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中风患者的身体表征:人体图形表征的系统综述

Body Representation in Stroke Patients: A Systematic Review of Human Figure Graphic Representation.

作者信息

Diyakonova Olga, Habib Valeria, Germanotta Marco, Taddei Ksenija, Bruschetta Roberta, Pioggia Giovanni, Tartarisco Gennaro, Aprile Irene Giovanna

机构信息

IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143 Florence, Italy.

National Research Council of Italy, Institute for Biomedical Research and Innovation, Via Leanza, Istituto Marino, 98164 Messina, Italy.

出版信息

J Clin Med. 2025 Apr 30;14(9):3098. doi: 10.3390/jcm14093098.

Abstract

Body representation is a complex process involving sensory, motor, and cognitive information. Frequently, it is disrupted after a stroke, impairing rehabilitation, emotional functioning, and daily functioning. The human figure graphic representation has emerged as a holistic tool to assess post-stroke outcomes. This systematic review examines the methodologies of human figure representation tests and their application in assessing post-stroke body representation, emphasizing its role in bridging subjective patient experiences with objective metrics. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A literature search was conducted through the databases PubMed, Scopus, Embase, Web of Science, and Google Scholar, collecting publications eligible for qualitative analysis. We selected studies where patients drew human figures in the study design to assess body representation, involving exclusively the adult stroke population. The Newcastle-Ottawa Scale was used to assess the risk of bias. Ten studies were analyzed. The tool demonstrated versatility in capturing unilateral spatial neglect, emotional disturbances, and functional independence. Qualitative metrics and quantitative indices correlated with cognitive deficits, mood disorders, and activities of daily living. Human figure representation also predicted rehabilitation outcomes, with improvements aligning with motor recovery. Innovations included digital quantification of evaluation metrics. Human figure graphic representation is a low-cost, adaptable tool bridging motor, cognitive, and emotional assessments in stroke survivors. While methodological variability persists, AI-driven analytics and standardized frameworks could enhance objectivity. Future research should prioritize validating parameters and developing hybrid models combining traditional qualitative insights with machine learning, thus advancing precision neurorehabilitation and personalized care.

摘要

身体表征是一个涉及感觉、运动和认知信息的复杂过程。中风后,它常常受到干扰,影响康复、情绪功能和日常功能。人体图形表征已成为评估中风后结果的一种综合工具。本系统评价考察了人体图形表征测试的方法及其在评估中风后身体表征中的应用,强调其在将患者主观体验与客观指标联系起来方面的作用。本评价遵循系统评价和荟萃分析的首选报告项目(PRISMA)声明。通过PubMed、Scopus、Embase、Web of Science和谷歌学术等数据库进行文献检索,收集符合定性分析的出版物。我们选择了在研究设计中让患者绘制人体图形以评估身体表征的研究,研究对象仅为成年中风患者。使用纽卡斯尔-渥太华量表评估偏倚风险。对10项研究进行了分析。该工具在捕捉单侧空间忽视、情绪障碍和功能独立性方面表现出多功能性。定性指标和定量指数与认知缺陷、情绪障碍和日常生活活动相关。人体图形表征还预测了康复结果,改善情况与运动恢复一致。创新包括评估指标的数字量化。人体图形表征是一种低成本、适应性强的工具,可在中风幸存者中衔接运动、认知和情绪评估。虽然方法学上的变异性仍然存在,但人工智能驱动的分析和标准化框架可以提高客观性。未来的研究应优先验证参数,并开发将传统定性见解与机器学习相结合的混合模型,从而推动精准神经康复和个性化护理的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/12072329/8fa180f34c89/jcm-14-03098-g001.jpg

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