The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

Abstract

Freezing of gait (FOG) is a devastating motor symptom of Parkinson's disease that leads to falls, reduced mobility, and decreased quality of life. Reliably eliciting FOG has been difficult in the clinical setting, which has limited discovery of pathophysiology and/or documentation of the efficacy of treatments, such as different frequencies of subthalamic deep brain stimulation (STN DBS). In this study we validated an instrumented gait task, the turning and barrier course (TBC), with the international standard FOG questionnaire question 3 (FOG-Q3, r = 0.74, p < 0.001). The TBC is easily assembled and mimics real-life environments that elicit FOG. People with Parkinson's disease who experience FOG (freezers) spent more time freezing during the TBC compared to during forward walking (p = 0.007). Freezers also exhibited greater arrhythmicity during non-freezing gait when performing the TBC compared to forward walking (p = 0.006); this difference in gait arrhythmicity between tasks was not detected in non-freezers or controls. Freezers' non-freezing gait was more arrhythmic than that of non-freezers or controls during all walking tasks (p < 0.05). A logistic regression model determined that a combination of gait arrhythmicity, stride time, shank angular range, and asymmetry had the greatest probability of classifying a step as FOG (area under receiver operating characteristic curve = 0.754). Freezers' percent time freezing and non-freezing gait arrhythmicity decreased, and their shank angular velocity increased in the TBC during both 60 Hz and 140 Hz STN DBS (p < 0.05) to non-freezer values. The TBC is a standardized tool for eliciting FOG and demonstrating the efficacy of 60 Hz and 140 Hz STN DBS for gait impairment and FOG. The TBC revealed gait parameters that differentiated freezers from non-freezers and best predicted FOG; these may serve as relevant control variables for closed loop neurostimulation for FOG in Parkinson's disease.

Trial registration: ClinicalTrials.gov NCT02304848.

Conflict of interest statement

Dr. Helen Bronte-Stewart is a member of a clinical advisory board for Medtronic Inc. and Scott Delp is a scientific advisor and board member of Cala Health, Circuit Therapeutics, and Zebra Medical Technologies, and receives compensation for this service. Dr.Helen Bronte-Stewart and Johanna O’Day have submitted a provisional patent as co-inventors of systems and methods for deep brain stimulation kinematic controllers (patent #S19-551). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1. Turning and Barrier Course (TBC) dimensions and specifications.

A: the individual barrier and course dimensions. Tall barriers limited vision around turns and narrow passageways to simulate a real-world environment. B: front view with patient walking in the TBC. C: aerial diagram of the TBC with barriers (dark grey bars) and wall (light grey bar). Subjects walked in two ellipses and then two figures of eight around the barriers; this task was repeated starting on both the left and right side, for a total of four ellipses and four figures of eight.

Fig 2. Gait parameters extracted from inertial measurement units (IMU).

Top: schematic of one gait cycle with IMU on the shank used to define gait parameters including stride time, forward swing time, swing angular range and peak angular velocities (peak AV). Bottom: gait parameters extracted from shank sagittal angular velocity data for the left (blue) and right (red) legs during periods of non-freezing walking, and freezing of gait (orange).

Fig 3. Relationship between gait parameters and freezing of gait questionnaire question 3 (FOG-Q3) during walking tasks.

Correlation with FOG-Q3 between A–C: gait arrhythmicity, and D–F: shank angular velocity, during FW (A, D), TBC ellipses (B, E), and TBC figures of eight (C, F). Regression lines (black line) and confidence intervals of the correlation coefficient at 95% (shaded grey), and subjects (black dots) shown.

Fig 4. Group gait parameters during walking tasks.

A: Gait arrhythmicity, B: average peak shank angular velocity, C: asymmetry, and D: stride time in healthy controls, non-freezers and freezers, during non-freezing FW, TBC ellipses and TBC figures of eight. Error bars represent standard deviation. * p < 0.05; ** p < 0.01; *** p < 0.001; ^ p < 0.05 TBC ellipses and TBC figures of eight compared to FW in freezers; ~ p < 0.05 between TBC ellipses and TBC figures of eight in non-freezers and in freezers.

Fig 5. Logistic regression model performance for different gait parameters.

A: overall model performance: AUC values for different model iterations using leave-one-out cross validation on the freezer group. First row: individual gait parameters; second row: all gait parameters; third row: sparse parameter set chosen from regularization. Peak Shank AV = Peak Shank Angular Velocity. Some metrics are calculated over a window of steps in time: “t-3:t” represents a window from “t-3” or 3 steps earlier, to and including the current step “t”. B: representative shank angular velocity traces from right and left legs; model-identified freezing events (pink shading) and neurologist-identified freezing behavior (orange shading).

Fig 6. Relationship between freezers’ non-freezing gait arrhythmicity and freezing severity.

Relationship between freezers’ non-freezing gait arrhythmicity and percent time freezing A: during FW, B: during the TBC ellipses, and C: during the TBC figures of eight. Regression lines (black line) and confidence intervals of the correlation coefficient at 95% (shaded grey), and subjects (colored dots) shown.

Fig 7. Gait arrhythmicity and average peak shank angular velocity OFF and during 60 Hz and 140 Hz deep brain stimulation (DBS).

A: Gait arrhythmicity and B: average peak shank angular velocity during stimulation conditions. Healthy control averages shown (green line) with standard deviations (shaded green). Error bars represent standard deviation. * denotes p < 0.05, ** denotes p < 0.01.