Dynamics of turning sharpness influences freezing of gait in Parkinson's disease
Introduction
Freezing of gait (FOG) is one of the most disabling symptoms in Parkinson's disease (PD) and can be described as an inability to generate effective stepping [1]. Although FOG occurs in various situations, arguably the most common trigger of freezing is turning [2], [3], [4], [5], [6]. Turning not only threatens stability more than any other freezing trigger, but also requires precise control of each limb [7]. However, despite turning and freezing being so commonly intertwined, no studies have explored the relationship between the sharpness of different turning angles and freezing. Evaluating FOG under these circumstances would not only allow us to better understand conditions under which freezing is most likely to occur, but also gait analysis can provide insight into the underlying mechanisms associated with FOG by unveiling spatial and temporal deficits linked with the phenomenon.
In a recent pilot study, we examined PD turning (non-freezers) at various angles and identified that patients displayed greater variability in gait timing at sharper turns (180°) compared to less sharp turns (90°). Previous work suggests that this timing deficit is caused by a faulty control of bilateral coordination [8]. In individuals that experience FOG, we might expect that step time variability (STV) to be even higher when making sharper turns, as these turns are more destabilizing in nature and therefore impose greater demand on lower limb coordination [9]. Furthermore, timing deficits as reflected in STV have been previously shown to be linked with FOG episodes [10]. Given the relationship between STV and FOG, we wanted to evaluate whether freezers might be most vulnerable to FOG events at sharper turns, assuming this is where STV is highest.
Along with spatiotemporal gait changes, turning is often accompanied by stepping strategies that enables continuous locomotion. Research on turning has identified the step out turn and crossover turn as two different strategies used by young adults when changing direction (See Fig. 2) [11], [12]. Of these two strategies, step out turns have been proven to provide greater stability and lower biomechanical cost [13]. Although turning strategies have been well established in young [11], [12], [13] and elderly [14] populations, strategies in PD are less understood. Describing turn type in PD can be valuable in providing a fuller description of how patients cope with turns rather than simply timing the overall performance [15]. Previous research attempting to provide new definitions for PD turning has been met with modest agreement (56%) [15]. Therefore, instead of creating a new turn classification for PD turning that is novel to the literature it may be more suitable to use existing definitions to classify turns in a step-by-step manner. As a result participants may be using one of three turn strategies, step out, crossover, or mixed strategy, in which both step out and crossover strategies are used to carry out the turn.
Thus the current study has three main objectives: to examine spatiotemporal gait parameters during turning in PD-FOG, PD non-FOG and healthy controls, to evaluate the prevalence of freezing episodes at different turn angles, and finally, to classify turning strategies based on pre-existing definitions.
Section snippets
Methods
This study involved a total of 30 participants, 20 PD patients and 10 healthy age and height-matched control participants. All PD participants were recruited from a database at the Sun Life Movement Disorders Research and Rehabilitation Centre at Wilfrid Laurier University (Waterloo, Canada) and divided into 10 freezers (PD-FOG) and 10 non-freezers (PD non-FOG). PD groups were matched for age, height, and motor symptom severity. Additionally PD patients were excluded from the study if they did
Baseline characteristics
A one-way ANOVA was performed between PD-FOG, PD non-FOG and Healthy controls for height and age, while a paired t-test was conducted between PD-FOG and PD non-FOG on motor symptom score (UPDRS section III), revealing no significant differences (Table 1).
Step length
Overall, both PD groups walked with a smaller step length compared to healthy control participants. However, across all groups, a significant main effect of angle was found (F(3,81) = 154.38, p < 0.001) resulting in a significant systematic
Freezing and gait parameters
The present study investigated the influence of dynamic turning on gait aspects of individuals with PD that experience FOG. To further the work by Huxham et al. [22], our results showed that both PD non-FOG and PD-FOG groups use a similar but scaled down step length when turning compared to healthy controls. This finding suggests that although patients are unable to produce a large step length, it appears that spatial adjustments associated with turning (i.e. systematic reduction in step length
Conclusion
The current study reveals that during sharper turns, freezers exhibit poorer timing control of gait and more freezing episodes, further supporting the theory that FOG may be associated by a deficit in gait timing rather than step length production. Additionally, freezers did not increase step width when dynamically turning and instead exhibited a cautious gait by slowing down when turning was necessary. This strategy might be compensating for deficits freezers face in controlling body dynamics
Financial disclosures of the past year
Almeida Quincy: Employment: Professor Wilfrid Laurier University; Grants; National Sciences and Engineering Research Council of Canada (NSERC) and Sun Life Financial; Canadian Foundation for Innovation (CFI).
Acknowledgements
The authors would like to thank Tracy Tan for her assistance in data collection for this project.
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2019, Journal of BiomechanicsCitation Excerpt :Since, in this case, the variability of possible foot placements is relatively low, the amount of necessary data for training and testing can be reduced. In rehabilitation, machine learning techniques can be used to classify turn strategies (Bhatt et al., 2013; Adamson et al., 2019). In this case, the analysis requires to take into account several successive steps increasing the variability within each group.