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		<title>Top 10
		Most Cited in Google Scholar
				JMIR Articles
				(All Time)
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	                                <item>
                    <title>What is e-health?</title>
                    <description><![CDATA[No Abstract Available<br /><br />
															Google Scholar Citations: 1457
									
		    ]]></description>


                                    <link>https://www.jmir.org/2001/2/e20</link>
                    <pubDate>Mon, 18 Jun 2001 00:00:00 EDT</pubDate>
                    <guid>https://www.jmir.org/2001/2/e20</guid>
                                </item>
                                        <item>
                    <title>The Law of Attrition</title>
                    <description><![CDATA[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 &#8220;science of attrition&#8221;, 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 &#8220;law of attrition&#8221; 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 &#8220;works&#8221;, 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 &#8220;usage half-life&#8221;, 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 &#8220;usability efficacy&#8221; of a system. A &#8220;run-in and withdrawal&#8221; 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.

<br /><br />
															Google Scholar Citations: 958
									
		    ]]></description>


                                    <link>https://www.jmir.org/2005/1/e11</link>
                    <pubDate>Thu, 31 Mar 2005 00:00:00 EST</pubDate>
                    <guid>https://www.jmir.org/2005/1/e11</guid>
                                </item>
                                        <item>
                    <title>Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness</title>
                    <description><![CDATA[In a very significant development for eHealth, a broad adoption of Web 2.0 technologies and approaches coincides with the more recent emergence of Personal Health Application Platforms and Personally Controlled Health Records such as Google Health, Microsoft HealthVault, and Dossia. “Medicine 2.0” applications, services and tools are defined as Web-based services for health care consumers, caregivers, patients, health professionals, and biomedical researchers, that use Web 2.0 technologies and/or semantic web and virtual reality approaches to enable and facilitate specifically 1) social networking, 2) participation, 3) apomediation, 4) openness and 5) collaboration, within and between these user groups. The Journal of Medical Internet Research (JMIR) publishes a Medicine 2.0 theme issue and sponsors a conference on “How Social Networking and Web 2.0 changes Health, Health Care, Medicine and Biomedical Research”, to stimulate and encourage research in these five areas.<br /><br />
															Google Scholar Citations: 779
									
		    ]]></description>


                                    <link>https://www.jmir.org/2008/3/e22</link>
                    <pubDate>Mon, 25 Aug 2008 18:23:14 EDT</pubDate>
                    <guid>https://www.jmir.org/2008/3/e22</guid>
                                </item>
                                        <item>
                    <title>The Internet and evidence-based decision-making: a needed synergy for efficient knowledge management in health care</title>
                    <description><![CDATA[No Abstract Available<br /><br />
															Google Scholar Citations: 766
									
		    ]]></description>


                                    <link>https://www.jmir.org/2000/suppl2/e2</link>
                    <pubDate>Wed, 13 Sep 2000 00:00:00 EDT</pubDate>
                    <guid>https://www.jmir.org/2000/suppl2/e2</guid>
                                </item>
                                        <item>
                    <title>Evidence-based Patient Choice and Consumer health informatics in the Internet age</title>
                    <description><![CDATA[No Abstract Available<br /><br />
															Google Scholar Citations: 760
									
		    ]]></description>


                                    <link>https://www.jmir.org/2001/2/e19</link>
                    <pubDate>Thu, 07 Jun 2001 00:00:00 EDT</pubDate>
                    <guid>https://www.jmir.org/2001/2/e19</guid>
                                </item>
                                        <item>
                    <title>Using the Internet to Promote Health Behavior Change: A Systematic Review and Meta-analysis of the Impact of Theoretical Basis, Use of Behavior Change Techniques, and Mode of Delivery on Efficacy</title>
                    <description><![CDATA[Background: The Internet is increasingly used as a medium for the delivery of interventions designed to promote health behavior change. However, reviews of these interventions to date have not systematically identified intervention characteristics and linked these to effectiveness. Objectives:  The present review sought to capitalize on recently published coding frames for assessing use of theory and behavior change techniques to investigate which characteristics of Internet-based interventions best promote health behavior change. In addition, we wanted to develop a novel coding scheme for assessing mode of delivery in Internet-based interventions and also to link different modes to effect sizes. Methods: We conducted a computerized search of the databases indexed by ISI Web of Knowledge (including BIOSIS Previews and Medline) between 2000 and 2008. Studies were included if (1) the primary components of the intervention were delivered via the Internet, (2) participants were randomly assigned to conditions, and (3) a measure of behavior related to health was taken after the intervention. Results:  We found 85 studies that satisfied the inclusion criteria, providing a total sample size of 43,236 participants. On average, interventions had a statistically small but significant effect on health-related behavior (d+ = 0.16, 95% CI 0.09-0.23). More extensive use of theory was associated with increases in effect size (P = .049), and, in particular, interventions based on the theory of planned behavior tended to have substantial effects on behavior (d+ = 0.36, 95% CI 0.15-0.56). Interventions that incorporated more behavior change techniques also tended to have larger effects compared to interventions that incorporated fewer techniques (P &#60; .001). Finally, the effectiveness of Internet-based interventions was enhanced by the use of additional methods of communicating with participants, especially the use of short message service (SMS), or text, messages. Conclusions: The review provides a framework for the development of a science of Internet-based interventions, and our findings provide a rationale for investing in more intensive theory-based interventions that incorporate multiple behavior change techniques and modes of delivery. <br /><br />
															Google Scholar Citations: 750
									
		    ]]></description>


                                    <link>https://www.jmir.org/2010/1/e4</link>
                    <pubDate>Wed, 17 Feb 2010 13:03:11 EST</pubDate>
                    <guid>https://www.jmir.org/2010/1/e4</guid>
                                </item>
                                        <item>
                    <title>The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes</title>
                    <description><![CDATA[BACKGROUND: A primary focus of self-care interventions for chronic illness is the encouragement of an individual's behavior change necessitating knowledge sharing, education, and understanding of the condition. The use of the Internet to deliver Web-based interventions to patients is increasing rapidly. In a 7-year period (1996 to 2003), there was a 12-fold increase in MEDLINE citations for &#8220;Web-based therapies.&#8221; The use and effectiveness of Web-based interventions to encourage an individual's change in behavior compared to non-Web-based interventions have not been substantially reviewed.

OBJECTIVE: This meta-analysis was undertaken to provide further information on patient/client knowledge and behavioral change outcomes after Web-based interventions as compared to outcomes seen after implementation of non-Web-based interventions.

METHODS: The MEDLINE, CINAHL, Cochrane Library, EMBASE, ERIC, and PSYCHInfo databases were searched for relevant citations between the years 1996 and 2003. Identified articles were retrieved, reviewed, and assessed according to established criteria for quality and inclusion/exclusion in the study. Twenty-two articles were deemed appropriate for the study and selected for analysis. Effect sizes were calculated to ascertain a standardized difference between the intervention (Web-based) and control (non-Web-based) groups by applying the appropriate meta-analytic technique. Homogeneity analysis, forest plot review, and sensitivity analyses were performed to ascertain the comparability of the studies.

RESULTS: Aggregation of participant data revealed a total of 11,754 participants (5,841 women and 5,729 men). The average age of participants was 41.5 years. In those studies reporting attrition rates, the average drop out rate was 21% for both the intervention and control groups. For the five Web-based studies that reported usage statistics, time spent/session/person ranged from 4.5 to 45 minutes. Session logons/person/week ranged from 2.6 logons/person over 32 weeks to 1008 logons/person over 36 weeks. The intervention designs included one-time Web-participant health outcome studies compared to non-Web participant health outcomes, self-paced interventions, and longitudinal, repeated measure intervention studies. Longitudinal studies ranged from 3 weeks to 78 weeks in duration. The effect sizes for the studied outcomes ranged from -.01 to .75. Broad variability in the focus of the studied outcomes precluded the calculation of an overall effect size for the compared outcome variables in the Web-based compared to the non-Web-based interventions. Homogeneity statistic estimation also revealed widely differing study parameters (Qw16 = 49.993, P &#8804; .001). There was no significant difference between study length and effect size. Sixteen of the 17 studied effect outcomes revealed improved knowledge and/or improved behavioral outcomes for participants using the Web-based interventions. Five studies provided group information to compare the validity of Web-based vs. non-Web-based instruments using one-time cross-sectional studies. These studies revealed effect sizes ranging from -.25 to +.29. Homogeneity statistic estimation again revealed widely differing study parameters (Qw4 = 18.238, P &#8804; .001).

CONCLUSIONS: The effect size comparisons in the use of Web-based interventions compared to non-Web-based interventions showed an improvement in outcomes for individuals using Web-based interventions to achieve the specified knowledge and/or behavior change for the studied outcome variables. These outcomes included increased exercise time, increased knowledge of nutritional status, increased knowledge of asthma treatment, increased participation in healthcare, slower health decline, improved body shape perception, and 18-month weight loss maintenance.

<br /><br />
															Google Scholar Citations: 524
									
		    ]]></description>


                                    <link>https://www.jmir.org/2004/4/e40</link>
                    <pubDate>Wed, 10 Nov 2004 00:00:00 EST</pubDate>
                    <guid>https://www.jmir.org/2004/4/e40</guid>
                                </item>
                                        <item>
                    <title>Social Media Use in the United States: Implications for Health Communication</title>
                    <description><![CDATA[Background:  Given the rapid changes in the communication landscape brought about by participative Internet use and social media, it is important to develop a better understanding of these technologies and their impact on health communication. The first step in this effort is to identify the characteristics of current social media users. Up-to-date reporting of current social media use will help monitor the growth of social media and inform health promotion/communication efforts aiming to effectively utilize social media. Objective:  The purpose of the study is to identify the sociodemographic and health-related factors associated with current adult social media users in the United States. Methods:  Data came from the 2007 iteration of the Health Information National Trends Study (HINTS, N = 7674). HINTS is a nationally representative cross-sectional survey on health-related communication trends and practices. Survey respondents who reported having accessed the Internet (N = 5078) were asked whether, over the past year, they had (1) participated in an online support group, (2) written in a blog, (3) visited a social networking site. Bivariate and multivariate logistic regression analyses were conducted to identify predictors of each type of social media use. Results:  Approximately 69% of US adults reported having access to the Internet in 2007. Among Internet users, 5% participated in an online support group, 7% reported blogging, and 23% used a social networking site. Multivariate analysis found that younger age was the only significant predictor of blogging and social networking site participation; a statistically significant linear relationship was observed, with younger categories reporting more frequent use. Younger age, poorer subjective health, and a personal cancer experience predicted support group participation. In general, social media are penetrating the US population independent of education, race/ethnicity, or health care access. Conclusions:  Recent growth of social media is not uniformly distributed across age groups; therefore, health communication programs utilizing social media must first consider the age of the targeted population to help ensure that messages reach the intended audience. While racial/ethnic and health status&#8211;related disparities exist in Internet access, among those with Internet access, these characteristics do not affect social media use. This finding suggests that the new technologies, represented by social media, may be changing the communication pattern throughout the United States. <br /><br />
															Google Scholar Citations: 510
									
		    ]]></description>


                                    <link>https://www.jmir.org/2009/4/e48</link>
                    <pubDate>Fri, 27 Nov 2009 11:24:54 EST</pubDate>
                    <guid>https://www.jmir.org/2009/4/e48</guid>
                                </item>
                                        <item>
                    <title>Using the Internet for Surveys and Health Research</title>
                    <description><![CDATA[No Abstract Available<br /><br />
															Google Scholar Citations: 472
									
		    ]]></description>


                                    <link>https://www.jmir.org/2002/2/e13</link>
                    <pubDate>Fri, 22 Nov 2002 00:00:00 EST</pubDate>
                    <guid>https://www.jmir.org/2002/2/e13</guid>
                                </item>
                                        <item>
                    <title>What Is eHealth (3): A Systematic Review of Published Definitions</title>
                    <description><![CDATA[CONTEXT: The term eHealth is widely used by many individuals, academic institutions, professional bodies, and funding organizations. It has become an accepted neologism despite the lack of an agreed-upon clear or precise definition. We believe that communication among the many individuals and organizations that use the term could be improved by comprehensive data about the range of meanings encompassed by the term.

OBJECTIVE: To report the results of a systematic review of published, suggested, or proposed definitions of eHealth.

DATA SOURCES: Using the search query string &#8220;eHealth&#8221; OR &#8220;e-Health&#8221; OR &#8220;electronic health&#8221;, we searched the following databases: Medline and Premedline (1966-June 2004), EMBASE (1980-May 2004), International Pharmaceutical Abstracts (1970-May 2004), Web of Science (all years), Information Sciences Abstracts (1966-May 2004), Library Information Sciences Abstracts (1969-May 2004), and Wilson Business Abstracts (1982-March 2004). In addition, we searched dictionaries and an Internet search engine.

STUDY SELECTION: We included any source published in either print format or on the Internet, available in English, and containing text that defines or attempts to define eHealth in explicit terms. Two of us independently reviewed titles and abstracts of citations identified in the bibliographic databases and Internet search, reaching consensus on relevance by discussion. 

DATA EXTRACTION: We retrieved relevant reports, articles, references, letters, and websites containing definitions of eHealth. Two of us qualitatively analyzed the definitions and coded them for content, emerging themes, patterns, and novel ideas.

DATA SYNTHESIS: The 51 unique definitions that we retrieved showed a wide range of themes, but no clear consensus about the meaning of the term eHealth. We identified 2 universal themes (health and technology) and 6 less general (commerce, activities, stakeholders, outcomes, place, and perspectives).

CONCLUSIONS: The widespread use of the term eHealth suggests that it is an important concept, and that there is a tacit understanding of its meaning. This compendium of proposed definitions may improve communication among the many individuals and organizations that use the term.

<br /><br />
															Google Scholar Citations: 449
									
		    ]]></description>


                                    <link>https://www.jmir.org/2005/1/e1</link>
                    <pubDate>Thu, 24 Feb 2005 00:00:00 EST</pubDate>
                    <guid>https://www.jmir.org/2005/1/e1</guid>
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