An effective screening model for subjective cognitive decline in community-dwelling older adults based on gait analysis and eye tracking

• Published in: Frontiers in aging neuroscience Vol. 16; p. 1444375
• Main Authors: Hao, Chenxi, Zhang, Xiaonan, An, Junpin, Bao, Wenjing, Yang, Fan, Chen, Jinyu, Hou, Sijia, Wang, Zhigang, Du, Shuning, Zhao, Yarong, Wang, Qiuyan, Min, Guowen, Li, Yang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 25.09.2024
• Subjects: Aging Neuroscience; eye tracking; gait analysis; in the community; machine learning; screening model; subjective cognitive decline; in the community; subjective cognitive decline; machine learning; eye tracking; gait analysis; screening model
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2024.1444375

To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community. In the study, 412 cognitively normal older adults aged over 65 years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline. A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (  < 0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, anti-saccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC = 0.969). The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community.