Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty

Frontiers in Public Health

Public Health

Zhong Pei¹  Bin Hu²  Jieshun Ye³  Zhimin Yang⁴  Fuping Xu⁴  Shaoyi Fan⁵  Runxin Peng⁵  Qing Xu⁵ 

[1] Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China;Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada;School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China;The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China;The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China;

关键词: digital health technology;    wearable sensor;    machine learning;    prediction model;    frailty;    gait;   

DOI  :  10.3389/fpubh.2023.1169083

 received in 2023-02-18, accepted in 2023-06-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundFrailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics.MethodsAs part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications.ResultsIt was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3–11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score.ConclusionThis study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.

【 授权许可】

Unknown   
Copyright © 2023 Fan, Ye, Xu, Peng, Hu, Pei, Yang and Xu.