Yin-Xia Ren, Bei Wu, Jian-Lin Lou, Xiao-Rong Zhu, Chen Zhang, Qing Lang, Zhu-Qin Wei, Li-Ming Su, Heng-Nian Qi, Li-Na Wang
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Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.</p><p><strong>Results: </strong>Compared to the cognitively normal group, the MCI group had slower gait velocity (<i>Z</i> = -2.911, <i>P</i> = 0.004), shorter stride and step lengths (<i>t</i> = -3.005, <i>P</i> = 0.003; <i>t</i> = 2.863, <i>P</i> = 0.005), and longer cycle, standing, and double support times (<i>t</i> = -2.274, <i>P</i> = 0.025; <i>t</i> = -2.376, <i>P</i> = 0.018; <i>t</i> = -2.717, <i>P</i> = 0.007). They also had reduced cadence (<i>t</i> = 2.060, <i>P</i> = 0.042) and increased double support time variability (<i>Z</i> = -2.614, <i>P</i> = 0.009). In handwriting, the MCI group showed lower average pressure (all tasks: Z = -2.135, <i>P</i> = 0.033) and decreased accuracy (graphic task: <i>Z</i> = -2.447, <i>P</i> = 0.014; Chinese character task: <i>Z</i> = -3.078, <i>P</i> = 0.002). In the graphic task, they demonstrated longer time in air (Z = -2.865, <i>P</i> = 0.004), reduced X-axis maximum velocities (<i>Z</i> = -3.237, <i>P</i> = 0.001), and lower accelerations (X-axis: <i>Z</i> = -2.880, <i>P</i> = 0.004; Y-axis: <i>Z</i> = -1.987, <i>P</i> = 0.047) and maximum accelerations (X-axis: <i>Z</i> = -3.998, <i>P</i> < 0.001; Y-axis: <i>Z</i> = -2.050, <i>P</i> = 0.040). The multimodal analysis achieved the highest accuracy (74.4%) with the Gradient Boosting Classifier.</p><p><strong>Conclusion: </strong>Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI, potentially supporting large-scale screening, especially in resource-limited settings.</p>","PeriodicalId":23896,"journal":{"name":"World Journal of Psychiatry","volume":"15 9","pages":"109478"},"PeriodicalIF":3.4000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417942/pdf/","citationCount":"0","resultStr":"{\"title\":\"Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters analysis.\",\"authors\":\"Yin-Xia Ren, Bei Wu, Jian-Lin Lou, Xiao-Rong Zhu, Chen Zhang, Qing Lang, Zhu-Qin Wei, Li-Ming Su, Heng-Nian Qi, Li-Na Wang\",\"doi\":\"10.5498/wjp.v15.i9.109478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Older adults with mild cognitive impairment (MCI) often show motor dysfunction, including slower gait and impaired handwriting. While gait and handwriting parameters are promising for MCI screening, their combined potential to distinguish MCI from cognitively normal adults is unclear.</p><p><strong>Aim: </strong>To assess gait and handwriting differences and their potential for screening MCI in older adults.</p><p><strong>Methods: </strong>Ninety-five participants, including 34 with MCI and 61 cognitively normal controls, were assessed for gait using the GAITRite<sup>®</sup> system and handwriting with a dot-matrix pen. Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.</p><p><strong>Results: </strong>Compared to the cognitively normal group, the MCI group had slower gait velocity (<i>Z</i> = -2.911, <i>P</i> = 0.004), shorter stride and step lengths (<i>t</i> = -3.005, <i>P</i> = 0.003; <i>t</i> = 2.863, <i>P</i> = 0.005), and longer cycle, standing, and double support times (<i>t</i> = -2.274, <i>P</i> = 0.025; <i>t</i> = -2.376, <i>P</i> = 0.018; <i>t</i> = -2.717, <i>P</i> = 0.007). They also had reduced cadence (<i>t</i> = 2.060, <i>P</i> = 0.042) and increased double support time variability (<i>Z</i> = -2.614, <i>P</i> = 0.009). In handwriting, the MCI group showed lower average pressure (all tasks: Z = -2.135, <i>P</i> = 0.033) and decreased accuracy (graphic task: <i>Z</i> = -2.447, <i>P</i> = 0.014; Chinese character task: <i>Z</i> = -3.078, <i>P</i> = 0.002). In the graphic task, they demonstrated longer time in air (Z = -2.865, <i>P</i> = 0.004), reduced X-axis maximum velocities (<i>Z</i> = -3.237, <i>P</i> = 0.001), and lower accelerations (X-axis: <i>Z</i> = -2.880, <i>P</i> = 0.004; Y-axis: <i>Z</i> = -1.987, <i>P</i> = 0.047) and maximum accelerations (X-axis: <i>Z</i> = -3.998, <i>P</i> < 0.001; Y-axis: <i>Z</i> = -2.050, <i>P</i> = 0.040). The multimodal analysis achieved the highest accuracy (74.4%) with the Gradient Boosting Classifier.</p><p><strong>Conclusion: </strong>Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI, potentially supporting large-scale screening, especially in resource-limited settings.</p>\",\"PeriodicalId\":23896,\"journal\":{\"name\":\"World Journal of Psychiatry\",\"volume\":\"15 9\",\"pages\":\"109478\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417942/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5498/wjp.v15.i9.109478\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5498/wjp.v15.i9.109478","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
摘要
背景：患有轻度认知障碍（MCI）的老年人通常表现为运动功能障碍，包括步态变慢和书写受损。虽然步态和书写参数有望用于MCI筛查，但它们将MCI与认知正常成人区分开来的综合潜力尚不清楚。目的：评估老年人步态和笔迹差异及其筛查轻度认知损伤的潜力。方法：95名参与者，包括34名轻度认知障碍患者和61名认知正常对照，使用GAITRite®系统评估步态和点阵笔笔迹。开发了五种机器学习模型来评估步态和手写数据对MCI筛查的判别能力。结果：与认知正常组相比，MCI组步态速度变慢（Z = -2.911, P = 0.004），步幅和步长变短（t = -3.005, P = 0.003; t = 2.863, P = 0.005），循环、站立和双支撑次数变长（t = -2.274, P = 0.025; t = -2.376, P = 0.018; t = -2.717, P = 0.007）。他们也有减少的节奏（t = 2.060, P = 0.042）和增加的双支持时间变异性（Z = -2.614, P = 0.009）。在书写方面，MCI组表现出较低的平均压力（所有任务：Z = -2.135, P = 0.033）和较低的准确率（图形任务：Z = -2.447, P = 0.014；汉字任务：Z = -3.078, P = 0.002）。在图形任务中，他们表现出在空中的时间更长（Z = -2.865, P = 0.004）， x轴最大速度降低（Z = -3.237, P = 0.001），加速度降低（x轴：Z = -2.880, P = 0.004； y轴：Z = -1.987, P = 0.047）和最大加速度降低（x轴：Z = -3.998, P < 0.001； y轴：Z = -2.050, P = 0.040）。使用梯度增强分类器的多模态分析达到了最高的准确率（74.4%）。结论：整合步态和手写运动学参数为区分MCI提供了可行的方法，可能支持大规模筛查，特别是在资源有限的情况下。
Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters analysis.
Background: Older adults with mild cognitive impairment (MCI) often show motor dysfunction, including slower gait and impaired handwriting. While gait and handwriting parameters are promising for MCI screening, their combined potential to distinguish MCI from cognitively normal adults is unclear.
Aim: To assess gait and handwriting differences and their potential for screening MCI in older adults.
Methods: Ninety-five participants, including 34 with MCI and 61 cognitively normal controls, were assessed for gait using the GAITRite® system and handwriting with a dot-matrix pen. Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.
Results: Compared to the cognitively normal group, the MCI group had slower gait velocity (Z = -2.911, P = 0.004), shorter stride and step lengths (t = -3.005, P = 0.003; t = 2.863, P = 0.005), and longer cycle, standing, and double support times (t = -2.274, P = 0.025; t = -2.376, P = 0.018; t = -2.717, P = 0.007). They also had reduced cadence (t = 2.060, P = 0.042) and increased double support time variability (Z = -2.614, P = 0.009). In handwriting, the MCI group showed lower average pressure (all tasks: Z = -2.135, P = 0.033) and decreased accuracy (graphic task: Z = -2.447, P = 0.014; Chinese character task: Z = -3.078, P = 0.002). In the graphic task, they demonstrated longer time in air (Z = -2.865, P = 0.004), reduced X-axis maximum velocities (Z = -3.237, P = 0.001), and lower accelerations (X-axis: Z = -2.880, P = 0.004; Y-axis: Z = -1.987, P = 0.047) and maximum accelerations (X-axis: Z = -3.998, P < 0.001; Y-axis: Z = -2.050, P = 0.040). The multimodal analysis achieved the highest accuracy (74.4%) with the Gradient Boosting Classifier.
Conclusion: Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI, potentially supporting large-scale screening, especially in resource-limited settings.
期刊介绍：
The World Journal of Psychiatry (WJP) is a high-quality, peer reviewed, open-access journal. The primary task of WJP is to rapidly publish high-quality original articles, reviews, editorials, and case reports in the field of psychiatry. In order to promote productive academic communication, the peer review process for the WJP is transparent; to this end, all published manuscripts are accompanied by the anonymized reviewers’ comments as well as the authors’ responses. The primary aims of the WJP are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in psychiatry.
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