Knee osteoarthritis (OA) is a leading cause of disability in older adults, with health and economic impacts. Despite pharmacological advances, exercise continues to be a fundamental pillar in the management of OA, with lower limb strength training showing significant benefits. Virtual reality (VR)-based interventions have emerged as innovative tools, providing immersive environments to facilitate functional movement exercises. VR offers pain relief, improved functionality and reduced fall risks, although its efficacy in OA management requires further exploration. The main aim of the study is to assess whether a VR-based intervention provides superior improvements in pain, stiffness, physical function and movement biomechanics compared with conventional therapeutic exercise in adults aged 60 years and older with knee OA.

This is a protocol for a randomised controlled trial comparing the effects of immersive VR interventions with conventional therapeutic exercises in individuals aged 60 years and older with knee OA. Participants are allocated 1:1 to experimental (VR) and control groups. The VR intervention involves 18 supervised sessions over 8 weeks, using Meta Quest 3 goggles to perform functional movements in virtual environments. The control group follows standard therapeutic exercise protocols per Osteoarthritis Research Society International guidelines. Outcomes include OA-related symptoms, kinematic performance, pain intensity, kinesiophobia and fall risk. Secondary measures assess cybersickness, depressive symptoms, medication use and comorbidities. Assessments occur at baseline, ninth week, sixth and ^12th months. Data analysis employs intention-to-treat principles, leveraging descriptive statistics, t-tests and multiple imputations for missing data.

This study was approved by the A Coruña-Ferrol Research Ethics Committee (reference: 2023/557), under the Galician Health Service. All participants will be required to provide written informed consent prior to their inclusion in the study. Participant data will be pseudonymised and securely stored. Additionally, anonymised datasets will be deposited in open-access repositories (Zenodo).

NCT06362785.