Changes in Conversational Pattern as a Clinical Trial Outcome Using the I-CONECT Data
Liu Chen, Chao-Yi Wu, Hiroko Dodge

TL;DR
This study shows that conversational patterns can change after an intervention, suggesting potential for monitoring cognitive health in clinical trials.
Contribution
The study introduces semantic noise as a novel NLP-based measure to track conversational changes in clinical trials.
Findings
At baseline, the MCI group had significantly higher semantic noise than the NC group.
After six months, the MCI group's semantic noise decreased to match the NC group's levels.
This suggests conversational interventions may improve cognitive function in MCI patients.
Abstract
Linguistic measures derived from spontaneous conversations are behavioral indicators linked to cognitive functions. We investigated a natural language processing measure, semantic noise (SN), to quantify conversational patterns and monitor intervention-induced changes. We analyzed transcriptions from semi-structured conversations in the I-CONECT project (ClinicalTrials.gov: NCT02871921). Participants engaged in 30-minute semi-structured conversations four times per week via online video chats for six months. Mean SN (MSN) from the second and 24th weeks’ conversations represented participants’ conversational patterns at baseline and month six (M6), respectively. Two-tailed t-tests and paired t-tests are used for statistical analysis. Among participants who completed baseline and M6 video chat interventions, the MCI group’s MSN was significantly higher than the NC group’s (MCI: 2.09 ±…
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Taxonomy
TopicsDigital Mental Health Interventions · Neurobiology of Language and Bilingualism · Patient-Provider Communication in Healthcare
