Is identifying boredom the answer to controlling the bombardment of notifications on mobile devices?

Rashid Kamal, Aimal Rextin, Chris Nugent, Ian Cleland, Paul McCullagh

Research output: Contribution to journalArticlepeer-review

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Mobile notifications have become ubiquitous in modern life, yet excessive volumes contribute to interruption overload. This paper investigates intelligent notification management leveraging user context. A three-stage methodology employed a focus group, survey, and in-the-wild data collection app. The focus group (n=12) provided preliminary insights into notification perceptions during boredom which informed survey design. The survey (n=106) probed usage habits across times, days, and app categories. The SeektheNotification app gathered real-world notification data from 20 Android users over 3 months.Analysis revealed social and personal apps dominate notification volumes (91% combined). Shorter response delays occurred on weekends and after 12 pm, suggesting heightened user receptivity during boredom. Random Forest classification achieved 88% accuracy, outperforming 13 other algorithms, underscoring machine learning’s potential for context-aware notification systems.Our exploratory findings indicate notifications could be optimized by considering situational factors like boredom. Further research should expand context beyond boredom and employ advanced deep learning techniques. This preliminary study demonstrates the promise of leveraging user psychology and machine intelligence to develop smarter interruption management systems to combat notification overload.
Original languageEnglish
Pages (from-to)115-132
Number of pages18
JournalCCF Transactions on Pervasive Computing and Interaction
Issue number2
Early online date20 Jan 2024
Publication statusPublished (in print/issue) - 1 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).

Publisher Copyright:
© The Author(s) 2024.


  • Behavioural modelling
  • Push notifications
  • Boredom


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