Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg November 26, 2019

Your data is gold – Data donation for better healthcare?

Veronika Strotbaum

Veronika Strotbaum, B.A. Gerontology and M.A., Healthcare Management has been working at ZTG Zentrum für Telematik und Telemedizin GmbH in Bochum as a consultant for telemedicine and mobile applications since 2013. Her work focuses on the evaluation and acceptance analysis of digital health applications, health reporting as well as the conception and implementation of advanced training courses in eHealth. She has written a wide range of articles in books and journals and is a member of the DGGÖ e.V. and GMDS e.V.

ORCID logo
, Monika Pobiruchin

Dr. Monika Pobiruchin received her diploma in Medical Informatics in 2010. Currently she is a research associate at GECKO Institute at Heilbronn University. Her doctoral thesis focused on the use of data from cancer registries for the generation of health economic models. Since 2014, she has been (co-)chair of the working group “Consumer Health Informatics” within GMDS e.V.

ORCID logo EMAIL logo
, Björn Schreiweis

Dr. Björn Schreiweis obtained his diploma in Medical Informatics (MI) at University of Heidelberg, Germany, in 2010. He finished his doctoral degree on cross-enterprise patient recruitment systems in 2016 at Heidelberg University. Since 2017, he is co-chair of the working group “Consumer Health Informatics” of the GMDS e.V. As of April 2018 he is a Senior Researcher at Dept. of MI at the Institute for Medical Informatics and Statistics of University Hospital Schleswig-Holstein (UKSH) & Kiel University. Since September 2018 he is Head of the Medical Data Integration Center at UKSH.

ORCID logo
, Martin Wiesner

Dipl.-Inform. Med. Martin Wiesner received his diploma in Medical Informatics at University of Heidelberg in 2007. He is working as a research associate at Heilbronn University and teaches software development, database and information systems. In 2014, he (co-)founded the working group “Consumer Health Informatics” within GMDS e.V. His research interest focuses on digital health applications and how related technology affects healthy citizens or patients.

ORCID logo
and Brigitte Strahwald

Brigitte Strahwald, MSc Mmel is an anesthesiologist with a master’s degree in epidemiology and a master’s degree in med. ethics and law. She is working at the LMU Munich and as managing director of an agency for health communication.

ORCID logo

Abstract

Today, medical data such as diagnoses, procedures, imaging reports and laboratory tests, are not only collected in context of primary research and clinical studies. In addition, citizens are tracking their daily steps, food intake, sport exercises, and disease symptoms via mobile phones and wearable devices. In this context, the topic of “data donation” is drawing increased attention in science, politics, ethics and practice. This paper provides insights into the status quo of personal data donation in Germany and from a global perspective. As this topic requires a consideration of several perspectives, potential benefits and related, multifaceted challenges for citizens, patients and researchers are discussed. This includes aspects such as data quality & accessibility, privacy and ethical considerations.

ACM CCS:

About the authors

Veronika Strotbaum

Veronika Strotbaum, B.A. Gerontology and M.A., Healthcare Management has been working at ZTG Zentrum für Telematik und Telemedizin GmbH in Bochum as a consultant for telemedicine and mobile applications since 2013. Her work focuses on the evaluation and acceptance analysis of digital health applications, health reporting as well as the conception and implementation of advanced training courses in eHealth. She has written a wide range of articles in books and journals and is a member of the DGGÖ e.V. and GMDS e.V.

Monika Pobiruchin

Dr. Monika Pobiruchin received her diploma in Medical Informatics in 2010. Currently she is a research associate at GECKO Institute at Heilbronn University. Her doctoral thesis focused on the use of data from cancer registries for the generation of health economic models. Since 2014, she has been (co-)chair of the working group “Consumer Health Informatics” within GMDS e.V.

Björn Schreiweis

Dr. Björn Schreiweis obtained his diploma in Medical Informatics (MI) at University of Heidelberg, Germany, in 2010. He finished his doctoral degree on cross-enterprise patient recruitment systems in 2016 at Heidelberg University. Since 2017, he is co-chair of the working group “Consumer Health Informatics” of the GMDS e.V. As of April 2018 he is a Senior Researcher at Dept. of MI at the Institute for Medical Informatics and Statistics of University Hospital Schleswig-Holstein (UKSH) & Kiel University. Since September 2018 he is Head of the Medical Data Integration Center at UKSH.

Martin Wiesner

Dipl.-Inform. Med. Martin Wiesner received his diploma in Medical Informatics at University of Heidelberg in 2007. He is working as a research associate at Heilbronn University and teaches software development, database and information systems. In 2014, he (co-)founded the working group “Consumer Health Informatics” within GMDS e.V. His research interest focuses on digital health applications and how related technology affects healthy citizens or patients.

Brigitte Strahwald

Brigitte Strahwald, MSc Mmel is an anesthesiologist with a master’s degree in epidemiology and a master’s degree in med. ethics and law. She is working at the LMU Munich and as managing director of an agency for health communication.

Literature

1. WHO. 2010. World Health Assembly Resolution 63.22 on Human Organ and Tissue Transplantation [Online: http://www.who.int/transplantation/en; Accessed: June 28, 2019].Search in Google Scholar

2. David M. Shaw, Juliane V. Gross, and Thomas C. Erren. 2016. Data donation after death. EMBO reports, 17, 1, 14–17. DOI:10.15252/embr.201541802.Search in Google Scholar

3. Weiqi Wang and Eswar Krishnan. 2014. Big Data and Clinicians: A Review on the State of the Science. JMIR Med Inform, 2, 1, e1. DOI:10.2196/medinform.2913.Search in Google Scholar

4. Ziad Obermeyer and Ezekiel J. Emanuel. 2016. Predicting the Future – Big Data, Machine Learning, and Clinical Medicine. N Engl J Med, 375, 13, 1216–1219. DOI:10.1056/NEJMp1606181.Search in Google Scholar

5. Jenny Krutzinna, Mariarosaria Taddeo, and Luciano Floridi. 2019. Enabling posthumous medical data donation: a plea for the ethical utilisation of personal health data. In The Ethics of Medical Data Donation. Springer, 163–180.10.1007/978-3-030-04363-6_11Search in Google Scholar

6. Sean Khozin, Gideon M. Blumenthal, and Richard Pazdur. 2017. Real-world Data for Clinical Evidence Generation in Oncology. JNCI, 109, 11. DOI:10.1093/jnci/djx187.Search in Google Scholar

7. Christopher J. L. Murray, et al. 2017. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390, 10100, 1151–1210. DOI:10.1016/S0140-6736(17)32152-9.Search in Google Scholar

8. Siegfried Geyer and Christoph Kowalski. 2018. GKV-Routinedaten in der onkologischen Versorgungsforschung. ONKOLOGIE heute, 1/2018, X70–X72.Search in Google Scholar

9. Kristiina Häyrinen, Kaija Saranto, and Pirkko Nykänen. 2008. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform, 77, 5, 291–304. DOI:10.1016/j.ijmedinf.2007.09.001.Search in Google Scholar PubMed

10. Monika Pobiruchin and Martin Wiesner. 2017. Workshop Report, conhIT Satellite Symposium 2017: Können von Bürgern generierte Daten für die Versorgungsforschung nutzbar gemacht werden? Technical report [Online: https://gmds.de/fileadmin/user_upload/Presse/170424_PGCHI_Workshop_Summary.pdf; Accessed: May 9, 2019].Search in Google Scholar

11. David D. McManus, Ludovic Trinquart, Emelia J. Benjamin, Emily S. Manders, Kelsey Fusco, et al. 2019. Design and preliminary findings from a new electronic cohort embedded in the framingham heart study. J Med Internet Res, 21, 3 (March 2019), e12143. DOI:10.2196/12143.Search in Google Scholar PubMed PubMed Central

12. Melanie Swan. 2012. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0. J Sens Actuator Netw, 1, 3, 217–253. DOI:10.3390/jsan1030217.Search in Google Scholar

13. Andre Henriksen, Martin Mikalsen, Woldaregay Haugen, Ashenafi Zebene, et al. 2018. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. J Med Internet Res, 20, 3, e110. DOI:10.2196/jmir.9157.Search in Google Scholar PubMed PubMed Central

14. Pascal Su. 2013. Direct-to-consumer genetic testing: a comprehensive view. Yale J Biol Med, 86, 3, 359.Search in Google Scholar

15. Population Health Research Network. 2019. About us [Online: https://www.phrn.org.au/about-us; Accessed: June 12, 2019].Search in Google Scholar

16. Christian Nohr, Liisa Parv, Pille Kink, Elizabeth Cummings, Helen Almond, Jens Rahbek Nørgaard, et al. 2017. Nationwide citizen access to their healthdata: analysing and comparing experiences in Denmark, Estonia and Australia. BMC Health Serv Res, 17, 534. DOI:10.1186/s12913-017-2482-y.Search in Google Scholar PubMed PubMed Central

17. Australian Government – Australian Digital Health Agency. 2018. What is My Health Record? Whats in a My Health Record? [Online: https://www.myhealthrecord.gov.au/for-you-your-family/what-is-my-health-record; Accessed: June 12, 2019].Search in Google Scholar

18. Sundhed. 2016. Sundhed.dk – background [Online: https://www.sundhed.dk/borger/service/om-sundheddk/ehealth-in-denmark/background; Accessed: June 13, 2019].Search in Google Scholar

19. Matthias Hannermann. 2014. Der Vorratsdatenspeicher [Online: https://www.brandeins.de/magazine/brand-eins-wirtschaftsmagazin/2014/beobachten/der-vorratsdatenspeicher; Accessed: June 13, 2019].Search in Google Scholar

20. Charles Seife. 2013. 23andMe Is Terrifying, but Not for the Reasons the FDA Thinks [Online: https://www.scientificamerican.com/article/23andme-is-terrifying-but-not-for-the-reasons-the-fda-thinks; Accessed: June 10, 2019].Search in Google Scholar

21. Henri-Corto Stoekle, Marie Fance Mamzer-Bruneel, Guillaume Vogt, and Christian Herve. 2016. 23andMe: a new two-sided data-bankingmarket model. BMC Medical Ethics, 17, 19. DOI:10.1186/s12910-016-0101-9.Search in Google Scholar PubMed PubMed Central

22. Wolfgang Hoffmann, Karl-Heinz Jöckel, Rudolf Kaaks, H.-Erich Wichmann, Karin Halina Greiser, and Jakob Linseisen. 2011. The National Cohort. A prospective epidemiologic study resource for health and disease research in Germany. Technical report [Online: https://nako.de/wp-content/uploads/2015/07/Wissenschaftliches-Konzept-der-NAKO2.pdf; Accessed: June 18, 2019].Search in Google Scholar

23. Wolfgang Ahrens and Karl-Heinz Jöckel. 2015. Der Nutzen großer Kohortenstudien für die Gesundheitsforschung am Beispiel der Nationalen Kohorte. Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz, 58, 8, 813–821. DOI:10.1007/s00103-015-2182-x.Search in Google Scholar PubMed

24. Hans-Konrad Selbmann. 1988. Epidemiologische Forschung in der Bundesrepublik Deutschland – Aufgaben und Grenzen. In Medizinische Informationsverarbeitung und Epidemiologie im Dienste der Gesundheit. Selbmann Hans-Konrad, et al., editors. Springer, Berlin, Heidelberg. DOI:10.1007/978-3-642-83520-9.Search in Google Scholar

25. Andreas Maier. 2019. Medical Data Donors e.V. – Mission [Online: https://www.medicaldatadonors.org/index.php/mission; Accessed: June 11, 2019].Search in Google Scholar

26. George Hripcsak, Meryl Bloomrosen, Patti F. Brennan, Christopher G. Chute, Jim Cimino, et al. 2014. Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA’s 2012 Health Policy Meeting. J Am Med Inform Assoc, 21, 2, 204–211. DOI:10.1136/amiajnl-2013-002117.Search in Google Scholar PubMed PubMed Central

27. Marc Cuggia and Stéphanie Combes. 2019. The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare. Yearb Med Inform, 28, 1, 195–202. DOI:10.1055/s-0039-1677917.Search in Google Scholar PubMed PubMed Central

28. Expertengruppe Smart Data im Gesundheitswesen. 2017. Smart Data im Gesundheitswesen 2017: Smart Data im Gesundheitswesen – Positionspapier der Expertengruppe Smart Data. Technical Report [Online: https://deutschland-intelligent-vernetzt.org/app/uploads/2017/07/20170612_DIV-Position-Smart-Data-im-Gesundheitswesen.pdf; Accessed: June 11, 2019]. Bundesministerium für Gesundheit, Berlin.Search in Google Scholar

29. Nature Editorial. 2015. Data overprotection – Draft European rules governing privacy threaten to hamper medical research. Nature, 522, 7557, 391–392. DOI:10.1038/522391b.Search in Google Scholar PubMed

30. German Ethics Council. 2017. Big Data and Health – Data Sovereignty as the Shaping of Informational Freedom. ISBN: 978-3-941957-75-6. Berlin.Search in Google Scholar

31. Thorsten Maybaum. 2019. Spahn appelliert an Bevölkerung, Gesundheitsdaten für Forschung freizugeben [Online: https://www.aerzteblatt.de/nachrichten/102368; Accessed: June 28, 2019].Search in Google Scholar

32. Kathrin Gießelmann. 2018. Hecken plädiert für verpflichtende Datenspende [Online: https://www.aerzteblatt.de/nachrichten/99900; Accessed: June 11, 2019].Search in Google Scholar

33. Philipp Grätzel von Grätz. 2019. Mehr Mut [Online: https://e-health-com.de/thema-der-woche/mehr-mut; Accessed: June 11, 2019].Search in Google Scholar

34. Urs-Vito Albrecht, Ute von Jan, Oliver Pramann, and Heiner Fangerau. 2016. Kapitel 7 – Gesundheits-Apps im Forschungskontext. Chancen und Risiken von Gesundheits-Apps (CHARISMHA). Urs-Vito Albrecht, editor.Search in Google Scholar

35. Johannes Hauswaldt, Valérie Kempter, Wolfgang Himmel, and Eva Hummers. 2018. Obstacles in secondary analysis of routine data from primary care. Gesundheitswesen, 80, 11, 987–993. DOI:10.1055/a-0668-5817.Search in Google Scholar PubMed

36. Antoine Vallot, Antoine d’Aquin, Joseph Adrien Le Roi, and Guy Crescent Fagon. 1862. Journal de la santé du roi Louis XIV de l’année 1647 à l’année 1711. Durand.Search in Google Scholar

37. Statista Survey. (n.d.) 2017. Percentage of mobile medical application categories used by U.S. adults at least once as of 2017 [Online: https://www.statista.com/statistics/378850; Accessed: June 21, 2019].Search in Google Scholar

38. Hannah E. Payne, Cameron Lister, Joshua H. West, and Jay M. Bernhardt. 2015. Behavioral Functionality of Mobile Apps in Health Interventions: A Systematic Review of the Literature. JMIR mHealth uHealth, 3, 1, e20. DOI:10.2196/mhealth.3335.Search in Google Scholar PubMed PubMed Central

39. Jennifer K. Carroll, Anne Moorhead, Raymond Bond, William G. LeBlanc, Robert J. Petrella, and Kevin Fiscella. 2017. Who Uses Mobile Phone Health Apps and Does Use Matter? A Secondary Data Analytics Approach. J Med Internet Res, 19, 4, e125. DOI:10.2196/jmir.5604.Search in Google Scholar PubMed PubMed Central

40. Walter Thompson. 2017. Worldwide survey of fitness trends for 2018: the crep edition. ACSMs Health Fit J, 21, 6, 10–19. DOI:10.1249/FIT.0000000000000341.Search in Google Scholar

41. Valerie Gay, and Peter Leijdekkers. 2015. Bringing Health and Fitness Data Together for Connected Health Care: Mobile Apps as Enablers of Interoperability. J Med Internet Res, 17, 11, e260. DOI:10.2196/jmir.5094.Search in Google Scholar PubMed PubMed Central

42. Martin Wiesner, Richard Zowalla, Julian Suleder, Maximilian Westers, and Monika Pobiruchin. 2018. Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study. JMIR mHealth uHealth, 6, 12, e201. DOI:10.2196/mhealth.9623.Search in Google Scholar PubMed PubMed Central

43. Pei-Yun Hsueh, Ying K. Cheung, Sanjoy. Dey, Katherine K. Kim, Fernando Martin-Sanchez, S. K. Petersen, and Thomas Wetter. 2017. Added Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics. Yearb Med Inform, 26, 01, 160–171. DOI:10.15265/IY-2017-009.Search in Google Scholar PubMed PubMed Central

44. Albert M. Lai, Pei-Yun Hsueh, Y. K. Choi, and Robin R. Austin. 2017. Present and future trends in consumer health informatics and patient-generated health data. Yearb Med Inform, 26, 01, 152–159. DOI:10.15265/IY-2017-016.Search in Google Scholar PubMed PubMed Central

45. Ethan Basch, Allison M. Deal, Mark G. Kris, Howard I. Scher, Clifford A. Hudis, et al. 2016. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J of Clin Oncol, 34, 6, 557. DOI:10.1200/JCO.2015.63.0830.Search in Google Scholar PubMed PubMed Central

46. Deborah Lupton. 2014. The commodification of patient opinion: the digital patient experience economy in the age of big data. Sociology of health & illness, 36, 6, 856–869. DOI:10.1111/1467-9566.12109.Search in Google Scholar PubMed

47. Melanie Swan. 2009. Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. Int. J. Environ. Res. Public Health, 6, 2, 492–525. DOI:10.3390/ijerph6020492.Search in Google Scholar PubMed PubMed Central

48. Christopher Burton, David Weller, and Michael Sharpe. 2007. Are electronic diaries useful for symptoms research? A systematic review. J Psychosom Res, 62, 5, 553–561. DOI:10.1016/j.jpsychores.2006.12.022.Search in Google Scholar PubMed

49. Martha M. Funnell and Robert M. Anderson. 2004. Empowerment and self-management of diabetes. Clinical diabetes, 22, 3, 123–127. DOI:10.2337/diaclin.22.3.123.Search in Google Scholar

50. Catherine A. Martin and Barry P. McGrath. 2014. White-coat hypertension. Clin Exp Pharmacol Physiol, 41, 1, 22–29. DOI:10.1111/1440-1681.12114.Search in Google Scholar PubMed

51. Daniel Rosen, Janice D. McCall, and Brian A. Primack. 2017. Telehealth protocol to prevent readmission among high-risk patients with congestive heart failure. The American J of Med, 130, 11, 1326–1330. DOI:10.1016/j.amjmed.2017.07.007.Search in Google Scholar PubMed

52. Rachel R. Bian, Gretchen A. Piatt, Ananda Sen, Melissa A. Plegue, Mariana L. De Michele, et al. 2017. The effect of technology-mediated diabetes prevention interventions on weight: a meta-analysis. J Med Internet Res, 19, 3, e76. DOI:10.2196/jmir.4709.Search in Google Scholar PubMed PubMed Central

53. Amy L. McKenzie, Sarah J. Hallberg, Brent C. Creighton, Brittanie M. Volk, Theresa M. Link, et al. 2017. A novel intervention including individualized nutritional recommendations reduces hemoglobin a1c level, medication use, and weight in type 2 diabetes. JMIR Diabetes, 2, 1, e5. DOI:10.2196/diabetes.6981.Search in Google Scholar PubMed PubMed Central

54. Ingrid Köster, Eduard Huppertz, Hans Hauner, and Ingrid Schubert. 2014. Costs of Diabetes Mellitus (CoDiM) in Germany, direct per-capita costs of managing hyperglycaemia and diabetes complications in 2010 compared to 2001. Exp Clin Endocrinol Diabetes, 122, 09, 510–516. DOI:10.1055/s-0034-1375675.Search in Google Scholar PubMed

55. Laura F. Garabedian, Dennis Ross-Degnan, and J. Frank Wharam. 2015. Mobile Phone and Smartphone Technologies for Diabetes Care and Self-Management. Curr Diab Rep, 15, 12, 109. DOI:10.1007/s11892-015-0680-8.Search in Google Scholar PubMed PubMed Central

56. William A. Wood, Antonia V. Bennett, and Ethan Basch. 2015. Emerging uses of patient generated health data in clinical research. Molecular Oncology, 9, 5, 1018–1024. DOI:10.1016/j.molonc.2014.08.006.Search in Google Scholar PubMed PubMed Central

57. Dana Lewis, and #OpenAPS Community. 2019. OpenAPS Outcomes [Online: https://openaps.org/outcomes/; Accessed: June 21, 2019].Search in Google Scholar

58. Dana Lewis, Scott Leibrand, and #OpenAPS Community. 2016. Real-world use of open source artificial pancreas systems. J of Diabetes Sci and Tech, 10, 6, 1411–1411. DOI:10.1177/1932296816665635.Search in Google Scholar PubMed PubMed Central

59. Dana M. Lewis, Richard S. Swain, and Thomas W. Donner. 2018. Improvements in A1C and time-in-range in DIY closed-loop (OpenAPS) users. Am Diabetes Assoc, 67, Supplement 1 (July 2018). DOI:10.2337/db18-352-OR.Search in Google Scholar

60. Maggie Koerth-Baker. 2018. The tricky ethics and big risks of medical ‘data donation’ [Online: https://www.advisory.com/daily-briefing/2018/07/18/personal-data; Accessed: June 10, 2019].Search in Google Scholar

61. Barbara Preinsack. 2019. Data Donation: How to resist the iLeviathan. In The Ethics of Medical Data Donation. Jenny Krutzinna and Luciano Floridi, editors. Springer, 9–22.10.1007/978-3-030-04363-6_2Search in Google Scholar PubMed

62. Quinn Grundy, Kellia Chiu, Fabian Held, Andrea Continella, et al. 2019. Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysis. BMJ, 364, 1920. DOI:10.1136/bmj.l920.Search in Google Scholar PubMed PubMed Central

63. Joshua M. Pevnick, Garth Fuller, Ray Duncan, and Brennan M. R. Spiegel. 2016. A large-scale initiative inviting patients to share personal fitness tracker data with their providers: initial results. PLOS ONE, 11, 11, 1–5. DOI:10.1371/journal.pone.0165908.Search in Google Scholar PubMed PubMed Central

64. Quinn Grundy, Fabian Held, and Lisa A. Bero. 2017. Tracing the Potential Flow of Consumer Data: A Network Analysis of Prominent Health and Fitness Apps. J Med Internet Res, 19, 6, e233. DOI:10.2196/jmir.7347.Search in Google Scholar PubMed PubMed Central

65. Jenny Krutzinna and Luciano Floridi. 2019. Ethical Medical Data Donation: A Pressing Issue. In The Ethics of Medical Data Donation. Jenny Krutzinna and Luciano Floridi, editors. Springer, 1–6.10.1007/978-3-030-04363-6Search in Google Scholar PubMed

66. Monika Pobiruchin, Julian Suleder, Richard Zowalla, and Martin Wiesner. 2017. Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study. JMIR mhealth uhealth, 5, 2, e24. DOI:10.2196/mhealth.6395.Search in Google Scholar PubMed PubMed Central

67. Arthur Jochems, Timo M. Deist, Johan van Soest, Michael Eble, Paul Bulens, et al. 2016. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – A real life proof of concept. Radiotherapy and Oncology, 121, 3, 459–467. DOI:10.1016/j.radonc.2016.10.002.Search in Google Scholar PubMed

Received: 2019-06-28
Revised: 2019-09-30
Accepted: 2019-11-08
Published Online: 2019-11-26
Published in Print: 2019-10-25

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 27.1.2023 from https://www.degruyter.com/document/doi/10.1515/itit-2019-0024/html
Scroll Up Arrow