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Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study

Using Machine Learning to Predict Cognitive Decline in Older Adults From the Chinese Longitudinal Healthy Longevity Survey: Model Development and Validation Study

Baseline subjects were screened by MMSE, and cognitive impairment was defined as an MMSE score Demographic predictors include age, gender, and BMI, calculated as weight in kilograms divided by height in meters squared (kg/m2). Data on life behaviors and disease history are collected from the CLHLS questionnaire. Life behaviors account for living status (living alone or not), current smoking and drinking habits, exercise practices, marital status, and overall activity ability and mental health.

Hao Ren, Yiying Zheng, Changjin Li, Fengshi Jing, Qiting Wang, Zeyu Luo, Dongxiao Li, Deyi Liang, Weiming Tang, Li Liu, Weibin Cheng

JMIR Aging 2025;8:e67437

A New Computer-Based Cognitive Measure for Early Detection of Dementia Risk (Japan Cognitive Function Test): Validation Study

A New Computer-Based Cognitive Measure for Early Detection of Dementia Risk (Japan Cognitive Function Test): Validation Study

In the total validation group participants, significant HRs for dementia incidence were found using the MMSE 23/24 and J-Cog 43/44 cutoffs (MMSE 23/24: HR 1.93, 95% CI 1.13-3.27; J-Cog 434/44: HR 2.42, 95% CI 1.50-3.93; Figure 2). The C-statistic was above 0.7 for all cutoff points. The ΔAIC with the MMSE 23/24 cutoff as a reference indicated that the MMSE 28/29 cutoff had poor discrimination, and the J-Cog 43/44 cutoff had good discrimination.

Hiroyuki Shimada, Takehiko Doi, Kota Tsutsumimoto, Keitaro Makino, Kenji Harada, Kouki Tomida, Masanori Morikawa, Hyuma Makizako

J Med Internet Res 2025;27:e59015

Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis

Factors Influencing Poststroke Cognitive Dysfunction: Cross-Sectional Analysis

Cognitive status was assessed using the Mini-Mental State Examination (MMSE), a widely used and validated tool for detecting cognitive impairment. The MMSE is particularly suitable for its practicality and ease of administration, providing a quick, general overview of cognitive function across several domains.

Wu Zhou, HaiXia Feng, Hua Tao, Hui Sun, TianTian Zhang, QingXia Wang, Li Zhang

JMIR Form Res 2024;8:e59572

Dietary Structure and Nutritional Status of Chinese Beekeepers: Demographic Health Survey

Dietary Structure and Nutritional Status of Chinese Beekeepers: Demographic Health Survey

The Mini-Mental State Examination (MMSE) was developed in 1975, and is a standardized tool to rapidly screen individuals for cognitive dysfunction [11]. It includes 11 questions involving orientation, attention, immediate and short-term recall, language, and the ability to follow simple verbal and written commands. The total score is 30 points, and it takes only about 5-10 minutes to administer. Descriptive epidemiological analysis methods include calculation rate, mean, etc.

Boshi Wang, Zhangkai Jason Cheng, Qian Xu, Tiangang Zhu, Lin Su, Mingshan Xue, Lin Pei, Li Zhu, Peng Liu

JMIR Public Health Surveill 2021;7(5):e28726