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GAIT-CKD（用于慢性肾脏病患者数字疗法的人工智能步态分析）：设计与方法

GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.

作者信息

Song Youngjin, Jeong In Cheol, Ryu Semin, Lee Sunghan, Koh Jeonghwan, Jeong Seokjue, Park Seongmin, Kim Munsang, Lee Wonjun, Rye Okhyeon, Kim Yeojin, Lee Sanggyu, Ahn Mooeob, Kim Hyunsuk

机构信息

Department of Internal Medicine, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.

Division of AI Convergence, College of Infomation Science, Hallym University, Chuncheon, Republic of Korea.

出版信息

Kidney Res Clin Pract. 2025 Sep;44(5):788-801. doi: 10.23876/j.krcp.23.273. Epub 2024 Aug 22.

DOI:10.23876/j.krcp.23.273
PMID:39384362
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12417576/
Abstract

BACKGROUND

Digital therapeutics are emerging as treatments for diseases and disabilities. In chronic kidney disease (CKD), gait is a potential biomarker for health status and intervention effectiveness. This study aims to analyze gait characteristics in CKD patients, providing baseline data for digital therapeutics development.

METHODS

At baseline and after an 8-week intervention, we performed bioimpedance analysis measurements, the Timed Up and Go, Tinetti, and grip strength tests, and gait analysis in 217 healthy individuals and 276 patients with CKD. Demographic and clinical information was collected, including underlying diseases and medications, laboratory tests, and quality of life satisfaction surveys. Gait analysis was performed using skeleton data, which involved acquiring three-dimensional skeleton data of a walker using a single Kinect sensor. The performance of an artificial intelligence-based classification model in distinguishing between healthy individuals and those with CKD was then investigated. Simultaneously, inertia measurement unit analysis was conducted using measurements taken from the wrist and waist.

RESULTS

Most subjects received a health intervention via an app, and their gait was assessed for improvements after an 8-week period. Incidents such as falls, fractures, hospitalizations, and deaths will be investigated in years 1 and 3.

CONCLUSION

This study confirmed that the gaits of healthy individuals and CKD patients were different, and the effect of the 8-week app-based health intervention will be analyzed. The study will yield important baseline data for creating digital therapeutics for CKD patients' diet/exercise in the future.

摘要

背景

数字疗法正逐渐成为治疗疾病和残疾的手段。在慢性肾脏病（CKD）中，步态是健康状况和干预效果的潜在生物标志物。本研究旨在分析CKD患者的步态特征，为数字疗法的开发提供基线数据。

方法

在基线期和8周干预后，我们对217名健康个体和276名CKD患者进行了生物电阻抗分析测量、定时起立行走测试、Tinetti测试、握力测试和步态分析。收集了人口统计学和临床信息，包括基础疾病和用药情况、实验室检查以及生活质量满意度调查。使用骨骼数据进行步态分析，这涉及使用单个Kinect传感器获取步行者的三维骨骼数据。然后研究基于人工智能的分类模型在区分健康个体和CKD患者方面的性能。同时，使用从手腕和腰部获取的测量数据进行惯性测量单元分析。

结果

大多数受试者通过应用程序接受了健康干预，并在8周后评估了他们的步态改善情况。在第1年和第3年将调查跌倒、骨折、住院和死亡等事件。

结论

本研究证实健康个体和CKD患者的步态不同，并将分析基于应用程序的8周健康干预的效果。该研究将为未来为CKD患者的饮食/运动创建数字疗法提供重要的基线数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/1dff9aa8efe4/j-krcp-23-273f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/1e6b21bb173a/j-krcp-23-273f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/7abc49afb60f/j-krcp-23-273f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/a83c0ff4131e/j-krcp-23-273f3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/437c47df8a43/j-krcp-23-273f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/fec7884373dd/j-krcp-23-273f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/1dff9aa8efe4/j-krcp-23-273f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/1e6b21bb173a/j-krcp-23-273f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/7abc49afb60f/j-krcp-23-273f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/a83c0ff4131e/j-krcp-23-273f3.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/fec7884373dd/j-krcp-23-273f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f079/12417576/1dff9aa8efe4/j-krcp-23-273f7.jpg

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