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Analysis of technological state of the art in e-mental health for the prevention and treatment of depression

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posted on 19.08.2019 by Franziska Burger
This dataset contains all data and analysis scripts pertaining to the research conducted for the JMIR paper: “Technological State of the Art of E-mental Health Interventions for Major Depressive Disorder: A Systematic Literature Review.” In the scope of the research conducted and described in this paper, we have developed a relational database to systematically describe e-mental health systems for the prevention and treatment of Major Depressive Disorder (MDD). For the purpose of creating this database, literature had to be retrieved from PubMed, Scopus, and Web of Knowledge and filtered for inclusion and exclusion based on title, abstract, and full-paper. Samples of records at each stage were double coded. Once the final body of literature was identified, information from the papers had to be extracted (coded) and entered into the database. Four of the database attributes were selected to be double coded again on samples. Furthermore, a set of scales was developed of which we assessed concurrent validity. However, the main contribution of this paper is an analysis of the state of the art of the field. To this end, we examine three research questions: (1) What types of systems exist? (2) How technologically advanced are these systems? (3) How has the system landscape evolved between 2000 and 2017? We here deliver: 1. eHealth4MDD database: the version of the database as it was used in the analyses conducted for the paper 2. reliability and validity analyses: all information pertaining to the assessment of reliability of the main rater and the assessment of the validity of the five scales including: -ratings as assigned by the first rater -ratings as assigned by the second raters -analysis scripts to determine reliability and validity 3. analyses to determine the technological state of the art of the field.





Brinkman, W.P. (Willem-Paul) [orcid:0000-0001-8485-7092]; Neerincx, M.A. (Mark) [orcid:0000-0002-8161-5722]; TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems


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