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基于计算机视觉的运动应用程序对膝骨关节炎患者的影响:随机对照试验。

Effects of a Computer Vision-Based Exercise Application for People With Knee Osteoarthritis: Randomized Controlled Trial.

作者信息

Zhu Dian, Zhao Jianan, Wu Tong, Zhu Beiyao, Wang Mingxuan, Han Ting

机构信息

School of Design, Shanghai Jiao Tong University, Dong Chuan rd, No 800, Shanghai, 200140, China, 86 18901626266.

College of Fashion and Design, Donghua University, Donghua University, Shanghai, China.

出版信息

JMIR Mhealth Uhealth. 2025 May 12;13:e63022. doi: 10.2196/63022.

Abstract

BACKGROUND

Exercise is a primary recommended treatment for knee osteoarthritis (KOA), as it helps alleviate symptoms and improves joint functionality. Personalized exercise programs, tailored to individual patient needs, have demonstrated promising results in maintaining physical fitness and enhancing overall well-being. In recent years, digital health applications have emerged as innovative tools for supervising and facilitating rehabilitation programs. Leveraging computer vision (CV) technology, these applications offer the potential to provide precise feedback and support personalized exercise interventions for patients with KOA in a scalable and accessible manner.

OBJECTIVE

This study aims to evaluate the impact of a CV-graded exercise intervention application over a 6-week period on clinical outcomes in patients with KOA . The outcomes were compared to those achieved through conventional exercise education by videos.

METHODS

A randomized controlled trial was conducted with 60 participants aged 60-80 years, recruited through community administrators between July 2023 and September 2023. Participants were randomly assigned to one of two groups: the graded exercise application group (n=32) and the exercise education brochure group (n=28). The primary outcomes assessed were short-term changes in pain, physical function, and stiffness as measured by the Western Ontario and McMaster Universities Arthritis Index (WOMAC). Secondary outcomes included assessments of participants' affective state, self-efficacy, quality of life, and user experience.

RESULTS

The study recruited 60 participants, including 26 males and 34 females. Analysis revealed statistically significant improvements in physical function (P=.02) and self-efficacy (P=.04) in the graded exercise application group compared to the exercise education brochure group after the intervention. While improvements in pain and stiffness were observed in both groups, these changes were not statistically significant. In addition, participants in the graded exercise application group reported a positive user experience, highlighting the application's usability and engagement features as beneficial to their rehabilitation process.

CONCLUSIONS

The findings suggest that the CV-based graded exercise intervention application effectively improves physical function and self-efficacy among patients with KOA . This digital tool demonstrates the potential to enhance the quality and personalization of exercise rehabilitation compared to traditional methods. Future studies should explore the application's long-term efficacy and replicability in larger community-based populations, with a focus on sustained engagement and adherence to rehabilitation programs.

摘要

背景

运动是膝骨关节炎(KOA)的主要推荐治疗方法,因为它有助于缓解症状并改善关节功能。根据个体患者需求定制的个性化运动计划,在维持身体健康和提高整体幸福感方面已显示出有前景的结果。近年来,数字健康应用程序已成为监督和促进康复计划的创新工具。利用计算机视觉(CV)技术,这些应用程序有可能以可扩展且易于使用的方式为KOA患者提供精确反馈并支持个性化运动干预。

目的

本研究旨在评估CV分级运动干预应用程序在6周内对KOA患者临床结果的影响。将这些结果与通过视频进行的传统运动教育所取得的结果进行比较。

方法

进行了一项随机对照试验,通过社区管理人员在2023年7月至2023年9月期间招募了60名年龄在60 - 80岁之间的参与者。参与者被随机分配到两组之一:分级运动应用程序组(n = 32)和运动教育手册组(n = 28)。评估的主要结果是通过西安大略和麦克马斯特大学关节炎指数(WOMAC)测量的疼痛、身体功能和僵硬程度的短期变化。次要结果包括对参与者情感状态、自我效能、生活质量和用户体验的评估。

结果

该研究招募了60名参与者,包括26名男性和34名女性。分析显示,与运动教育手册组相比,分级运动应用程序组在干预后身体功能(P = 0.02)和自我效能(P = 0.04)方面有统计学上的显著改善。虽然两组在疼痛和僵硬程度上都有改善,但这些变化没有统计学意义。此外,分级运动应用程序组的参与者报告了积极的用户体验,强调该应用程序的可用性和参与功能对他们的康复过程有益。

结论

研究结果表明,基于CV的分级运动干预应用程序能有效改善KOA患者的身体功能和自我效能。与传统方法相比,这种数字工具显示出增强运动康复质量和个性化的潜力。未来的研究应探索该应用程序在更大的社区人群中的长期疗效和可重复性,重点关注持续参与和坚持康复计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7699/12088618/247d0e595308/mhealth-v13-e63022-g001.jpg

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