Musculoskeletal Pain, Motor Control, Risk of Falls and Virtual Reality

Musculoskeletal Pain Clinical Trial

Official title: Study on the Relationship Between Musculoskeletal Pain, Motor Control and the Risk of Falls, and the Effectiveness of Virtual Reality in Managing it in Older People

Status Not yet recruiting

Clinical Trial Summary

The goal of this clinical trial is to learn about musculoskeletal pain, functionality, and risk of falls in community-dwelling adults aged 60 years and older. The main questions it aims to answer are:

• What are the characteristics of a community-dwelling population over 60 years and older in terms of musculoskeletal pain, functionality, and risk of falls?

• Does an intervention with virtual reality lower musculoskeletal pain, raise functionality and prevent falls in community-dwelling adults aged 60 years and older, compared to a control group that does not receive such intervention?

Participants will:

• Answer questionnaires.

• Perform physical laboratory tests through a camera-based motion capture system.

• Execute task-based exercises in a virtual environment through virtual reality.

Researchers will compare community-dwelling adults aged 60 years and older who execute task based exercises in a virtual environment through virtual reality and community-dwelling adults aged 60 years and older without any intervention to see if virtual reality improves musculoskeletal pain, functionality, and lowers fall risk.

Clinical Trial Description

Background

Musculoskeletal pain (MSP) is common among older adults, affecting a considerable proportion of this population and being associated with various conditions such as joint pain, muscle pain, or back pain, among others. More specifically, neck pain and low back pain are highly painful and disabling globally, entailing a significant cost both economically and in terms of healthcare.

Several studies have demonstrated a strong association between MSP and falls in older adults. In particular, it was observed that MSP is a significant predictor of multiple falls and is closely related to a balance deficit.

To properly understand the close relationship between MSP and falls, it is crucial to consider dysfunctions in motor control associated with pain, as they are often the main trigger for falls. Studies in this field highlight the importance of directing new research with a focus on functional tasks, as they represent the epitome of motor control. Therefore, the observation and analysis of functional tasks are crucial for examining the motor control of older adults, being 3D motion capture systems the gold standard for the measurement and analysis of human movement. On the other hand, new technologies such as immersive virtual reality (VR) are gaining recognition and popularity as treatment modalities. This is because they allow users to interact with environments and situations that simulate real life, offering activities tailored to their needs while avoiding the potential risks of the real world. Additionally, it provides an opportunity to repeat exercises, improve performance, and achieve effective learning.

Objectives Several objectives are outlined, as a comprehensive approach involving two different phases will be carried out.

• Phase 1 (Cross-Sectional Study) Main objective: to describe the population aged 60 and older living in the community in terms of musculoskeletal pain, functionality, and fall risk.

• Phase 2 (Experimental Study) Main objective: to assess the efficacy of a virtual reality intervention in a community-dwelling population aged 60 and above in terms of musculoskeletal pain reduction, functionality improvement, and fall risk reduction.

Methods

• Setting, locations and relevant dates: testing is going to be conducted in the Research Center for Information and Communication Technologies (CITIC) (A Coruña, Spain) and Senior University Center of the University of A Coruña (A Coruña, Spain).

• Data collection is planned for the months of May 2024 to May 2025.

• Participants: the recruitment of participants will be carried out through the dissemination of the study via informative posters distributed in the University of A Coruña, including the Senior University; in the network of civic centers in the city of A Coruña; as well as electronically through the researchers' social media networks (Twitter, Facebook, and Instagram). The participant information process, as well as the request for informed consent, will be conducted in the CITIC, after having answered the pre-selection questionnaires.

• Variables: see "outcomes measures".

• Study size: the sample size is estimated using the G-power program, indicating that the minimum number of participants needed to detect medium effect sizes, with a type I error probability of 0.05 and a power of 0.95, with an effect size of 0.7 and considering the analysis through the t-test: the difference between two independent means would be a total of 110 participants.

• Statistical analysis: a descriptive analysis will be performed to characterize the subjects in detail. Qualitative variables will be presented as absolute figures with their respective percentages, and quantitative variables as measures of central tendency (mean and median) and dispersion (standard deviation, minimum, and maximum values). The Kolmogorov-Smirnov test will be used to assess the normality of data distribution.

To explore significant differences between groups, a bivariate statistical analysis will be conducted. The Student's T-test for independent samples will be used for comparing means if the variables under consideration are normal; otherwise, the Mann-Whitney U test will be employed for non-normal variables. The chi-square statistic (X2) with a 95% confidence interval will be applied for the association of qualitative variables. The correlation between quantitative variables will be assessed using the Pearson or Spearman correlation coefficient. A significance level of p < 0.05 will be considered. Data processing and analysis will be carried out using the statistical package SPSS, version 24.0 for Windows (Armonk, NY: IBM Corp.). ;

Related Conditions & MeSH terms

• Accidental Falls
• Musculoskeletal Pain
• Task Performance and Analysis
• Virtual Reality

• NCT number: NCT06362785
• Study type: Interventional
• Source: Universidade da Coruña
• Contact: Beatriz Rodríguez-Romero, PhD
• Phone: 00034 981167000
• Email: beatriz.romero@udc.es
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 2024
• Completion date: June 2026