Top 10 Most Cited in Google Scholar JMIR Articles(All Time)

What is e-health?

Mon, 18 Jun 2001 00:00:00 EDT

No Abstract Available

Google Scholar Citations: 502

The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes

Wed, 10 Nov 2004 00:00:00 EST

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 “Web-based therapies.” 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 ≤ .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 ≤ .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.

Google Scholar Citations: 340

The Law of Attrition

Thu, 31 Mar 2005 00:00:00 EST

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.

Google Scholar Citations: 283

Using the Internet for Surveys and Health Research

Fri, 22 Nov 2002 00:00:00 EST

No Abstract Available

Google Scholar Citations: 243

Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness

Mon, 25 Aug 2008 18:23:14 EDT

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.

Google Scholar Citations: 240

Evidence-based Patient Choice and Consumer health informatics in the Internet age

Thu, 07 Jun 2001 00:00:00 EDT

No Abstract Available

Google Scholar Citations: 186

The Internet and evidence-based decision-making: a needed synergy for efficient knowledge management in health care

Wed, 13 Sep 2000 00:00:00 EDT

No Abstract Available

Google Scholar Citations: 186

Internet Versus Mailed Questionnaires: A Randomized Comparison

Wed, 15 Sep 2004 00:00:00 EDT

BACKGROUND: The use of Internet-based questionnaires for collection of data to evaluate patient education and other interventions has increased in recent years. Many self-report instruments have been validated using paper-and-pencil versions, but we cannot assume that the psychometric properties of an Internet-based version will be identical. OBJECTIVES: To look at similarities and differences between the Internet versions and the paper-and-pencil versions of 16 existing self-report instruments useful in evaluation of patient interventions. METHODS: Participants were recruited via the Internet and volunteered to participate (N=397), after which they were randomly assigned to fill out questionnaires online or via mailed paper-and-pencil versions. The self-report instruments measured were overall health, health distress, practice mental stress management, Health Assessment Questionnaire (HAQ) disability, illness intrusiveness, activity limitations, visual numeric for pain, visual numeric for shortness of breath, visual numeric for fatigue, self-efficacy for managing disease, aerobic exercise, stretching and strengthening exercise, visits to MD, hospitalizations, hospital days, and emergency room visits. Means, ranges, and confidence intervals are given for each instrument within each type of questionnaire. The results from the two questionnaires were compared using both parametric and non-parametric tests. Reliability tests were given for multi-item instruments. A separate sample (N=30) filled out identical questionnaires over the Internet within a few days and correlations were used to assess test-retest reliability. RESULTS: Out of 16 instruments, none showed significant differences when the appropriate tests were used. Construct reliability was similar within each type of questionnaire, and Internet test-retest reliability was high. Internet questionnaires required less follow-up to achieve a slightly (non-significant) higher completion rate compared to mailed questionnaires. CONCLUSIONS: Among a convenience sample recruited via the Internet, results from those randomly assigned to Internet participation were at least as good as, if not better than, among those assigned mailed questionnaires, with less recruitment effort required. The instruments administered via the Internet appear to be reliable, and to be answered similarly to the way they are answered when they are administered via traditional mailed paper questionnaires.

Google Scholar Citations: 179

Overcoming Depression on the Internet (ODIN) (2): A Randomized Trial of a Self-Help Depression Skills Program With Reminders

Tue, 21 Jun 2005 00:00:00 EDT

BACKGROUND: Guided self-help programs for depression (with associated therapist contact) have been successfully delivered over the Internet. However, previous trials of pure self-help Internet programs for depression (without therapist contact), including an earlier trial conducted by us, have failed to yield positive results. We hypothesized that methods to increase participant usage of the intervention, such as postcard or telephone reminders, might result in significant effects on depression. OBJECTIVES: This paper presents a second randomized trial of a pure self-help Internet site, ODIN (Overcoming Depression on the InterNet), for adults with self-reported depression. We hypothesized that frequently reminded participants receiving the Internet program would report greater reduction in depression symptoms and greater improvements in mental and physical health functioning than a comparison group with usual treatment and no access to ODIN. METHODS: This was a three-arm randomized control trial with a usual treatment control group and two ODIN intervention groups receiving reminders through postcards or brief telephone calls. The setting was a nonprofit health maintenance organization (HMO). We mailed recruitment brochures by US post to two groups: adults (n = 6030) who received depression medication or psychotherapy in the previous 30 days, and an age- and gender-matched group of adults (n = 6021) who did not receive such services. At enrollment and at 5-, 10- and 16-weeks follow-up, participants were reminded by email (and telephone, if nonresponsive) to complete online versions of the Center for Epidemiological Studies Depression Scale (CES-D) and the Short Form 12 (SF-12). We also recorded participant HMO health care services utilization in the 12 months following study enrollment. RESULTS: Out of a recruitment pool of 12051 approached subjects, 255 persons accessed the Internet enrollment site, completed the online consent form, and were randomized to one of the three groups: (1) treatment as usual control group without access to the ODIN website (n = 100), (2) ODIN program group with postcard reminders (n = 75), and (3) ODIN program group with telephone reminders (n = 80). Across all groups, follow-up completion rates were 64% (n = 164) at 5 weeks, 68% (n = 173) at 10 weeks, and 66% (n = 169) at 16 weeks. In an intention-to-treat analysis, intervention participants reported greater reductions in depression compared to the control group (P = .03; effect size = 0.277 standard deviation units). A more pronounced effect was detected among participants who were more severely depressed at baseline (P = .02; effect size = 0.537 standard deviation units). By the end of the study, 20% more intervention participants moved from the disordered to normal range on the CES-D. We found no difference between the two intervention groups with different reminders in outcomes measures or in frequency of log-ons. We also found no significant intervention effects on the SF-12 or health care services. CONCLUSIONS: In contrast to our earlier trial, in which participants were not reminded to use ODIN, in this trial we found a positive effect of the ODIN intervention compared to the control group. Future studies should address limitations of this trial, including relatively low enrollment and follow-up completion rates, and a restricted number of outcome measures. However, the low incremental costs of delivering this Internet program makes it feasible to offer this type of program to large populations with widespread Internet access.

Google Scholar Citations: 171

What Is eHealth (3): A Systematic Review of Published Definitions

Thu, 24 Feb 2005 00:00:00 EST

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 “eHealth” OR “e-Health” OR “electronic health”, 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.

Google Scholar Citations: 171