Dataset Information

Identifying indicators of important diagnostic features of delirium.

ABSTRACT: To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium.Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm.Meetings of expert panel.Panel of seven interdisciplinary clinical experts.Panelists' assignments of each assessment item to indicate CAM features.From an initial pool of 119 assessment items, the panel assigned 66 items to at least one CAM feature, and many items were assigned to more than one feature. Experts achieved a high level of consensus, with a postmeeting kappa for agreement of 0.98. The study staff compiled the assignment results to create a comprehensive list of CAM feature indicators, consisting of 107 patient interview questions, cognitive tasks, and interviewer observations, with some items assigned to multiple features. A subpanel shortened this list to 28 indicators of important delirium features.A systematic, well-described qualitative methodology was used to create a list of indicators for delirium based on the features of the CAM diagnostic algorithm. This indicator list may be useful as a clinical tool for enhancing delirium recognition at the bedside and for aiding in the development of a brief delirium screening instrument.

SUBMITTER: Huang LW

PROVIDER: S-EPMC3376010 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Identifying indicators of important diagnostic features of delirium.

Huang Li-Wen LW Inouye Sharon K SK Jones Richard N RN Fong Tamara G TG Rudolph James L JL O'Connor Margaret G MG Metzger Eran D ED Crane Paul K PK Marcantonio Edward R ER

Journal of the American Geriatrics Society 20120601 6

<h4>Objectives</h4>To use an expert consensus process to identify indicators of delirium features to help enhance bedside recognition of delirium.<h4>Design</h4>Modified Delphi consensus process to assign existing cognitive and delirium assessment items to delirium features in the Confusion Assessment Method (CAM) diagnostic algorithm.<h4>Setting</h4>Meetings of expert panel.<h4>Participants</h4>Panel of seven interdisciplinary clinical experts.<h4>Measurements</h4>Panelists' assignments of each ...[more]

PMID: 22690980

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Project description:The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners' emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays. Following a more contextualized approach, the present study aims to identify features of bodily expression (i.e., posture and activity of the upper body and the head) that relate to genuine emotional experience during interaction with a serious game. In a multimethod approach, 70 undergraduates played a serious game relating to financial education while their bodily expression was captured using an off-the-shelf depth-image sensor (Microsoft Kinect). In addition, self-reports of experienced enjoyment, boredom, and frustration were collected repeatedly during gameplay, to address the dynamic changes in emotions occurring in educational tasks. Results showed that, firstly, the intensities of all emotions indeed changed significantly over the course of the game. Secondly, by using generalized estimating equations, distinct features of bodily expression could be identified as significant indicators for each emotion under investigation. A participant keeping their head more turned to the right was positively related to frustration being experienced, whereas keeping their head more turned to the left was positively related to enjoyment. Furthermore, having their upper body positioned more closely to the gaming screen was also positively related to frustration. Finally, increased activity of a participant's head emerged as a significant indicator of boredom being experienced. These results confirm the value of bodily expression as an indicator of emotional experience in multimedia learning systems. Furthermore, the findings may guide developers of emotion recognition procedures by focusing on the identified features of bodily expression.

Dataset Information

Identifying indicators of important diagnostic features of delirium.

Publications

Identifying indicators of important diagnostic features of delirium.

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