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ORIGINAL RESEARCH article

Front. Aging Neurosci.
Sec. Alzheimer's Disease and Related Dementias
Volume 16 - 2024 | doi: 10.3389/fnagi.2024.1444375

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

Provisionally accepted
Chenxi Hao Chenxi Hao 1Junpin An Junpin An 1*Wenjing Bao Wenjing Bao 1*Xiaonan Zhang Xiaonan Zhang 2Fan Yang Fan Yang 3Jinyu Chen Jinyu Chen 1Sijia Hou Sijia Hou 1*Zhigang Wang Zhigang Wang 4*Shuning Du Shuning Du 1*Yarong Zhao Yarong Zhao 1*Qiuyan Wang Qiuyan Wang 1*Guowen Min Guowen Min 1*Yang Li Yang Li 1*
  • 1 Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
  • 2 Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China
  • 3 Chinese Academy of Sciences (CAS), Beijing, Beijing, China
  • 4 Shanxi Medical University, Taiyuan, Shanxi Province, China

The final, formatted version of the article will be published soon.

    Objective 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. Methods 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. Results 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 (p<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.

    Keywords: Subjective cognitive decline, machine learning, gait analysis, eye tracking, Screening model, in the community

    Received: 05 Jun 2024; Accepted: 03 Sep 2024.

    Copyright: © 2024 Hao, An, Bao, Zhang, Yang, Chen, Hou, Wang, Du, Zhao, Wang, Min and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Junpin An, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Wenjing Bao, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Sijia Hou, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Zhigang Wang, Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
    Shuning Du, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Yarong Zhao, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Qiuyan Wang, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Guowen Min, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China
    Yang Li, Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China

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