Knowledge discovery from lifestyle profiles to support self-management of chronic heart failure

Yan Huang, Huiru Zheng, Chris Nugent, Paul McCullagh, Norman Black, Mark Hawley, Gail Mountain

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Citations (Scopus)


    In this paper, we explore the feasibility of integrating data gleaned from home-based sensors and information from self reporting to support the self-management of Chronic Heart Failure. Time spent sleeping, television usage and utility usage were recorded by sensor based technology within the home environment for one participant over a 30 day period. Information in relation to a participant's daily self report was used to assist analysis in an effort to provide more meaningful and relevant feedback to the participant in relation to how they should manage their condition. The results indicate that trends which could lead to lifestyle change can be discovered. For example, whilst no particular cause of an unusual sleeping pattern event was discovered, the ability to identify such events could be important over longer periods of time. In conclusion, the findings from the study have suggested that feedback used to support self-management can be generated using both activity information and self report, and potentially benefits for the combination.

    Original languageEnglish
    Title of host publicationComputing in Cardiology 2011, CinC 2011
    Number of pages4
    Publication statusPublished (in print/issue) - 1 Dec 2011
    EventComputing in Cardiology 2011, CinC 2011 - Hangzhou, China
    Duration: 18 Sept 201121 Sept 2011


    ConferenceComputing in Cardiology 2011, CinC 2011


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