Dataset Information

Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

ABSTRACT: Gait and balance impairments associated with Parkinson's disease (PD) are often refractory to traditional treatments. Objective, quantitative analysis of gait patterns is crucial in successful management of these symptoms. This project aimed to 1) determine if biomechanical metrics from a mobile device inertial measurement unit were sensitive enough to characterize the effects of anti-parkinsonian medication during the Timed Up and Go (TUG) Test, and 2) develop the Cleveland Clinic Mobility and Balance application (CC-MB) to provide clinicians with objective report following completion of the TUG. The CC-MB captured 3-dimensional acceleration and rotational data from people with PD (pwPD) to characterize center of mass movement while performing the TUG. Trials were segmented into four components: Sit-to-Walk, Gait, Turning, and Stand-to-Sit. Thirty pwPD were tested On and Off (12 h) anti-PD medication. Significant improvements (p < 0.05) between On versus Off conditions included: reduction in MDS-UPDRS III motor scores (10.7%), faster trial times (9.3%), more dynamic walking as evident by increased normalized jerk scores (vertical: 17.3%, medial-lateral: 12.3%), shorter turn durations (10.4%), and faster turn velocities (8%). Measures in Sit-to-Walk and Stand-to-Sit did not show significant changes. Trial time and turn velocity showed excellent test-retest reliability (ICC range: 0.83-0.96) across both medication states. A mobile device platform provided quantitative measures of gait and turning during the TUG that detected significant improvements from anti-parkinsonian medications. This platform is a low-cost, easy-to-use tool that can provide objective reports immediately following the clinical assessments, making it ideal for use in and outside the clinical setting.

SUBMITTER: Miller Koop M

PROVIDER: S-EPMC6095098 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

Miller Koop Mandy M Ozinga Sarah J SJ Rosenfeldt Anson B AB Alberts Jay L JL

IBRO reports 20180621

Gait and balance impairments associated with Parkinson's disease (PD) are often refractory to traditional treatments. Objective, quantitative analysis of gait patterns is crucial in successful management of these symptoms. This project aimed to 1) determine if biomechanical metrics from a mobile device inertial measurement unit were sensitive enough to characterize the effects of anti-parkinsonian medication during the Timed Up and Go (TUG) Test, and 2) develop the Cleveland Clinic Mobility and ...[more]

PMID: 30135951

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Project description:BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

Project description:BACKGROUND:Gait impairments including shuffling gait and hesitation are common in people with Parkinson's disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal characteristics of gait, but less is known of the angular characteristics of the gait, which may provide helpful information pertaining to the functional status and effects of the treatment in PD. OBJECTIVE:This study aimed to quantify the angles of steps during walking, and explore if this novel step angle metric is associated with the severity of PD and the effects of the treatment including the acute levodopa challenge test (ALCT) and deep brain stimulation (DBS). METHODS:A total of 18 participants with PD completed the walking test before and after the ALCT, and 25 participants with PD completed the test with the DBS on and off. The walking test was implemented under two conditions: walking normally at a preferred speed (single task) and walking while performing a cognitive serial subtraction task (dual task). A total of 17 age-matched participants without PD also completed this walking test. The angular velocity was measured using wearable sensors on each ankle, and three gait angular metrics were obtained, that is mean step angle, initial step angle, and last step angle. The conventional gait metrics (ie, step time and step number) were also calculated. RESULTS:The results showed that compared to the control, the following three step angle metrics were significantly smaller in those with PD: mean step angle (F1,48=69.75, P<.001, partial eta-square=0.59), initial step angle (F1,48=15.56, P<.001, partial eta-square=0.25), and last step angle (F1,48=61.99, P<.001, partial eta-square=0.56). Within the PD cohort, both the ALCT and DBS induced greater mean step angles (ACLT: F1,38=5.77, P=.02, partial eta-square=0.13; DBS: F1,52=8.53, P=.005, partial eta-square=0.14) and last step angles (ACLT: F1,38=10, P=.003, partial eta-square=0.21; DBS: F1,52=4.96, P=.003, partial eta-square=0.09), but no significant changes were observed in step time and number after the treatments. Additionally, these step angles were correlated with the Unified Parkinson's Disease Rating Scale, Part III score: mean step angle (single task: r=-0.60, P<.001; dual task: r=-0.52, P<.001), initial step angle (single task: r=-0.35, P=.006; dual task: r=-0.35, P=.01), and last step angle (single task: r=-0.43, P=.001; dual task: r=-0.41, P=.002). CONCLUSIONS:This pilot study demonstrated that the gait angular characteristics, as quantified by the step angles, were sensitive to the disease severity of PD and, more importantly, can capture the effects of treatments on the gait, while the traditional metrics cannot. This indicates that these metrics may serve as novel markers to help the assessment of gait in those with PD as well as the rehabilitation of this vulnerable cohort.

Dataset Information

Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

Publications

Quantifying turning behavior and gait in Parkinson's disease using mobile technology.

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