Predicting Real-World Physical Activity in Multiple Sclerosis: An Integrated Approach Using Clinical, Sensor-Based,...

Area of Science:

• Neuroscience
• Rehabilitation Science
• Biomedical Engineering

Background:

• Multiple sclerosis (MS) is a chronic neurodegenerative condition impacting mobility and quality of life.
• Traditional assessments may not fully capture real-world mobility complexities in MS.
• Objective and subjective measures are needed for comprehensive understanding.

Purpose of the Study:

• To evaluate the predictive power of combining sensor-derived clinical measures and participant-reported outcomes for future physical activity in MS.
• To identify key predictors of physical activity levels in individuals with MS.

Main Methods:

• Forty-six MS participants completed surveys (fatigue, fall concerns, perceived walking ability - MSWS-12) and underwent sensor-based gait/balance assessments.
• Participants wore Fitbit devices for three months to track step counts and overall activity.
• Forward stepwise regression analyzed the combined data to predict future physical activity.

Main Results:

• A combined model integrating participant-reported outcomes and sensor data explained the most variance in future physical activity.
• The MSWS-12 (perceived walking ability) and backward walking velocity were identified as significant predictors.
• This highlights the synergy between subjective experiences and objective biomechanical data.

Conclusions:

• Integrating subjective patient-reported outcomes with objective sensor-derived measures provides a more holistic understanding of physical activity in MS.
• This combined approach is crucial for developing personalized interventions to enhance mobility and quality of life.
• Future research should leverage multimodal data for improved MS management.