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The progressive neuropathology involved in dementia frequently causes a gradual decline in communication skills. Communication partners who are unaware of the specific communication problems faced by people with dementia (PWD) can inadvertently challenge their conversation partner, leading to distress and a reduced flow of information between speakers. Previous research has produced an extensive literature base recommending strategies to facilitate conversational engagement in dementia. However, empirical evidence for the beneficial effects of these strategies on conversational dynamics is sparse. This study uses a time-efficient computational discourse analysis tool called Discursis to examine the link between specific communication behaviours and content-based conversational engagement in 20 conversations between PWD living in residential aged-care facilities and care staff members. Conversations analysed here were baseline conversations recorded before staff members underwent communication training. Care staff members spontaneously exhibited a wide range of facilitative and non-facilitative communication behaviours, which were coded for analysis of conversation dynamics within these baseline conversations. A hybrid approach combining manual coding and automated Discursis metric analysis provides two sets of novel insights. Firstly, this study revealed nine communication behaviours that, if used by the care staff member in a given turn, significantly increased the appearance of subsequent content-based engagement in the conversation by PWD. Secondly, the current findings reveal alignment between human- and computer-generated labelling of communication behaviour for 8 out of the total 22 behaviours under investigation. The approach demonstrated in this study provides an empirical procedure for the detailed evaluation of content-based conversational engagement associated with specific communication behaviours.

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Background and ObjectivesCommunication difficulties have been reported as one of the most stress-inducing aspects of caring for people with dementia. Notably, with disease progression comes an increase in the frequency of communication difficulty and a reduction in the effectiveness of attempts to remedy breakdowns in communication. The aim of the current research was to evaluate the utility of an automated discourse analysis tool (i.e., Discursis) in distinguishing between different types of trouble and repair signaling behaviors, demonstrated within conversations between people with dementia and their professional care staff.Research Design and MethodsTwenty conversations between people with dementia and their professional care staff were human-coded for instances of interactive/noninteractive trouble and typical/facilitative repair behaviors. Associations were then examined between these behaviors and recurrence metrics generated by Discursis.ResultsSignificant associations were identified between Discursis metrics, trouble-indicating, and repair behaviors.Discussion and ImplicationsThese results suggest that discourse analysis software is capable of discriminating between different types of trouble and repair signaling behavior, on the basis of term recurrence calculated across speaker turns. The subsequent recurrence metrics generated by Discursis offer a means of automating the analysis of episodes of conversational trouble and repair. This achievement represents the first step toward the future development of an intelligent assistant that can analyze conversations in real time and offers support to people with dementia and their carers during periods of communicative trouble.