Fall Risk Assessment in Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated disorder of the central nervous system often leading to gait and balance impairments that increase fall risk. Clinical assessment of fall risk in MS requires quantification of dynamic balance, postural control and environmental factors that precipitate falls. Objective measures such as posturography, instrumented gait analysis and sensor-based monitoring complement traditional clinical scales to reveal subtle deficits. Advances in technology have enabled remote collection of real-world mobility data via wearable sensors, while emerging digital tools facilitate closed-loop fall reporting and personalised intervention. A comprehensive approach integrates physiological, cognitive and contextual factors to stratify individuals according to risk, guide tailored rehabilitation and reduce fall incidence. The global burden of falls in MS highlights the need for sensitive, scalable and clinically actionable assessment strategies.

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Fall Risk Assessment in Multiple Sclerosis publication trend

The graph below shows the total number of articles in fall risk assessment in multiple sclerosis across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic balance: The ability to maintain postural stability and orientation during movement or in changing support conditions.

Centre of pressure (COP): The point location of the vertical ground reaction force vector, used to quantify postural sway and balance control.

Posturography: A method for measuring balance and postural control, often using force platforms to record COP excursions under various conditions.

Inertial measurement unit (IMU): A wearable sensor that combines accelerometers and gyroscopes to capture motion data such as acceleration and angular velocity in real-world environments.

References

1. Testing Dynamic Balance in People with Multiple Sclerosis: A Correlational Study between Standard Posturography and Robotic-Assistive Device. Sensors (2024).
2. A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.. JMIR Human Factors (2024).
3. Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis. PLOS Digital Health (2022).