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

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Dian Zhu, Jianan Zhao, Tong Wu, Beiyao Zhu, Mingxuan Wang, Ting Han
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引用次数: 0

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.

基于计算机视觉的运动应用对膝骨关节炎患者的影响:随机对照试验。
背景:运动是膝关节骨关节炎(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。个性化的锻炼计划,根据患者的个人需求量身定制,在保持身体健康和提高整体健康方面显示出有希望的结果。近年来,数字健康应用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,这些应用提供了以可扩展和可访问的方式为KOA患者提供精确反馈和个性化运动干预的潜力。目的:本研究旨在评估cv分级运动干预应用6周对KOA患者临床结果的影响。将这些结果与通过视频进行传统运动教育的结果进行比较。方法:通过社区管理人员于2023年7月至2023年9月招募60名年龄在60-80岁之间的参与者进行随机对照试验。参与者被随机分为两组:分级运动应用组(n=32)和运动教育宣传册组(n=28)。评估的主要结果是通过西安大略省和麦克马斯特大学关节炎指数(WOMAC)测量疼痛、身体功能和僵硬的短期变化。次要结果包括评估参与者的情感状态、自我效能、生活质量和用户体验。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,干预后,分级运动应用组的身体功能(P= 0.02)和自我效能感(P= 0.04)比运动教育宣传册组有统计学意义的改善。虽然两组患者的疼痛和僵硬程度均有改善,但这些变化在统计学上并不显著。此外,分级锻炼应用程序组的参与者报告了积极的用户体验,强调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:基于cv的分级运动干预应用能有效改善KOA患者的身体功能和自我效能感。与传统方法相比,这个数字工具展示了提高运动康复质量和个性化的潜力。未来的研究应该探索应用程序在更大的社区人群中的长期功效和可复制性,重点是持续参与和坚持康复计划。
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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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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