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用于优化术后治疗与康复的数字健康技术:基于设备的吊带姿势测量与依从性监测

Digital Health Technologies for Optimising Treatment and Rehabilitation Following Surgery: Device-Based Measurement of Sling Posture and Adherence.

作者信息

Langford Joss, Barakat Ahmed, Daghash Engy, Singh Harvinder, Rowlands Alex V

机构信息

ActivInsights Ltd., 6 Nene Road, Bicton Industrial Park, Kimbolton, Huntingdon PE28 0LF, UK.

Faculty of Health and Life Sciences, University of Exeter, Stocker Rd, Exeter EX4 4PY, UK.

出版信息

Sensors (Basel). 2024 Dec 31;25(1):166. doi: 10.3390/s25010166.

Abstract

BACKGROUND

Following shoulder surgery, controlled and protected mobilisation for an appropriate duration is crucial for appropriate recovery. However, methods for objective assessment of sling wear and use in everyday living are currently lacking. In this pilot study, we aim to determine if a sling-embedded triaxial accelerometer and/or wrist-worn sensor can be used to quantify arm posture during sling wear and adherence to sling wear.

METHODS

Four participants were asked to wear a GENEActiv triaxial accelerometer on their non-dominant wrist for four hours in an office environment, and, for two of those hours, they also wore a sling in which an additional GENEActiv accelerometer was secured. During sling wear, they were asked to move their arm in the sling through a series of pre-specified arm postures.

RESULTS

We found that upper arm angle and posture type during sling wear can be predicted from a sling sensor alone (R = 0.79, < 0.001 and Cohen's kappa = 0.886, respectively). The addition of a wrist-worn sensor did not improve performance. The optimisation of an existing non-wear algorithm accurately detected adherence (99.3%).

CONCLUSIONS

the remote monitoring of sling adherence and the quantification of immobilisation is practical and effective with digital health technology.

摘要

背景

肩部手术后,在适当的时间段内进行有控制的、受保护的活动对于实现适当的恢复至关重要。然而,目前缺乏在日常生活中客观评估吊带佩戴情况和使用情况的方法。在这项初步研究中,我们旨在确定嵌入吊带的三轴加速度计和/或腕部佩戴的传感器是否可用于量化吊带佩戴期间的手臂姿势以及对吊带佩戴的依从性。

方法

四名参与者被要求在办公环境中在其非优势手腕上佩戴一个GENEActiv三轴加速度计四个小时,并且在这四个小时中的两个小时里,他们还要佩戴一个固定有另一个GENEActiv加速度计的吊带。在佩戴吊带期间,他们被要求在吊带内将手臂移动到一系列预先指定的手臂姿势。

结果

我们发现仅通过吊带传感器就能预测吊带佩戴期间的上臂角度和姿势类型(R分别为0.79,P<0.001和Cohen's kappa为0.886)。添加腕部佩戴的传感器并没有提高性能。对现有的非佩戴算法进行优化可准确检测依从性(99.3%)。

结论

利用数字健康技术对吊带依从性进行远程监测和对固定情况进行量化是切实可行且有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29cd/11723119/93f168924048/sensors-25-00166-g001.jpg

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