An Automated Approach to Examining Conversational Dynamics between People with Dementia and Their Carers

Abstract

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.

Spoken language biomarkers for detecting cognitive impairment

“…Speech data used in experiments varied from being highly structured memory and reading tests [29,28,30], to organic, open-ended, conversation-like interactions (e.g. sharing stories) [31,27].…”

Situational Strength Cues from Social Sources at Work: Relative Importance and Mediated Effects

“…Future research could therefore use qualitative content and discourse analysis techniques (e.g., Atay et al, 2015 ) to examine the themes that emerge from situational strength-related written and verbal messages received by employees from various organizational sources. Future research could also assess perceptions of situational strength from additional sources (e.g., non-social sources such as the nature of the work itself, Meyer et al, 2009 ; external social sources such as clients/customers, Oliver et al, 2016 ) as well as perceptions relevant to additional facets of situational strength not included in the current study (e.g., consistency and consequences, Meyer et al, 2014 ).…”

Beyond frequency counts: Novel conceptual recurrence analysis metrics to index semantic coordination in team communications

“…In effective interactions, the caregiver tends to align the conceptual content of his or her utterances to those of the patient, and these interactions are typically characterized by a strong leader-follower relation. It has also been shown that Discursis can be used to quantify content engagement between individuals with dementia and their caretakers, and that metrics associated with short-term topic repetition are related to subsets of communicative interactions manually coded as Bactive listening^ (Atay et al, 2015). These relations show the potential of CRA metrics in the systematic evaluation of communicative content.…”

“…Despite this potential limitation, we believe our results successfully demonstrate both the experimental utility of the proposed novel CRA metrics and how they may be affected by parameter choices. We also note that CRA is a relatively new tool, and some of the metrics derived from it require further research before they can be clearly interpreted (though some work along these lines has been recently conducted; e.g., Atay et al, 2015). Furthermore, a variety of parameter values must be set during data analysis with CRA, including the number of key terms and the source of key terms.…”