Step-Based Metrics and Exercise Science in MS Care

Update: 2025-08-19

Description

In this episode of MD Newsline, we welcome Peixuan Zheng, an exercise scientist at the University of Illinois Chicago, who shares her research on using step-based physical activity metrics to assess and enhance the daily function of individuals with multiple sclerosis (MS). Peixuan discusses how wearable technology and new step-counting methods—like peak cadence—can provide more accurate insights into patients' real-world mobility compared to traditional clinical assessments.

She also dives into her intervention programs combining aerobic and resistance training to support cognitive and physical function in older adults with MS, highlighting improvements in learning, memory, and ambulation. The conversation explores the promise of personalized, at-home exercise prescriptions, the role of motivation in long-term adherence, and how machine learning and advanced wearable sensors could further revolutionize MS care.

Episode Highlights

Real-World Walking Metrics for MS Patients
Peixuan introduces the use of wearable sensors to measure "step-based metrics" like daily steps and peak cadence, capturing real-life walking performance more accurately than traditional lab tests.

Reliability of Step-Based Metrics
Validated over a six-month period, step-based data showed consistency across time, making it a reliable alternative for tracking changes in mobility outside clinical settings.

Home-Based Exercise Interventions
Zheng shares results from a 16-week home-based program combining aerobic and resistance exercises aimed at improving cognition and mobility in older adults with MS.

Impact on Cognitive Function
The intervention led to improved cognitive processing speed and learning/memory outcomes, measured by the BICAMS battery.

Role of Motivation and Personal Goals
Understanding patient motivations—like walking better or engaging in family activities—helps tailor exercise plans, boosting adherence and long-term outcomes.

Machine Learning and Future Applications
Zheng sees a future where AI and wearable technology help generate personalized exercise prescriptions based on real-time physiological and movement data.

Key Takeaway

Peixuan Zheng's research shows that real-world step-based metrics and personalized, home-based exercise programs offer promising ways to monitor and improve mobility and cognition in people with MS. Her work supports a future of MS care grounded in precision, motivation, and multidisciplinary collaboration.