# Linking Pathogenesis to Fall Risk in Multiple Sclerosis

**Authors:** Jaylan Patel, Marcel P. Fraix, Devendra K. Agrawal

PMC · DOI: 10.26502/aimr.0194 · Archives of internal medicine research · 2025-03-04

## TL;DR

This review examines factors that increase fall risk in people with Multiple Sclerosis and highlights strategies to prevent falls and improve quality of life.

## Contribution

The paper provides a comprehensive overview of fall risk factors and interventions in Multiple Sclerosis, emphasizing the need for personalized strategies.

## Key findings

- Gait abnormalities, cognitive dysfunction, and fatigue are key contributors to fall risk in Multiple Sclerosis.
- Exercise, cognitive-behavioral therapy, and self-management programs can help reduce fall risk and improve mobility.
- Emerging technologies and machine learning may enhance fall prediction and prevention in Multiple Sclerosis.

## Abstract

Multiple Sclerosis is a chronic neurological disorder characterized by progressive disability, with falls being a significant consequence of its physical and cognitive impairments. This review explores the major contributors to fall risk in individuals with multiple sclerosis and explores the broader implications of these factors, such as the fear of falling. The primary factors associated with fall risk include gait abnormalities, cognitive dysfunction, and fatigue. These factors often interact, leading to mobility limitations and diminishing overall quality of life. Interventions to mitigate fall risk in multiple sclerosis have shown varying degrees of success. Exercise and rehabilitation strategies improve physical function and balance, while cognitive-behavioral therapy addresses fatigue and associated symptoms. Self-management programs empower patients to take an active role in symptom management, though their effectiveness varies. Disease-modifying therapies are the primary treatment for slowing disease progression, indirectly reducing fall risk. Emerging technologies show promise in enhancing mobility and safety, while machine learning algorithms offer the potential for predicting fall risk in multiple sclerosis populations. This review underscores the need for a comprehensive approach to fall prevention in multiple sclerosis. Healthcare providers can develop personalized strategies to improve mobility, reduce fall incidence, and enhance the quality of life for individuals with multiple sclerosis. Further research is essential to refine these interventions and optimize long-term outcomes.

## Linked entities

- **Diseases:** Multiple Sclerosis (MONDO:0005301)

## Full-text entities

- **Diseases:** falls (MESH:C537863), cognitive dysfunction (MESH:D003072), neurological disorder (MESH:D009461), Multiple Sclerosis (MESH:D009103), gait abnormalities (MESH:D020233), fatigue (MESH:D005221), mobility limitations (MESH:D051346)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11879276/full.md

## References

109 references — full list in the complete paper: https://tomesphere.com/paper/PMC11879276/full.md

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Source: https://tomesphere.com/paper/PMC11879276