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The power of mobile notifications to increase wellbeing logging behavior
KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Mobile Service Laboratory (MS Lab).ORCID iD: 0000-0002-9554-0071
2013 (English)In: CHI '13 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM Digital Library, 2013, 1095-1098 p.Conference paper, Published paper (Refereed)
Abstract [en]

Self-logging is a critical component to many wellbeing systems. However, self-logging often is difficult to sustain at regular intervals over many weeks. We demonstrate the power of passive mobile notifications to increase logging of wellbeing data, particularly food intake, in a mobile health service. Adding notifications increased the frequency of logging from 12% in a one-month, ten-user pilot study without reminders to 63% in the full 60-user study with reminders included. We will discuss the benefits of passive notifications over existing interruptive methods.

Place, publisher, year, edition, pages
ACM Digital Library, 2013. 1095-1098 p.
Keyword [en]
Behavior change, Logging, Mobile, Notifications, Personal informatics, Wellbeing
National Category
Human Computer Interaction Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-125989DOI: 10.1145/2470654.2466140Scopus ID: 2-s2.0-84877962856ISBN: 978-1-4503-1899-0 (print)OAI: oai:DiVA.org:kth-125989DiVA: diva2:641412
Conference
31st Annual CHI Conference on Human Factors in Computing Systems: Changing Perspectives, CHI 2013, 27 April 2013 through 2 May 2013, Paris
Projects
Mobile Health Mashups
Funder
Wireless@kth
Note

QC 20130816

Available from: 2013-08-16 Created: 2013-08-16 Last updated: 2016-04-19Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf