Changes in conversational pattern as a clinical trial outcome: a proof-of-concept study using the I-CONECT data.

Background and Objectives: Recent advancements in natural language processing show that linguistic measures can detect subtle cognitive decline. However, most analyses rely on restrictive tasks (eg, picture descriptions). In this proof-of-concept study, we use semantic noise (SN) to quantify conversational patterns and monitor changes following a behavioral intervention. Methods: We analyzed text derived from semi-structured conversations of the I-CONECT project. Participants in the intervention group, who were either cognitively normal (normal cognition [NC]) or had mild cognitive impairment (MCI), engaged in frequent conversational interactions (4 times/week) via an internet/webcam for 6 months. The mean semantic noise (MSN) from the second week and 24th week was used to represent the baseline and month 6, respectively. Results: Using a 2-tailed Discussion and Implications: After 6 months of conversational engagement, the MCI group's MSN came to resemble that of the NC group. These findings suggest that linguistic measures derived from spontaneous, naturalistic conversations may be sensitive to cognitive changes over time. Further research on extracting linguistic measurements from spontaneous conversations could facilitate cognitive monitoring in daily life. Clinical Trial Registration Number: NCT02871921.