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将可穿戴技术融入髋关节和膝关节置换术后的康复监测中。

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement.

机构信息

moveUp, 1000 Brussels, Belgium.

Department of PXL-Healthcare, PXL University of Applied Sciences and Arts, 3500 Hasselt, Belgium.

出版信息

Sensors (Basel). 2024 Feb 10;24(4):1163. doi: 10.3390/s24041163.

Abstract

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients' dynamic activity profiles.

摘要

骨关节炎(OA)给老龄化人口带来了日益严峻的挑战,尤其是髋关节和膝关节,极大地导致了残疾和社会成本。本研究探索了可穿戴技术的整合,以解决传统康复评估在捕捉真实世界体验和动态变化方面的局限性。具体来说,它侧重于在康复过程中使用自动化无监督评估来持续监测髋关节和膝关节 OA 患者的身体活动。我们分析了 1144 名手术后使用移动健康应用程序的患者的数据;活动数据是使用 Garmin Vivofit 4 收集的。每天计算和分析了几个参数,如每天的总步数、6 分钟连续最大步频(P6MC)和 1 分钟最大步频(P1M)。结果表明,基于步频的测量可以有效地、更早地区分髋关节和膝关节疾病患者以及康复过程中的患者。基于恢复状态和手术类型的比较揭示了独特的轨迹,强调了 P6MC 和 P1M 在检测变化方面比每天的总步数更早的有效性。此外,基于步频的测量比每天的总步数(80%)具有更低的日内变异性(40%)。包括 P1M 和 P6MC 在内的自动化评估为患者的动态活动特征提供了细致的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f1d/10892564/ad053c4db9fa/sensors-24-01163-g001.jpg

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