Short: Causal structural learning of conversational engagement for socially isolated older adults

Smart Health (Amst). 2023 Jun:28:100384. doi: 10.1016/j.smhl.2023.100384. Epub 2023 Mar 21.

Abstract

Social isolation has become a growing public health concern in older adults and older adults with mild cognitive impairment. Coping strategies must be developed to increase social contact for socially isolated older adults. In this paper, we explored the conversational strategy between trained conversation moderators and socially isolated adults during a conversational engagement clinical trial (Clinicaltrials.gov: NCT02871921). We carried out structural learning and causality analysis to investigate the conversation strategies used by the trained moderators to engage socially isolated adults in the conversation and the causal effects of the strategy on engagement. Causal relations and effects were inferred between participants' emotions, the dialogue strategies used by moderators, and participants' following emotions. The results found in this paper may be used to support the development of cost-efficient, trustworthy AI- and/or robot-based platform to promote conversational engagement for older adults to address the challenges in social interaction.

Keywords: Causal inference; Conversational engagement; Mild cognitive impairment; Older adults; Social isolation.

Associated data

  • ClinicalTrials.gov/NCT02871921