Blog
About

246
views
0
recommends
+1 Recommend
0 collections
    13
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      The Law of Attrition

      , MD, MPH , 1

      (Reviewer)

      Journal of Medical Internet Research

      Gunther Eysenbach

      Internet, clinical trials, longitudinal studies, patient dropouts, survival analysis

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 8

          • Record: found
          • Abstract: not found
          • Article: not found

          Diffusion of innovations

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Delivering interventions for depression by using the internet: randomised controlled trial.

            To evaluate the efficacy of two internet interventions for community-dwelling individuals with symptoms of depression--a psychoeducation website offering information about depression and an interactive website offering cognitive behaviour therapy. Randomised controlled trial. Internet users in the community, in Canberra, Australia. 525 individuals with increased depressive symptoms recruited by survey and randomly allocated to a website offering information about depression (n = 166) or a cognitive behaviour therapy website (n = 182), or a control intervention using an attention placebo (n = 178). Change in depression, dysfunctional thoughts; knowledge of medical, psychological, and lifestyle treatments; and knowledge of cognitive behaviour therapy. Intention to treat analyses indicated that information about depression and interventions that used cognitive behaviour therapy and were delivered via the internet were more effective than a credible control intervention in reducing symptoms of depression in a community sample. For the intervention that delivered cognitive behaviour therapy the reduction in score on the depression scale of the Center for Epidemiologic Studies was 3.2 (95% confidence interval 0.9 to 5.4). For the "depression literacy" site (BluePages), the reduction was 3.0 (95% confidence interval 0.6 to 5.2). Cognitive behaviour therapy (MoodGYM) reduced dysfunctional thinking and increased knowledge of cognitive behaviour therapy. Depression literacy (BluePages) significantly improved participants' understanding of effective evidence based treatments for depression (P < 0.05). Both cognitive behaviour therapy and psychoeducation delivered via the internet are effective in reducing symptoms of depression.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A Comparison of Changes in Anxiety and Depression Symptoms of Spontaneous Users and Trial Participants of a Cognitive Behavior Therapy Website

              Background In randomized controlled trials Internet sites have been shown to be effective in the treatment of depression and anxiety. However, it is unclear if the positive effects demonstrated in these trials transfer to community users of such sites. Objective To compare anxiety and depression outcomes for spontaneous visitors to a publicly accessible cognitive behavior therapy website (MoodGYM) (http://moodgym.anu.edu.au) with outcomes achieved through a randomized controlled efficacy trial of the same site. Methods All community visitors to the MoodGYM site between April 2001 and September 2003 were sampled: 182 participants in the BlueMood Trial who had been randomly assigned to the MoodGYM site as part of a large trial and 19607 visitors (public registrants) to the site. Symptom assessments (quizzes) were repeated within the website intervention to allow the examination of change in symptoms across modules. Outcome variables were (1) age, gender, initial depression severity scores, and number of assessments attempted, and (2) symptom change measures based on Goldberg anxiety and depression scores recorded on a least two occasions. Results Public registrants did not differ from trial participants in gender, age, or initial level of depression, which was high for both groups relative to previously published epidemiological data sets. Trial participants completed more assessments. No significant differences in anxiety or depression change scores were observed, with both public registrants and trial participants improving through the training program. Conclusions Public registrants to a cognitive behavior therapy website show significant change in anxiety and depression symptoms. The extent of change does not differ from that exhibited by participants enrolled on the website for a randomized controlled trial.
                Bookmark

                Author and article information

                Contributors
                Journal
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                Gunther Eysenbach (Centre for Global eHealth Innovation, Toronto, Canada )
                1438-8871
                Jan-Mar 2005
                31 March 2005
                : 7
                : 1
                Affiliations
                1simpleCentre for Global eHealth Innovation simpleUniversity Health Network Toronto ONCanada
                Article
                v7i1e11
                10.2196/jmir.7.1.e11
                1550631
                15829473
                © Gunther Eysenbach. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 31.3.2005. Except where otherwise noted, articles published in the Journal of Medical Internet Research are distributed under the terms of the Creative Commons Attribution License (http://www.creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited, including full bibliographic details and the URL (see "please cite as" above), and this statement is included.
                Categories
                Viewpoint

                Medicine

                survival analysis, patient dropouts, longitudinal studies, clinical trials, internet

                Comments

                Comment on this article