eHealth research, this paper argues for the need for a “science of attrition”, that is, a need to develop models for discontinuation of eHealth applications and the related phenomenon of participants dropping out of eHealth trials. What I call “law of attrition” here is the observation that in any eHealth trial a substantial proportion of users drop out before completion or stop using the appplication. This feature of eHealth trials is a distinct characteristic compared to, for example, drug trials. The traditional clinical trial and evidence-based medicine paradigm stipulates that high dropout rates make trials less believable. Consequently eHealth researchers tend to gloss over high dropout rates, or not to publish their study results at all, as they see their studies as failures. However, for many eHealth trials, in particular those conducted on the Internet and in particular with self-help applications, high dropout rates may be a natural and typical feature. Usage metrics and determinants of attrition should be highlighted, measured, analyzed, and discussed. This also includes analyzing and reporting the characteristics of the subpopulation for which the application eventually “works”, ie, those who stay in the trial and use it. For the question of what works and what does not, such attrition measures are as important to report as pure efficacy measures from intention-to-treat (ITT) analyses. In cases of high dropout rates efficacy measures underestimate the impact of an application on a population which continues to use it. Methods of analyzing attrition curves can be drawn from survival analysis methods, eg, the Kaplan-Meier analysis and proportional hazards regression analysis (Cox model). Measures to be reported include the relative risk of dropping out or of stopping the use of an application, as well as a “usage half-life”, and models reporting demographic and other factors predicting usage discontinuation in a population. Differential dropout or usage rates between two interventions could be a standard metric for the “usability efficacy” of a system. A “run-in and withdrawal” trial design is suggested as a methodological innovation for Internet-based trials with a high number of initial dropouts/nonusers and a stable group of hardcore users. [J Med Internet Res 2005;7(1):e11]

What is e-health?

Abstract: Everybody talks about e-health these days, but few people have come up with a clear definition of this comparatively new term. Barely in use before 1999, this term now seems to serve as a general "buzzword," used to characterize not only "Internet medicine", but also virtually everything related to computers and medicine. The term was apparently first used by industry leaders and marketing people rather than academics. They created and used this term in line with other "e-words" such as e-commerce, e-business, e-solutions, and so on, in an attempt to convey the promises, principles, excitement (and hype) around e-commerce (electronic commerce) to the health arena, and to give an account of the new possibilities the Internet is opening up to the area of health care. Intel, for example, referred to e-health as "a concerted effort undertaken by leaders in health care and hi-tech industries to fully harness the benefits available through convergence of the Internet and health care." Because the Internet created new opportunities and challenges to the traditional health care information technology industry, the use of a new term to address these issues seemed appropriate. These "new" challenges for the health care information technology industry were mainly (1) the capability of consumers to interact with their systems online (B2C = "business to consumer"); (2) improved possibilities for institutionto-institution transmissions of data (B2B = "business to business"); (3) new possibilities for peerto-peer communication of consumers (C2C = "consumer to consumer").

How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews

Abstract: Objectives: To describe techniques for retrieval and appraisal used by consumers when they search for health information on the internet. Design: Qualitative study using focus groups, naturalistic observation of consumers searching the world wide web in a usability laboratory, and in-depth interviews. Participants: A total of 21 users of the internet participated in three focus group sessions. 17 participants were given a series of health questions and observed in a usability laboratory setting while retrieving health information from the web; this was followed by in-depth interviews. Setting: Heidelberg, Germany. Results: Although their search technique was often suboptimal, internet users successfully found health information to answer questions in an average of 5 minutes 42 seconds (median 4 minutes 18 seconds) per question. Participants in focus groups said that when assessing the credibility of a website they primarily looked for the source, a professional design, a scientific or official touch, language, and ease of use. However, in the observational study, no participants checked any “about us” sections of websites, disclaimers, or disclosure statements. In the post-search interviews, it emerged that very few participants had noticed and remembered which websites they had retrieved information from. Conclusions: Further observational studies are needed to design and evaluate educational and technological innovations for guiding consumers to high quality health information on the web. What is already known on this topic Little is known about how consumers retrieve and assess the quality of health information on the internet Qualitative data are needed to design educational and technological innovations to guide consumers to high quality health information What this study adds Users of the internet explore only the first few links on general search engines when seeking health information Consumers say that when assessing the credibility of a site they primarily look for the source, a professional design, and a variety of other criteria In practice, internet users do not check the “about us” sections of websites, try to find out who authors or owners of the site are, or read disclaimers or disclosure statements Very few internet users later remember from which websites they retrieved information or who stood behind the sites

Empirical Studies Assessing the Quality of Health Information for Consumers on the World Wide Web: A Systematic Review

Abstract: ContextThe quality of consumer health information on the World Wide Web is an important issue for medicine, but to date no systematic and comprehensive synthesis of the methods and evidence has been performed.ObjectivesTo establish a methodological framework on how quality on the Web is evaluated in practice, to determine the heterogeneity of the results and conclusions, and to compare the methodological rigor of these studies, to determine to what extent the conclusions depend on the methodology used, and to suggest future directions for research.Data SourcesWe searched MEDLINE and PREMEDLINE (1966 through September 2001), Science Citation Index (1997 through September 2001), Social Sciences Citation Index (1997 through September 2001), Arts and Humanities Citation Index (1997 through September 2001), LISA (1969 through July 2001), CINAHL (1982 through July 2001), PsychINFO (1988 through September 2001), EMBASE (1988 through June 2001), and SIGLE (1980 through June 2001). We also conducted hand searches, general Internet searches, and a personal bibliographic database search.Study SelectionWe included published and unpublished empirical studies in any language in which investigators searched the Web systematically for specific health information, evaluated the quality of Web sites or pages, and reported quantitative results. We screened 7830 citations and retrieved 170 potentially eligible full articles. A total of 79 distinct studies met the inclusion criteria, evaluating 5941 health Web sites and 1329 Web pages, and reporting 408 evaluation results for 86 different quality criteria.Data ExtractionTwo reviewers independently extracted study characteristics, medical domains, search strategies used, methods and criteria of quality assessment, results (percentage of sites or pages rated as inadequate pertaining to a quality criterion), and quality and rigor of study methods and reporting.Data SynthesisMost frequently used quality criteria used include accuracy, completeness, readability, design, disclosures, and references provided. Fifty-five studies (70%) concluded that quality is a problem on the Web, 17 (22%) remained neutral, and 7 studies (9%) came to a positive conclusion. Positive studies scored significantly lower in search (P = .02) and evaluation (P = .04) methods.ConclusionsDue to differences in study methods and rigor, quality criteria, study population, and topic chosen, study results and conclusions on health-related Web sites vary widely. Operational definitions of quality criteria are needed.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

Abstract: Since 1980, the American College of Cardiology (ACC) and American Heart Association (AHA) have translated scientific evidence into clinical practice guidelines (guidelines) with recommendations to improve cardiovascular health. In 2013, the National Heart, Lung, and Blood Institute (NHLBI) Advisory

Health literacy : a prescription to end confusion

Abstract: To maintain their own health and the health of their families and communities, consumers rely heavily on the health information that is available to them. This information is at the core of the partnerships that patients and their families forge with today?s complex modern health systems. This information may be provided in a variety of forms ? ranging from a discussion between a patient and a health care provider to a health promotion advertisement, a consent form, or one of many other forms of health communication common in our society. Yet millions of Americans cannot understand or act upon this information. To address this problem, the field of health literacy brings together research and practice from diverse fields including education, health services, and social and cultural sciences, and the many organizations whose actions can improve or impede health literacy. Health Literacy: Prescription to End Confusion examines the body of knowledge that applies to the field of health literacy, and recommends actions to promote a health literate society. By examining the extent of limited health literacy and the ways to improve it, we can improve the health of individuals and populations.

Detecting influenza epidemics using search engine query data

Abstract: This paper - first published on-line in November 2008 - draws on data from an early version of the Google Flu Trends search engine to estimate the levels of flu in a population. It introduces a computational model that converts raw search query data into a region-by-region real-time surveillance system that accurately estimates influenza activity with a lag of about one day - one to two weeks faster than the conventional reports published by the Centers for Disease Prevention and Control. This report introduces a computational model based on internet search queries for real-time surveillance of influenza-like illness (ILI), which reproduces the patterns observed in ILI data from the Centers for Disease Control and Prevention. Seasonal influenza epidemics are a major public health concern, causing tens of millions of respiratory illnesses and 250,000 to 500,000 deaths worldwide each year1. In addition to seasonal influenza, a new strain of influenza virus against which no previous immunity exists and that demonstrates human-to-human transmission could result in a pandemic with millions of fatalities2. Early detection of disease activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza3,4. One way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines, which are submitted by millions of users around the world each day. Here we present a method of analysing large numbers of Google search queries to track influenza-like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms, we can accurately estimate the current level of weekly influenza activity in each region of the United States, with a reporting lag of about one day. This approach may make it possible to use search queries to detect influenza epidemics in areas with a large population of web search users.