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      Predictors of Adherence by Adolescents to a Cognitive Behavior Therapy Website in School and Community-Based Settings

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          Abstract

          Background

          There have been no previous studies of the variables that predict adherence to online depression and anxiety intervention programs among adolescents. However, research of traditionally delivered intervention programs for a variety of health conditions in adolescence suggests that health knowledge, type and level of symptomatology, race, socioeconomic status, treatment setting, and support may predict adherence.

          Objective

          The aim was to compare adherence rates and identify the predictors of adherence to a cognitive behavior therapy website in two adolescent samples that were offered the program in different settings and under different conditions of support.

          Methods

          The first adolescent sample consisted of 1000 school students who completed the MoodGYM program in a classroom setting over five weeks as part of a randomized controlled trial. The second sample consisted of 7207 adolescents who accessed the MoodGYM program spontaneously and directly through the open access URL. All users completed a brief survey before the start of the program that measured background characteristics, depression history, symptoms of depression and anxiety, and dysfunctional thinking.

          Results

          Adolescents in the school-based sample completed significantly more online exercises (mean = 9.38, SD = 6.84) than adolescents in the open access community sample (mean = 3.10, SD = 3.85; t 1088.62 = −28.39, P < .001). A multiple linear regression revealed that school-based setting ( P < .001) and female gender ( P < .001) were predictive of greater adherence, as were living in a rural area ( P < .001) and lower pre-test anxiety ( P = .04) scores for the school-based sample and higher pre-test depression scores ( P = .01) for the community sample. A history of depression ( P = .33) and pre-test warpy thoughts scores ( P = .35) were not predictive of adherence in the school-based or community sample.

          Conclusion

          Adherence is greater in monitored settings, and the predictors of adherence differ between settings. Understanding these differences may improve program effectiveness and efficiency.

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          Most cited references14

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          Detecting anxiety and depression in general medical settings.

          To aid general practitioners and other non-psychiatrists in the better recognition of mental illness short scales measuring anxiety and depression were derived by latent trait analysis from a standardised psychiatric research interview. Designed to be used by non-psychiatrists, they provide dimensional measures of the severity of each disorder. The full set of nine questions need to be administered only if there are positive answers to the first four. When assessed against the full set of 60 questions contained in the psychiatric assessment schedule they had a specificity of 91% and a sensitivity of 86%. The scales would be used by non-psychiatrists in clinical investigations and possibly also by medical students to familiarise them with the common forms of psychiatric illness, which are often unrecognised in general medical settings.
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            The Law of Attrition Revisited

            Eysenbach’s Law of Attrition Revisited Early last year, Eysenbach published a paper [1] urging the need for a science of attrition. Rather surprisingly, there has not been much debate about the issues raised in the paper (this judged by a Web of Science citation search), despite the clear observation that attrition is a major problem in the use and evaluation of ehealth sites. This letter is an attempt to stimulate more discussion about this important issue. Eysenbach’s paper gives us three major conceptual advances – the distinction between trial dropout and low/nonusage nondropouts; the proposition of “diffusion of innovation” effectively reversed as a model for the take-up of Internet interventions; and the concept of the “Run in and Withdrawal Design.” The diffusion of innovation reversed is essentially a structural one in that it suggests a number of “systems” features that influence dropout and usage including expectation management, ease of ease of enrolment, ease of dropout, usability, adjunct personal contact, financial commitment, workload, competing events, and experience. User Characteristics and Preferences are Important A number of issues arise from Eysenbach’s proposal. First, the structural or systems model factors in the model may need to be supplemented by consideration of user characteristics. For example, the use and uptake of Web sites in mental health are likely to be modulated by the severity of the user’s mental health problem [2], the users need for anonymity (possibly arising from stigma), lack of alternative resources due to living in a remote location, and the preferences an individual might have for certain sorts of help. The potential impact of these factors in contributing to site adherence is something that needs to be recognized and, more than that, actually studied! There are a number of methods, which although indirect, can provide possible clues for further analysis. These include techniques such as correlating or predicting user characteristics with usage patterns and outcome measures. A second attribute of users that warrants incorporation in any model of nonusage is an understanding of the expectations that people bring to a Web site, and what they mean by their intention-to-use. For example, many young people do not recognize “lousy feelings” as depression or anxiety, but a brief visit to a Web site provides a “mini-diagnosis” and a label. For them, one module may well fulfill their needs: They have no expectation that they are lining up for a full set of modules. Recognizing these multiple paths and trajectories of web usage means that low usage and dropout do not necessarily coincide with “failure”. Dropouts may well be e-attainers [3]. The multiple uses made of Web sites by different users raises the distinct, but highly relevant issue of the suitability of the Internet to provide full treatment packages for different conditions. The Internet has the capacity to reach many individuals who may never seek formal treatment for mental health services. However, it may well be that the primary role of the Internet in disease prevention will be in the delivery of short positive health messages, rather than the delivery of ‘therapy’ that requires hours of online work. Web site adherence or “stickiness” may cease to be an issue for online sites like MoodGYM when shorter interventions can be demonstrated to lead to similar health outcomes and brief bursts of information lead to increased help-seeking. An Example The following is an example of how attrition may be influenced by the personal characteristics or the preferences of the online users. We are currently conducting a trial of MoodGYM in which those intending to use the program: (a) report that they have been asked to do the trial as a part of their clinician’s treatment plan; (b) chose to do five modules when offered the opportunity to do only fewer than five in the early part of MoodGYM; or (c) are randomized to the MoodGYM condition as a function of an ongoing trial. Figure 1 shows the completion rates of modules as a function of group membership. It is emerging that those who commit to undertake five modules do have a higher likelihood continuing to use the site, although attrition after the third module is almost complete in all groups. Figure 1 Completion rates of MoodGYM modules as a function of group membership Similarities and Differences with Clinical Trials We suggest that Eysenbach’s argument asserting the differences between e-health and traditional clinical trials might be slightly overstated. Many researchers who have been involved with traditional randomized controlled trials (RCTs) of pharmacological treatments in psychiatry will recognize Eysenbach’s characterization of attrition in such settings as extremely optimistic. The dropout of a third of recruited patients in such trials is common with rates exceeding 80% being observed in some long term trials aimed at relapse prevention. Determining whether patients have complied with medication regimes is difficult. In many respects, e-health is in a far stronger position than other studies to detail the low usage of the interventions, given the tracking of length and number of visits to the application. Moreover, e-health interventions have high fidelity: the exact same intervention is potentially available to all the participants. There are other minor points that need to be made. For example, Eysenbach makes a distinction between users lost to dropout and low usage nondropouts. This model suggests that people discontinue innovations because they are disenchanted or because they seek a better alternative to meet their needs. On reflection there are four theoretically possible usage curves: (i) dropout, low or no usage; (ii) nondropout, low or no usage; (iii) dropout, high usage; and (iv) nondropout, high usage. The dropout, high usage is a person who prefers not to engage with a Web site but undertakes the program under a new user name each visit (if the application is an open web-based one). Emerging Statistical Techniques Up until recently, interventions and clinical trials have been analyzed using classical analysis of variance methods. For these techniques, missing observations arising from participant dropout are a just nuisance factor which is addressed, a priori, by admonitions to minimize dropout [4] and, post hoc, by analysis of only those participants with complete data or by simplistic and often inappropriate methods of imputation. Mixed or random coefficient models are more recently developed methods that overcome problems due to missing data. These models operate under the assumption that the cause of dropout is measured as part of the available data rather than being contingent on the missing information itself (the missing at random assumption)[5]. These methods enable estimation of the effect of an intervention under the intention to treat model. Mixed models themselves throw little light on the nature of attrition, its causes or consequences. However, more advanced techniques, based on latent variable modeling, may help us understand the complexity of the multiplicity of paths through and of out interventions. The complier average causal (CACE) model is specially aimed at estimating the effect of an intervention in the presence of noncompliance [6]. Related techniques can be used to empirically delineate classes of response trajectories through and after an intervention [7]. These methods appear to be amenable to extension to accommodate attrition and to model causes of dropout. Beyond Attrition Developing a metric of the attrition attributable to an internet intervention site is an attractive initiative. It would parallel the notion of the acceptability or tolerability of conventional treatments. This concept, usually measured informally or only crudely, recognizes that some treatments, while efficacious, are possibly so odious as to be persevered with by only a few patients who might benefit from them. There are substantial hurdles to such measurements. It will prove difficult, if not impossible to disentangle attrition due to site effects from attrition due to the characteristics of users and the paths they take to a site. More important, to focus exclusive on attrition is to focus on the negative side of e-health interventions. E-health interventions have enormous potential to reach those warranting assistance and to address their needs. Recognizing the fact of high attrition, we need to respond with a science (and an art) of participation and encouragement.
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              Psychological and/or educational interventions for the prevention of depression in children and adolescents.

              Depression is the fourth most important disease in the estimation of the burden of disease Murray 1996 and is a common problem with prevalence rates estimated to be as high as 8% in young people. Depression in young people is associated with poor academic performance, social dysfunction, substance abuse, suicide attempts, and completed suicide (NHMRC 1997). This has precipitated the development of programmes aimed at preventing the onset of depression. This review evaluates evidence for the effectiveness of these prevention programmes. To determine whether psychological and/or educational interventions (both universal and targeted) are effective in reducing risk of depressive disorder by reducing depressive symptoms immediately after intervention or by preventing the onset of depressive disorder in children and adolescents over the next one to three years. The Cochrane Depression, Anxiety and Neurosis Group trials register (August 2002), MEDLINE (1966 to December Week 3 2002), EMBASE (1980 to January Week 2 2003), PsychInfo (1886 to January Week 2 2003) and ERIC (1985 to December 2002) were searched. In addition, conference abstracts, the reference lists of included studies, and other reviews were searched and experts in the field were contacted. Each identified study was assessed for possible inclusion by two independent reviewers based on the methods sections. The determinants for inclusion were that the trial include a psychological and/or educational prevention programme for young people aged 5 to 19 years-old, who did not meet DSM or ICD criteria for depression and/or did not fall into the clinical range on standardised, validated, and reliable rating scales of depression. The methodological quality of the included trials was assessed by two independent reviewers according to a list of pre-determined criteria, which were based on quality ratings devised by Moncrieff and colleagues (Moncrieff 2001). Outcome data was extracted and entered into Revman 4.2. Means and standard deviations for continuous outcomes and number of events for dichotomous outcomes were extracted where available. For trials where the required data were not reported or could not be calculated, further details were requested from first authors. If no further details were provided, the trial was included in the review and described, but not included in the meta-analysis. Results were presented for each type of intervention: targeted or universal interventions; and educational or psychological interventions and if data were provided, by gender. Where possible data were combined in meta-analyses to give a treatment effect across all trials. Sensitivity analysis were conducted on studies rated as "adequate" or "high" quality, that is with a score over 22, based on the scale by Moncrieff et al (Moncrieff 2001). The presence of publication bias was assessed using funnel plots. Studies were divided into those that compared intervention with an active comparison or placebo (i.e. a control condition that resembles the intervention being investigated but which lacks the elements thought to be active in preventing depression) and those that used a "wait-list" or no intervention comparison group. Only two studies fell into the former category and neither showed effectiveness although one study was inadequately powered to show a difference and in the other the "placebo" contained active therapeutic elements, reducing the ability to demonstrate a difference from intervention. Psychological interventions were effective compared with non-intervention immediately after the programmes were delivered with a significant reduction in scores on depression rating scales for targeted (standardised mean difference (SMD) of -0.26 and a 95% confidence interval (CI) of -0.40 to -0.13 ) but not universal interventions (SMD -0.21, 95% CI -0.48, 0.06), with a significant effect maintained on pooling data (SMD -0.26, 95% CI -0.36, -0.15). While small effect sizes were reported, these were associated with a significant reduction in depressive episodes. The overall risk difference after intervention translates to "numbers needed to treat" (NNT) of 10. The most effective study is the targeted programme by Clarke (Clarke 2001) where the initial effect size of -0.46 is associated with an initial risk difference of -0.22 and NNT 5. There was no evidence of effectiveness for educational interventions. Reports of effectiveness for boys and girls were contradictory. The quality of many studies was poor, and only two studies made allocation concealment explicit. Sensitivity analysis of only high quality studies did not alter the results significantly. The only analysis in which there was significant statistical heterogeneity was the sub-group analysis by gender where there was variability in the response to different programmes for both girls and boys. For the most part funnel plots indicate findings are robust for short term effects with no publication bias evident. There are too few studies to comment on whether there is publication bias for studies reporting long-term (12-36 month) follow-up. Although there is insufficient evidence to warrant the introduction of depression prevention programmes currently, results to date indicate that further study would be worthwhile. There is a need to compare interventions with a placebo or some sort of active comparison so that study participants do not know whether they are in the intervention group or not, to investigate the impact of booster sessions to see if effectiveness immediately after intervention can be prolonged, ideally for a year or longer, and to consider practical implementation of prevention programmes when choosing target populations. Until now most studies have focussed on psychological interventions. The potential effectiveness of educational interventions has not been fully investigated. Given the gender differences in prevalence, and the change in these that occurs in adolescence with a disproportionate increase in prevalence rates for girls, it is likely that girls and boys will respond differently to interventions. Although differences have been reported in studies in this review the findings are contradictory and a more definitive delineation of gender specific responses to interventions would be helpful.
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                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 2009
                23 February 2009
                : 11
                : 1
                : e6
                Affiliations
                [1] 1simpleCentre for Mental Health Research simpleThe Australian National University CanberraACTAustralia
                Article
                v11i1e6
                10.2196/jmir.1050
                2762770
                19275982
                b1bc9280-2439-4e70-bc13-043baff6e8a2
                © Alison L Neil, Philip Batterham, Helen Christensen, Kylie Bennett, Kathleen M Griffiths. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.02.2009.  

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 26 February 2008
                : 17 July 2008
                : 04 November 2008
                : 21 November 2008
                Categories
                Original Paper

                Medicine
                adolescent,prevention,anxiety disorders,mood disorders,patient non-adherence,internet
                Medicine
                adolescent, prevention, anxiety disorders, mood disorders, patient non-adherence, internet

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