In the realm of human movement, gait analysis stands as a pivotal tool for understanding the way that we move through our environment. Quantitative gait analysis can provide insights to disease, injuries and movement disorders by looking at the magnitudes, asymmetries, and variability of the outcome measures.  While reviewing gait analysis data from a single time point can provide important information about a patient’s health status, tracking gait function over time is a vital tool for additional insight into disease progression and treatment efficacy. For example, the Baltimore Longitudinal Study of Aging is looking to gain insights into typical aging by studying functional gait declines. Longitudinal gait analysis is also used in clinical trials to track results of treatment and is commonly used to track changes in motor function due to disease progression. Recent work out of the University of Arkansas for Medical Sciences has highlighted the importance of longitudinal temporal spatial gait analysis as a part of clinical care for patients with Parkinson’s disease.  

Freezing of gait (FOG) is a major disabling symptom seen in patients with Parkinson’s Disease (PD). While we know that FOG leads to an increase in falls and a decrease in overall quality of life, the overall understanding of what causes FOG and how we can predict it is lacking. Much of what we know about FOG comes from studying phenotypic features, comparing non-freezers with freezers. Studies comparing freezers with non-freezers have identified a wide range of motor and non-motor predictors of FOG, however there is no clear consensus. Work by Virmani et al. set out to gain more insight into FOG by looking at changes in temporal spatial gait over time. They reasoned that because FOG is ultimately a gait phenotype, it is reasonable to suspect that changes in gait over time could help predict when a patient would move from a non-FOG phenotype to a FOG phenotype.

Patients between 50 and 90 years old with a diagnosis of PD had their gait analyzed every 6 months for a minimum of 3 timepoints. An FOG questionnaire was given at each visit to identify subjects as part of a non-Freezers (noFOG) or Freezers (FOG). If a subject started in the noFOG group and became part of the FOG group at a later visit, they were categorized the conversion group (FOGConv).

To test gait, subjects walked over an instrumented Zeno Walkway System at steady state. All freezing episodes were excluded. Changes in mean and variability for stride length, stride width, stride velocity, stride time, swing phase %, double support %, integrated pression, and foot-strike length were examined.

There were 3 major findings from this study:

  1. Temporal spatial gait parameters declined faster in FOGConv and FOG groups when compared to the noFOG group.
  2. You can differentiate between the noFOG group and both of the FOG and FOGConv groups by using the rates of changes of the gait parameters.
  3. Simple rates of conversions were better able to classify FOGConv than demographics, motor, or non-motor disease features, levodopa equivalent dosing, or initial temporal spatial gait values.

These findings led the authors to suggest, “… that incorporation of objective gait monitoring of people with PD into routine clinical care could help guide clinicians when counseling individuals on their disease progression. Knowledge of more rapidly progressive disease may also allow clinicians to target more aggressive management plans to those individuals.”

Read this complete publication here.

Virmani T, Landes RD, Pillai L, Glover A, Larson-Prior L, Prior F, Factor SA. Gait Declines Differentially in, and Improves Prediction of, People with Parkinson's Disease Converting to a Freezing of Gait Phenotype. J Parkinsons Dis. 2023;13(6):963-975. doi: 10.3233/JPD-230020. PMID: 37522218.