Neuropsychology · 2026
Cognitive health on the fingertips: Fine motor deficiencies for assessing the risk of mild cognitive impairment in the aging adults.
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Zhao Yejing, Zhang Jie, Zhao Yanyan, Yuan Ying, Shen Ji, Peng Hongyu, Li Jing
Аннотация
OBJECTIVE: Mild cognitive impairment (MCI) represents a critical prodromal stage associated with Alzheimer's disease and other age-related neurocognitive conditions in middle-old aging adults, yet research exploring its association with fine motor skill deficits remains relatively limited. This study explores the specific characteristics of fine motor function in middle-old aging adults with MCI using the latest wearable inertial motion capture technology.
METHOD: A computerized system with wearable inertial motion capture technology captured and assessed fine motions. The least absolute shrinkage and selection operator-based machine learning algorithm was utilized to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve. Decision curve analysis, clinical impact curve analysis, and bootstrap validation were used to evaluate and validate the stability of models.
RESULTS: A total of 289 participants aged ≥50 were recruited, of which 140 participants experienced MCI. Compared with the control group, patients with MCI showed significantly poorer fine motor performance in hand function assessment tasks. Spearman correlation analysis revealed a stronger correlation between fine motor function and visuospatial/executive function. A multivariable nomogram model based on fine motion parameters showed satisfactory discrimination, with an area under the curve of .773. Decision curve analysis, clinical impact curve analysis, and bootstrap validation demonstrated the good clinical utility and stability of the model.
CONCLUSIONS: Poor motor functioning was associated with MCI status. The nomogram model, based on fine motion parameters, demonstrated moderate discriminative performance in assessing the risk of MCI, serving as a valuable clinical supplement. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
METHOD: A computerized system with wearable inertial motion capture technology captured and assessed fine motions. The least absolute shrinkage and selection operator-based machine learning algorithm was utilized to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve. Decision curve analysis, clinical impact curve analysis, and bootstrap validation were used to evaluate and validate the stability of models.
RESULTS: A total of 289 participants aged ≥50 were recruited, of which 140 participants experienced MCI. Compared with the control group, patients with MCI showed significantly poorer fine motor performance in hand function assessment tasks. Spearman correlation analysis revealed a stronger correlation between fine motor function and visuospatial/executive function. A multivariable nomogram model based on fine motion parameters showed satisfactory discrimination, with an area under the curve of .773. Decision curve analysis, clinical impact curve analysis, and bootstrap validation demonstrated the good clinical utility and stability of the model.
CONCLUSIONS: Poor motor functioning was associated with MCI status. The nomogram model, based on fine motion parameters, demonstrated moderate discriminative performance in assessing the risk of MCI, serving as a valuable clinical supplement. (PsycInfo Database Record (c) 2026 APA, all rights reserved).