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基于社区居住老年人的衰弱和肌少症综合征的 iPhone 应用程序检测的有效性:一项诊断准确性研究的方案。

Validity of an iPhone App to Detect Prefrailty and Sarcopenia Syndromes in Community-Dwelling Older Adults: The Protocol for a Diagnostic Accuracy Study.

机构信息

Physiotherapy Department, Faculty of Health Sciences, Universidad Católica de Murcia, Campus de los Jerónimos, 30107 Murcia, Spain.

Cystic Fibrosis Association of Murcia, Av. de las Palmeras, 37, 30120 Murcia, Spain.

出版信息

Sensors (Basel). 2022 Aug 11;22(16):6010. doi: 10.3390/s22166010.

DOI:10.3390/s22166010
PMID:36015771
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9416054/
Abstract

Prefrailty and sarcopenia in combination are more predictive of mortality than either condition alone. Early detection of these syndromes determines the prognosis of health-related adverse events since both conditions can be reversed through appropriate interventions. Nowadays, there is a lack of cheap, portable, rapid, and easy-to-use tools for detecting prefrailty and sarcopenia in combination. The aim of this study is to validate an iPhone App to detect prefrailty and sarcopenia syndromes in community-dwelling older adults. A diagnostic test accuracy study will include at least 400 participants aged 60 or over without cognitive impairment and physical disability recruited from elderly social centers of Murcia (Spain). Sit-to-stand muscle power measured through a slow-motion video analysis mobile application will be considered as the index test in combination with muscle mass (calf circumference or upper mid-arm circumference). Frailty syndrome (Fried's Phenotype) and sarcopenia (EWGSOP2) will both be considered as reference standards. Sensibility, specificity, positive and negative predictive values and likelihood ratios will be calculated as well as the area under the curve of the receiver operating characteristic. This mobile application will add the benefit for screening large populations in short time periods within a field-based setting, where space and technology are often constrained (NCT05148351).

摘要

衰弱和肌少症合并存在比任何单一条件更能预测死亡率。这些综合征的早期检测决定了与健康相关的不良事件的预后,因为这两种情况都可以通过适当的干预来逆转。如今,缺乏廉价、便携、快速且易于使用的工具来同时检测衰弱和肌少症。本研究的目的是验证一款 iPhone 应用程序,以检测社区居住的老年人的衰弱和肌少症综合征。一项诊断测试准确性研究将包括至少 400 名年龄在 60 岁或以上、无认知障碍和身体残疾的参与者,他们是从西班牙穆尔西亚的老年人社会中心招募的。通过慢动作视频分析移动应用程序测量的坐站肌肉力量将被视为联合肌肉质量(小腿围或上臂中部周长)的指标测试。衰弱综合征(弗里德表型)和肌少症(EWGSOP2)都将被视为参考标准。将计算敏感性、特异性、阳性和阴性预测值以及似然比,以及受试者工作特征曲线下的面积。这款移动应用程序将在基于现场的环境中,在短时间内筛选大量人群,这在空间和技术往往受限的情况下增加了益处(NCT05148351)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d45c/9416054/c68593f8f60d/sensors-22-06010-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d45c/9416054/c68593f8f60d/sensors-22-06010-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d45c/9416054/c68593f8f60d/sensors-22-06010-g001.jpg

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