1.1
1
xml
info:srw/schema/1/dc-v1.1
Detecting Covid-19 Relevant Situations using Privacy-by-Design based Mobile Experience Sampling
Hannes Restel
Eridy Lukau
Sebastian Sterl
Lars Gerhold
Rob Grace
Hossein Baharmand
To observe psychosocial effects of the Covid-19 pandemic on the population, multiple retrospective studies have been performed in Germany. However, this approach may lead to response bias regarding affective and cognitive processes as it fails to capture situations as they occur (in situ). Identifying those situations in daily life where individuals are emotionally and cognitively affected by Covid-19 can provide valuable insights for mobile experience sampling method studies (MESM) that evaluate participants affective and cognitive processes. This study presents an MESM solution (a self-developed smartphone app and server backend) to detect Covid-19 induced in-situ frames which was successfully used in a long-term psychosocial study in Berlin (Germany) from November 2021 to January 2022. As highly sensitive personal data (e.g., emotional state, vaccination status and infection state, socio-demographics) have been collected, the solution places a strong emphasis on privacy, pseudo-anonymization, data-minimization, and security. To support long-time motivation for the participants, good usability and user experience containing gamification elements were also realized. The results indicate that Covid-19-related situations expressed by means of a high emotional risk perception could be identified even though participants located themselves in rather Covid-19 free private spaces.
urn:ISBN:978-82-8427-099-9
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Detecting%20Covid-19%20Relevant%20Situations%20using%20Privacy-by-Design%20based%20Mobile%20Experience%20Sampling&stitle=Iscram%202022&issn=2411-3387&isbn=978-82-8427-099-9&date=2022&spage=506&epage=527&aulast=Hannes%20Restel&au=Eridy%20Lukau&au=Sebastian%20Sterl&au=Lars%20Gerhold&place=Tarbes%2C%20France&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2436
citekey:HannesRestel_etal2022
citation:Hannes Restel, Eridy Lukau, Sebastian Sterl, & Lars Gerhold. (2022). Detecting Covid-19 Relevant Situations using Privacy-by-Design based Mobile Experience Sampling. In Rob Grace, & Hossein Baharmand (Eds.), ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management (pp. 506-527). Tarbes, France.
2022
ConferencePaper
text
Mobile Experience Sampling Method
in-situ frames
Covid-19
Privacy-by-Design
risk perception
file:http://idl.iscram.org/files/hannesrestel/2022/2436_HannesRestel_etal2022.pdf
English
2411-3387
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management
2022
506
527
1