Su LM, Wu B, Chen Z, Wang XY, Shen XH, Wei ZQ, Cheng H, Wang LN. Development and validation of screening tools for motoric cognitive risk syndrome in community settings. World J Psychiatry 2025; 15(8): 105433 [DOI: 10.5498/wjp.v15.i8.105433]
Corresponding Author of This Article
Li-Na Wang, PhD, Professor, School of Medicine, Huzhou University, No. 759 Erhuan East Road, Huzhou 313000, Zhejiang Province, China. 02474@zjhu.edu.cn
Research Domain of This Article
Geriatrics & Gerontology
Article-Type of This Article
Randomized Clinical Trial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Aug 19, 2025; 15(8): 105433 Published online Aug 19, 2025. doi: 10.5498/wjp.v15.i8.105433
Development and validation of screening tools for motoric cognitive risk syndrome in community settings
Li-Ming Su, Bei Wu, Zhang Chen, Xiao-Yan Wang, Xin-Hua Shen, Zhu-Qin Wei, Huang Cheng, Li-Na Wang
Li-Ming Su, Zhu-Qin Wei, Huang Cheng, Li-Na Wang, School of Medicine, Huzhou University, Huzhou 313000, Zhejiang Province, China
Bei Wu, Rory Meyers College of Nursing, New York University, New York, NY 10010, United States
Zhang Chen, Xiao-Yan Wang, Renhuangshan Binhu Community Health Service Centre, Huzhou 313000, Zhejiang Province, China
Xin-Hua Shen, Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou 313000, Zhejiang Province, China
Author contributions: Su LM and Wang LN contributed to the study conception and design, conducted to the acquisition, curation and analysis of data, drafted the manuscript; Chen Z, Wang XY and Shen XH supervised the research; Chen Z conducted to the collection and processing of pre-existing data; Wang XY, Shen XH, Cheng H, Wei ZQ and Wu B provided methodological input on data analysis; Wu B revised the manuscript critically for important intellectual content; All authors have read and approve the final manuscript.
Supported by the National Natural Science Foundation of China, No. 72174061 and No. 71704053; China Scholarship Council Foundation, No. 202308330251; and Health Science and Technology Project of Zhejiang Provincial Health Commission, No. 2022KY370 and No. 2023KY1186.
Institutional review board statement: The study was reviewed and approved by the Third People’s Hospital Institutional Review Board (approval No. 2021-025).
Clinical trial registration statement: This study is registered at https://www.chictr.org.cn/. The registration identification number is ChiCTR2200059090.
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at 02474@zjhu.edu.cn.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Li-Na Wang, PhD, Professor, School of Medicine, Huzhou University, No. 759 Erhuan East Road, Huzhou 313000, Zhejiang Province, China. 02474@zjhu.edu.cn
Received: January 22, 2025 Revised: April 15, 2025 Accepted: June 13, 2025 Published online: August 19, 2025 Processing time: 198 Days and 21.7 Hours
Abstract
BACKGROUND
Motoric cognitive risk (MCR) syndrome represents an “ultra-early” stage of dementia prevention, highlighting the need for effective screening tools.
AIM
To develop and validate a novel tool for MCR identification, comparing its effectiveness with existing methods.
METHODS
As part of a community study on healthy aging, a cross-sectional study recruited 1189 Chinese participants aged 50 years and older between May 1, 2022, and March 15, 2023. The cohort was randomly split into training (70%) and testing (30%) datasets. Relevant features were selected for logistic regression (LR) and decision tree (DT) models using the training dataset, and their performance was subsequently assessed using the testing dataset to validate reliability and generalizability.
RESULTS
The prevalence of MCR was 13.12% among 1189 participants. DT models had the area under the curves (AUCs) of 0.834 and 0.821 for training and testing datasets, respectively, while LR models indicated AUCs of 0.840 and 0.859. Non-inferiority tests confirmed the DT model’s comparable effectiveness to the LR models in predicting MCR. Both models demonstrated good calibration and clinical utility. Seven modifiable risk factors were identified: Age, education level, social engagement, physical activity, nutritional status, depressive symptoms, and purpose in life. Notably, social engagement emerged as a novel factor compared to those previously identified. Both models are integrated into an easy-to-use, interpretable web-based user interface.
CONCLUSION
The interactive, web-based user interface of both models effectively identifies MCR, with the DT model recommended for its simplicity and interpretability, supporting community nurses and clinicians in triaging MCR.
Core Tip: The current study employed decision tree (DT) and logistic regression (LR) models to predict motoric cognitive risk (MCR) syndrome by identifying key modifiable risk factors spanning the demographic, lifestyle, and health-related domains. Feature selection was performed using the least absolute shrinkage and selection operator, with LR being used to develop a nomogram and DT being used to construct a classification tree. Both models were integrated into a user-friendly, web-based interface. This is the first known application of machine learning for predicting MCR, demonstrating that the DT model is effective in supporting community nurses and clinicians in triaging MCR due to its simplicity and interpretability.
