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用于促进老年人健康和预防疾病的可穿戴技术:系统综述与证据图谱

Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.

作者信息

Sun Yue, Chen Ji, Ji Mengmeng, Li Xiaomei

机构信息

School of Nursing, Health Science Center, Xi'an Jiaotong University, Xi'an, China.

Mianyang Research Institute of Traditional Chinese Medicine, Mianyang Hospital of Traditional Chinese Medicine, Mianyang, China.

出版信息

J Med Internet Res. 2025 Jun 24;27:e69077. doi: 10.2196/69077.


DOI:10.2196/69077
PMID:40553512
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12238792/
Abstract

BACKGROUND: The demand for wearable technologies has surged in recent years, demonstrating remarkable potential, especially in health promotion. However, there is currently a lack of clarity about the types and roles of wearable devices in health care of older adults. OBJECTIVE: This review aims to provide a comprehensive overview and categorize the current research conducted with wearable devices for health promotion and disease prevention in older adults. METHODS: We conducted a systematic literature review using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework and synthesized the results. A total of 6 databases were searched to identify wearable devices reported in studies from inception to July 28, 2024. Titles, abstracts, and full texts were independently screened by 2 reviewers. Any discrepancies were resolved by a third reviewer when necessary. The types of results from relevant studies were systematically mapped into predefined categories. RESULTS: Based on the inclusion criteria, 109 studies were included. The most commonly reported health targets of wearable devices were mobility, mental health, fall-related, arrhythmia detection, activity recognition, disease diagnosis, and sleep monitoring. Most studies were application design and observational study, and in European countries and the United States, 51 studies of the participants were healthy. The most popular anatomical landmarks for wearable placement were the wrist, waist, and chest. Two evaluation approaches for wearable devices were used: performance metrics in controlled settings and real-world assessments with end users. The opportunities presented by wearable devices are countered by multiple challenges, including data availability and reliability, technical limitations, utility and user acceptance, cost, security and privacy, performance gaps, and challenges. CONCLUSIONS: Wearable devices hold great promise for promoting health in older adults, but several hurdles remain for full adoption. A broader and more diverse group of older adults is needed to identify the most beneficial wearables and to optimize the technology. Further studies are required to statistically synthesize real-world performance and evaluation results. We hope that this review will serve as a valuable reference for the development of wearable devices in older adults.

摘要

背景:近年来,对可穿戴技术的需求激增,显示出巨大潜力,尤其是在促进健康方面。然而,目前可穿戴设备在老年人医疗保健中的类型和作用尚不清楚。 目的:本综述旨在全面概述并对目前使用可穿戴设备促进老年人健康和预防疾病的研究进行分类。 方法:我们使用PRISMA-ScR(系统评价和Meta分析扩展的范围综述的首选报告项目)框架进行了系统的文献综述,并综合了结果。共检索了6个数据库,以识别从研究开始到2024年7月28日报道的可穿戴设备。标题、摘要和全文由2名审稿人独立筛选。如有任何差异,必要时由第三名审稿人解决。相关研究的结果类型被系统地映射到预定义的类别中。 结果:根据纳入标准,纳入了109项研究。可穿戴设备最常报告的健康目标是 mobility、心理健康、跌倒相关、心律失常检测、活动识别、疾病诊断和睡眠监测。大多数研究是应用设计和观察性研究,在欧洲国家和美国,51项研究的参与者是健康的。可穿戴设备放置最受欢迎的解剖部位是手腕、腰部和胸部。使用了两种可穿戴设备评估方法:在受控环境中的性能指标和与最终用户的实际评估。可穿戴设备带来的机遇受到多种挑战的阻碍,包括数据可用性和可靠性、技术限制、实用性和用户接受度、成本、安全性和隐私、性能差距以及挑战。 结论:可穿戴设备在促进老年人健康方面具有巨大潜力,但要全面采用仍存在一些障碍。需要更广泛、更多样化的老年人群体来确定最有益的可穿戴设备并优化技术。需要进一步研究以统计方式综合实际性能和评估结果。我们希望本综述将为老年人可穿戴设备的开发提供有价值的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/e9367fc98d6b/jmir_v27i1e69077_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/1e21e231a32f/jmir_v27i1e69077_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/7cd00592b120/jmir_v27i1e69077_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/f9d9fa813644/jmir_v27i1e69077_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/9b2e4f2107c3/jmir_v27i1e69077_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/e9367fc98d6b/jmir_v27i1e69077_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/1e21e231a32f/jmir_v27i1e69077_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/7cd00592b120/jmir_v27i1e69077_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/f9d9fa813644/jmir_v27i1e69077_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/9b2e4f2107c3/jmir_v27i1e69077_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6764/12238792/e9367fc98d6b/jmir_v27i1e69077_fig5.jpg

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本文引用的文献

[1]
Wearable Technology and Chronic Illness: Balancing Justice and Care Ethics.

Cureus. 2024-11-14

[2]
Wearable Devices for Supporting Chronic Disease Self-Management: Scoping Review.

Interact J Med Res. 2024-12-9

[3]
Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study.

NPJ Aging. 2024-11-25

[4]
The frail-LESS (LEss sitting and sarcopenia in frail older adults) remote intervention to improve sarcopenia and maintain independent living via reductions in sedentary behaviour: findings from a randomised controlled feasibility trial.

BMC Geriatr. 2024-9-9

[5]
A novel health monitoring system for vital signs using IoT.

Sci Rep. 2024-8-19

[6]
Exploring the Experiences of Community-Dwelling Older Adults on Using Wearable Monitoring Devices With Regular Support From Community Health Workers, Nurses, and Social Workers: Qualitative Descriptive Study.

J Med Internet Res. 2024-8-7

[7]
An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

J Med Internet Res. 2024-8-5

[8]
Further acceleration in fertility decline in 2023: deviation of Recently published provisional fertility estimates in selected OECD countries from those in the 2022 revision of the world population prospects.

Biodemography Soc Biol. 2024

[9]
Promoting child and adolescent health through wearable technology: A systematic review.

Digit Health. 2024-6-11

[10]
A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.

NPJ Digit Med. 2024-5-25

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