The professor of neurology at University Medical Center Schleswig‐Holstein, and member of the department of neurodegenerative diseases at University Hospital Tübingen shared insight into the quantitative gait characteristics measured by wearable devices which can play an important role in the identification of prodromal Parkinson disease and its progression.
Walter Maetzler, MD
In a recent study of an attempt to establish gait impairments and trajectories within the prodromal phase of Parkinson disease and identify which gait characteristics within 5 domains—pace, variability, rhythm, asymmetry, and postural control—are potentially early diagnostic markers of Parkinson, Walter Maetzler, MD, and colleagues found a number of quantifiable changes which can predict conversion to Parkinson.
The professor of neurology at University Medical Center Schleswig‐Holstein, and member of the department of neurodegenerative diseases at University Hospital Tübingen, and colleagues found that at usual-speed walking, the domains of gait variability (step time, P
= .028), pace (swing time, P
= .033), asymmetry (step time, P
=.003; swing time, P
= .002; stance time, P
= .004), and postural control (step length, P
<.001) were significantly predictive of conversion to Parkinson.
To find out more about what these findings mean for clinicians, and how they might impact practice, NeurologyLive®
spoke with Maetzler about the findings he and his colleagues observed.
NeurologyLive®: What do these findings mean for the diagnosis and treatment of Parkinson disease?
Walter Maetzler, MD:
These results are a further building block for our understanding of the early "subclinical" phase of Parkinson disease, that most probably occurs over many years. It suggests that, besides many non-motor features such as smell and some aspects of cognition and sleep behavior, gait also becomes affected about 4 years before a diagnosis is currently possible. Moreover, we found specific parameters of gait deteriorating during these 4 years, which gives us hope that gait has potential to serve as a progression marker during this early disease phase, or even as a marker for treatment response—as soon as treatment is available for this disease stage.
Was anything particularly surprising or unexpected about these data?
It was surprising that we found gait alterations in future Parkinson converters only in 1 paradigm: walking at a convenient speed. Dual-tasking paradigms and fast walking did not show differences between the groups. This gives us hope that we can detect the observed differences not only under supervised conditions in the lab, as reported in this paper, but also in unsupervised conditions, such as in the home environment, as in this environment we walk with convenient walking speed over most of the time.
Another interesting aspect was that the gait features that indicate that a person "deviates" from normal aging to a prodromal Parkinson state were from the variability and asymmetry domains of gait: step and swing time variability; step, swing, and stance time and step length asymmetry—those can be considered trait markers. Also, the parameters that best predicted progression within this prodromal phase—they can be considered state and progression markers—were out of the pace domain: gait velocity and step length. Thus, trait and state markers differed.
Could this impact clinical practice, or does this method still require further study?
We provide, to the best of my knowledge, the first longitudinal evidence for gait changes in prodromal Parkinson disease. Together with already existing evidence coming from previous cross-sectional studies, I feel that there is little doubt that gait changes occur in prodromal Parkinson disease. Clinicians that see patients that are potentially in such a prodromal phase should thus do a thorough gait examination with evaluation of the above-mentioned gait parameters, ideally repeatedly.
Transcript edited for clarity
Del Din S, Elshehabi M, Galna B, et al. Gait analysis with wearables predicts conversion to Parkinson disease. Ann Neurol. Published online July 11, 2019. doi: 10.1002/ana.25548.