Objective Internet‐based guided self‐help (GSH) programs increase accessibility and utilization of evidence‐based treatments in binge‐eating disorder (BED). We evaluated acceptance and short as well as long‐term efficacy of our 8‐session internet‐based GSH program in a randomized clinical trial with an immediate treatment group, and two waitlist control groups, which differed with respect to whether patients received positive expectation induction during waiting or not. Method Sixty‐three patients (87% female, mean age 37.2 years) followed the eight‐session guided cognitive‐behavioural internet‐based program and three booster sessions in a randomized clinical trial design including an immediate treatment and two waitlist control conditions. Outcomes were treatment acceptance, number of weekly binge‐eating episodes, eating disorder pathology, depressiveness, and level of psychosocial functioning. Results Treatment satisfaction was high, even though 27% of all patients dropped out during the active treatment and 9.5% during the follow‐up period of 6 months. The treatment, in contrast to the waiting conditions, led to a significant reduction of weekly binge‐eating episodes from 3.4 to 1.7 with no apparent rebound effect during follow‐up. All other outcomes improved as well during active treatment. Email‐based positive expectation induction during waiting period prior to the treatment did not have an additional beneficial effect on the temporal course and thus treatment success, of binge episodes in this study. Conclusion This short internet‐based program was clearly accepted and highly effective regarding core features of BED. Dropout rates were higher in the active and lower in the follow‐up period. Positive expectations did not have an impact on treatment effects.

Background Binge-eating disorder (BED) is characterized by recurrent episodes of loss of control over eating and is related to a higher prevalence of other mental disorders and somatic consequences associated with overweight and obesity. In community-based samples, 2–4% of women and 1–3% men are diagnosed with BED. Psychotherapeutic interventions focusing on maintenance factors of disturbed eating behavior have proven to be effective. However, treatment access is limited for a considerable number of patients with BED. A lack of specialized institutions and treatment resources, but also long distances to treatment facilities for people living in remote or rural areas are often causes of insufficient care. Internet-based guided self-help (GSH) programs have the potential to fill this gap. Methods This project aims to develop and evaluate an Internet-based treatment for BED derived from an evidence-based manualized cognitive behavioral therapy (CBT). The primary goal is to test feasibility and suitability of the Internet-based program and to evaluate the treatment outcome in comparison to a pure and a placebo-inspired waitlist control group (i.e. reduction of binge-eating episodes and eating disorder pathology as primary outcome variables). In total, 60 women and men aged 18–70 years with a BED diagnosis will be recruited. The Internet-based GSH treatment comprises eight sessions followed by three booster sessions. The placebo-inspired waitlist control group receives weekly messages containing information increasing positive expectations regarding the treatment effects during the four-week waiting period. The pure waitlist control group receives weekly messages simply asking patients to fill in a short questionnaire. Discussion The access to evidence-based treatments for BED might be made easier using an Internet-based GSH approach. The present study protocol presents a randomized controlled trial. As well as evaluating the suitability and efficacy of the Internet-based GSH treatment, there will also be a prelimarily investigation on the influence of positive expectations (placebo) for a therapeutic intervention on core symptoms. Trial registration German Clinical Trials Register, DRKS00012355 . Registered on 14 September 2017.

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Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive "subject-verb-complement" set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of the limitations, the current state of the method and the upcoming steps for this annotation interface.