Evidence map›Paper›PMID 40354624›Full record

Trial reportJMIR mHealth and uHealth2025

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

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

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dian ZhuSchool of Design, Shanghai Jiao Tong University, Dong Chuan rd, No 800, Shanghai, 200140, China, 86 18901626266.ORCID 0000-0002-1710-6659
Jianan ZhaoCollege of Fashion and Design, Donghua University, Donghua University, Shanghai, China.ORCID 0000-0003-4105-1683
Tong WuSchool of Design, Shanghai Jiao Tong University, Dong Chuan rd, No 800, Shanghai, 200140, China, 86 18901626266.ORCID 0009-0003-0119-1160
Beiyao ZhuDepartment of Plastic and Reconstructive Surgery, Shanghai Jiao Tong University Ninth People's Hospital, Shanghai, China.ORCID 0000-0002-6443-5586
Mingxuan WangSchool of Design, Shanghai Jiao Tong University, Dong Chuan rd, No 800, Shanghai, 200140, China, 86 18901626266.ORCID 0009-0008-3376-6762
Ting HanSchool of Design, Shanghai Jiao Tong University, Dong Chuan rd, No 800, Shanghai, 200140, China, 86 18901626266.ORCID 0000-0001-7446-6733

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Exercise TherapyOsteoarthritis, KneeAgedAged, 80 and overFemaleHumansMaleMiddle AgedQuality of Lifeappapplicationapplicationsbehaviorbehavior change theorydigital healthexerciseexercise rehabilitationkneeknee osteoarthritisolder adultsosteoarthritisphysical functionrandomized controlled trialrehabilitationself-efficacyvision

Identifiers

PMID40354624
PMCPMC12088618

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.