Backward walking sensitively detects fallers in persons with multiple sclerosis.

Area of Science:

• Neurology
• Biomechanics
• Gerontology

Background:

• Multiple sclerosis (MS) is associated with mobility deficits and injurious falls.
• Current clinical fall detection methods lack sensitivity and predictive value.
• Backward walking has shown promise in differentiating fallers in other populations.

Purpose of the Study:

• To identify the strongest unique predictor of falls in individuals with MS.
• To compare the predictive value of forward and backward walking measures for fall risk.
• To evaluate the potential of backward walking velocity as a fall detection tool.

Main Methods:

• Collected spatiotemporal gait parameters (forward and backward walking) and fall history.
• Utilized discriminant function analysis to identify key differentiating variables.
• Monitored falls prospectively for six months using a fall diary.

Main Results:

• Backward walking velocity was the strongest unique predictor of falls in individuals with MS.
• A model combining backward walking velocity and symptom duration accurately classified 76.3% of fallers and non-fallers.
• Forward walking measures did not improve, and even reduced, classification accuracy.

Conclusions:

• Backward walking velocity is a highly sensitive and specific indicator for differentiating fallers from non-fallers in MS.
• Backward walking velocity shows potential as a clinically feasible and efficient fall detection tool.
• This approach may improve fall prevention strategies for individuals with MS.