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Original article | published - printed | peer reviewed

Zufallsbefunde: Risikobewusstsein von Probandinnen und forschungsethische Konsequenzen


Computer Methods and Programs in Biomedicine 2013 ; 110(3): 369 - 379


Bibliometric indicators


Impact Factor = 1.093

Citations (WOS) = 19


Authors

Erdmann P

Affiliations

fis.beans.InstitutionBean@34794e17


Abstract

A Central Data Management (CDM) system based on Electronic Data Capture (EDC) software and study specific databases is an essential component for assessment and management of large data volumes in epidemiologic studies. Conventional CDM systems using web applications for data capture depend on permanent access to a network. However, in many study settings permanent network access cannot be guaranteed, e.g. when participants/patients are visited in their homes. In such cases a different concept for data capture is needed. The utilized EDC software must be able to ensure data capture as stand-alone instance and to synchronize captured data with the server at a later point in time. This article describes the design of the Mobile Information Capture (MInCa) system an EDC software meeting these requirements. In particular, we focus on client and server design, data synchronization, and data privacy as well as data security measures. The MInCa software has already proven its efficiency in epidemiologic studies revealing strengths and weaknesses concerning both concept and practical application which will be addressed in this article.

Published in

Computer Methods and Programs in Biomedicine


Year 2013
Impact Factor (2013) 1.093
Volume 110
Issue 3
Pages 369 - 379
Open Access nein
Peer reviewed ja
Article type Original article
Article state published - printed

Common journal data

Short name: COMPUT METH PROG BIO
ISSN: 0169-2607
eISSN: 1872-7565
Country: NETHERLANDS
Language: English
Categories:
  • ENGINEERING, BIOMEDICAL
  • COMPUTER SCIENCE, THEORY & METHODS
  • MEDICAL INFORMATICS
  • COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS


Impact factor trend

Year Impact Factor
2008 1.22
2009 1.144
2010 1.238
2011 1.516
2012 1.555
2013 1.093
2014 1.897
2015 1.862
2016 2.503
2017 2.674
2018 3.424
2019 3.632
2020 5.428
2021 7.027
2022 6.1
2023 4.9
2024 4.8

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