首页> 外文会议>Computing in Cardiology 2011 >Knowledge discovery from lifestyle profiles to support self-management of Chronic Heart Failure
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Knowledge discovery from lifestyle profiles to support self-management of Chronic Heart Failure

机译:从生活方式档案中发现知识以支持对慢性心力衰竭的自我管理

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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.
机译:在本文中,我们探讨了整合从家用传感器收集的数据和来自自我报告的信息以支持慢性心力衰竭的自我管理的可行性。在家庭环境中,基于传感器的技术在30天的时间内记录了睡眠时间,电视使用情况和公用事业使用情况。与参与者的日常自我报告有关的信息被用于辅助分析,以向参与者提供有关他们应如何管理其病情的更有意义和相关的反馈。结果表明,可以发现可能导致生活方式改变的趋势。例如,虽然未发现异常睡眠模式事件的特殊原因,但在更长的时间段内识别此类事件的能力可能很重要。总而言之,这项研究的结果表明,可以使用活动信息和自我报告来产生用于支持自我管理的反馈,并从中受益。

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