多模式系统在老年人虚弱早期检测中的先验可接受性

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL Irbm Pub Date : 2023-10-01 DOI:10.1016/j.irbm.2023.100775
Joaquim Prud'Homm , Fabien Lemoine , Manuel Abbas , Guy Carrault , Dominique Somme , Régine Le Bouquin Jeannès
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引用次数: 0

摘要

引言在法国,每5名85岁及以上的人中就有4人会受到虚弱的影响。方法作为一个全球项目的一部分,该项目旨在开发一个多模式系统,用于在家中或独立老年公寓中的老年人中早期检测虚弱,我们报道了一项前瞻性定量研究,该研究基于Bel的综合模型,专门用于使用未知技术进行行为预测,评估该系统的先验可接受性。该平台由5个设备组成:体重秤、张力计、腕戴式步数计、活动跟踪器和平板电脑,用于通过互联网与上述传感器交换数据。纳入标准为:年龄2:80岁,住在家里或独立的老年公寓,评估为“强健”或“前期虚弱”。结果我们纳入34名志愿者(24名女性和10名男性),平均年龄85.6岁(±4.1),其中24人被归类为“强壮”,10人被归类于“前期虚弱”。多模式系统的先验效用(平均得分=6.0(±0.9))、先验使用意图(平均得分=5.9(±0.8))和首次使用前设备的推荐率(82.4%(±17.4))显然有利于该设备。结论本研究有利于系统的先验可接受性和先验意图的使用,对系统的发展相当有利。这些结果表明,为了获得良好的可接受性,需要这种连接系统的显著易用性和几乎完美的功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Priori Acceptability of a Multimodal System for the Early Detection of Frailty in Older Adults

Introduction

Frailty would affect 4 out of 5 people aged of 85 and over in France.

Methods

As part of a global project aimed to develop a multimodal system for the early detection of frailty among older adults living at home or in independent senior living apartments, we reported prospective quantitative study assessing a priori acceptability of this system based on Bel's integrative model devoted to the behavioral prediction use of an unknown technology. The platform is composed of 5 devices: a weight scale, a tensiometer, a wrist-worn step counter, an activity tracker and a tablet to exchange data with the aforementioned sensors over the internet. The inclusion criteria are: age 2: 80 years old, living at home or in independent senior living apartments, assessed as ‘robust’ or ‘pre-frail’. The a priori acceptability is assessed through self-evaluation questionnaires, mainly using a continuous scale (min-max score 1-7).

Results

We included 34 volunteers (24 women and 10 men), mean age 85.6 years (± 4.1), 24 of whom were classified as ‘robust’ and 10 as ‘pre-frail’. A priori utility of the multimodal system (mean score = 6.0 (± 0.9)), a priori intention of use (mean score = 5.9 (± 0.8)) and the rate of recommendation of the device before its first use (82.4% (± 17.4)) were clearly in favor of the device.

Conclusion

This study is in favor of an a priori acceptability and an a priori intention to use rather favorable to the developing system. Those results point a need for significant ease of use and almost-perfect functioning of this connected system for good acceptability.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
期刊最新文献
Editorial Board Contents Potential of Near-Infrared Optical Techniques for Non-invasive Blood Glucose Measurement: A Pilot Study Corrigendum to “Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models” [IRBM (2023) 100725] Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
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