journal articles
EFFECTIVENESS OF DIGITAL SCREENING TOOLS IN DETECTING COGNITIVE IMPAIRMENT AMONG COMMUNITY-DWELLING ELDERLY IN NORTHERN CHINA: A LARGE COHORT STUDY
Xiaonan Zhang, Feifei Zhang, Sijia Hou, Chenxi Hao, Xiangmin Fan, Yarong Zhao, Wenjing Bao, Junpin An, Shuning Du, Guowen Min, Qiuyan Wang, Wencheng Zhu, Yang Li, Hui Zhang
INTRODUCTION: This study assessed the effectiveness of three digital screening tools in detecting cognitive impairment (CI) in a large cohort of community-dwelling elderly individuals and investigated the relationship between key digital features and plasma p-tau217 levels.
METHODS: This community-based cohort study included 1,083 participants aged 65 years or older, with 337 diagnosed with CI and 746 classified as normal controls (NC). We utilized two screening approaches: traditional methods (AD8, MMSE scale, and APOE genotyping) and digital tools (drawing, gait, and eye tracking). LightGBM-based machine learning models were developed for each digital screening tool and their combination, and their performance was evaluated. The correlation between key digital features and plasma p-tau217 levels was analyzed as well.
RESULTS: A total of 21 drawing, 71 gait, and 35 eye-tracking parameters showed significant differences between the two groups (all p < 0.05). The area under the curve (AUC) values for the drawing, gait, and eye-tracking models in distinguishing CI from NC were 0.860, 0.848, and 0.895, respectively. The combination of eye-tracking and drawing achieved the highest classification effectiveness, with an AUC of 0.958, and accuracy, sensitivity, and specificity all exceeded 85%. The fusion model achieved an AUC of 0.928 in distinguishing mild cognitive impairment (MCI) from NC. Additionally, several digital features (including two drawing, ten gait, and one eye-tracking parameters) were significantly correlated with plasma p-tau217 levels (all |r| > 0.3, p < 0.001).
DISCUSSION: Digital screening tools offer objective, accurate, and efficient alternatives for detecting CI in community settings, with the fusion of drawing and eye-tracking providing the best performance (AUC = 0.958).
CITATION:
Xiaonan Zhang ; Feifei Zhang ; Sijia Hou ; Chenxi Hao ; Xiangmin Fan ; Yarong Zhao ; Wenjing Bao ; Junpin An ; Shuning Du ; Guowen Min ; Qiuyan Wang ; Wencheng Zhu ; Yang Li ; Hui Zhang (2025): Effectiveness of digital screening tools in detecting cognitive impairment among community-dwelling elderly in Northern China: A large cohort study. The Journal of Prevention of Alzheimer’s Disease (JPAD). https://doi.org/10.1016/j.tjpad.2025.100080