Analysis of gait in Parkinson’s disease reflecting the effect of l-DOPA

Mondal, Banashree; Choudhury, Supriyo; Banerjee, Rebecca; Chatterjee, Koustav; Ghosal, Sayam; Anand, Sidharth Shankar; Kumar, Hrishikesh

doi:10.4103/aomd.aomd_19_18

Cited by 12 publications

(13 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatiotemporal gait parameters are one form of data obtained through gait analysis that have been shown to be useful clinical measures that can detect 'negative' changes in individuals' gait patterns due to pathology (Elbaz et al, 2014;Givon et al, 2009;Lemke et al, 2000;Morris et al, 2001) or aging (Hollman et al, 2011), and 'positive' changes due to rehabilitation (Fung et al, 2006;Patterson et al, 2008) or locomotor training (Abd El-Kafy and El-Basatiny, 2014; Smania et al, 2011;Vitale et al, 2012). They have been implemented to study the gait patterns of children (Alderson et al, 2019), older adults (Vallabhajosula et al, 2019), individuals with Parkinson's disease (Mondal et al, 2019), dementia (Darweesh et al, 2019), multiple sclerosis (Novotna et al, 2019), and post-stroke patients (Cleland et al, 2019) as a few examples. However, it is crucial that they are obtained using objective techniques to ensure adequate accuracy and repeatability (Toro et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko

Laende

Strutzenberger

et al. 2021

Journal of Biomechanics

View full text Add to dashboard Cite

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and pressure sensitive gait mats. For the first study, thirty healthy adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heelstrike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems.Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters during treadmill and overground gait.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko

Laende

Strutzenberger

et al. 2021

Journal of Biomechanics

View full text Add to dashboard Cite

show abstract

“…For example, Curtze et al (2015) showed that the ON state increased speed and stride length but did not influence cadence, step initiation, double support time, and swing time. Differently, Mondal et al (2019) showed a decrease in the double support time and the number of steps, increasing the step and the stride length in the ON state. In that same study, the medication did not affect cadence, one-leg support time, step time, cycle time, swing time, and support base width.…”

Section: Introductionmentioning

confidence: 80%

“…The gait of individuals with PD tends to be slower, characterized by narrow and short steps, flexed torso, little or no arm swing, slow and spasmodic turns (see more in Mancini et al, 2019). However, when explicitly analysing the spatiotemporal parameters of gait in individuals with PD compared with healthy individuals, studies have shown decreased speed (Cheng et al, 2014;Mondal et al, 2019;Morris et al, 1999), increased number of steps (Mondal et al, 2019), step (Cheng et al, 2014;Mondal et al, 2019), and stride length (Mondal et al, 2019) decrease; shorter duration of the swing phase and the one-leg support phase (Mondal et al, 2019); and longer duration of the double support phase (Mondal et al, 2019;Sofuwa et al, 2005). These changes in gait pattern are considered one of the symptoms that most affect the quality of life of individuals with PD and have been considered responsible for about 50% of the falls of patients (Lord et al, 2017), which may result in hospitalization and impaired motor function.…”

Section: Introductionmentioning

confidence: 99%

“…The administration of L-DOPA is one of the leading drug treatments for PD to restore dopaminergic levels. During the ON state of the medication, the self-selected gait speed of individuals with PD increases (Curtze et al, 2015;Mondal et al, 2019) regardless of how dopaminergic signalling acts (motor or motivational). However, when looking at the spatiotemporal parameters of gait individually, the medication induces different and inconsistent effects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The effects of levodopa in the spatiotemporal gait parameters are mediated by self‐selected gait speed in Parkinson's disease

Oliveira

Bazán

Oliveira

et al. 2021

Eur J of Neuroscience

View full text Add to dashboard Cite

In individuals with Parkinson's disease (PD), the medication induces different and inconsistent results in the spatiotemporal parameters of gait, making it difficult to understand its effects on gait. As spatiotemporal gait parameters have been reported to be affected by gait speed, it is essential to consider the gait speed when studying walking biomechanics to interpret the results better when comparing the gait pattern of different conditions. Since the medication alters the self-selected gait speed of individuals with PD, this study analysed whether the change in gait speed can explain the selective effects of L-DOPA on the spatiotemporal parameters of gait in individuals with PD. We analysed the spatiotemporal gait parameters at the self-selected speed of 22 individuals with PD under ON and OFF states of L-DOPA medication. Bayesian mediation analysis evaluated which gait variables were affected by the medication state and checked if those effects were mediated by speed changes induced by medication. The gait speed was significantly higher among ON compared with OFF medication. All the spatiotemporal parameters of the gait were mediated by speed, with proportions of mediation close to 1 (effect entirely explained by speed changes). Our results show that a change in gait speed better explains

show abstract

“…Spatiotemporal gait parameters are one form of data obtained through gait analysis that have been shown to be useful clinical measures that can detect 'negative' changes in individuals' gait patterns due to pathology (Elbaz et al, 2014;Givon et al, 2009;Lemke et al, 2000;Morris et al, 2001) or aging (Hollman et al, 2011), and 'positive' changes due to rehabilitation (Fung et al, 2006;Patterson et al, 2008) or locomotor training (Abd El-Kafy and El-Basatiny, 2014;Smania et al, 2011;Vitale et al, 2012). They have been implemented to study the gait patterns of children (Alderson et al, 2019), older adults (Vallabhajosula et al, 2019), individuals with Parkinson's disease (Mondal et al, 2019), dementia (Darweesh et al, 2019), multiple sclerosis (Novotna et al, 2019), and post-stroke patients (Cleland et al, 2019) as a few examples. However, it is crucial that they are obtained using objective techniques to ensure adequate accuracy and repeatability (Toro et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Kanko¹,

Strutzenberger²,

Brown³

et al. 2020

Preprint

View full text Add to dashboard Cite

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. The aim of this work was to determine whether spatiotemporal gait parameters measured using Theia3D markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture. Thirty healthy adult participants performed treadmill walking at self-selected speeds while 2D video and marker-based motion capture data were collected simultaneously. Kinematic-based gait events were used to measure nine spatiotemporal gait parameters from both systems independently. The parameters were compared using their group means, Bland-Altman methods, Pearson correlation coefficients, paired-samples t-tests, and intraclass correlation coefficients (ICC(A-1) and ICC(C-1)). Group means between systems were indistinguishable across all nine gait parameters, and the Bland-Altman plots showed no systematic biases or clinically meaningful differences between the systems. Pearson coefficients indicated near-perfect correlations (r ≥0.96) between systems for all but two parameters, which had strong correlations (double-limb support time, r=0.87; swing time, r=0.88). T-tests indicated differences in stance, swing, and double-limb support time parameters, but strong correlations were found for all ICCs, the lowest being 0.84 for both double-limb support time and swing time. The measurements made by the Theia3D markerless motion capture system demonstrated concurrent validity with those from the marker-based system, indicating sufficient accuracy for research, clinical, and other uses.

show abstract

Analysis of gait in Parkinson’s disease reflecting the effect of l-DOPA

Cited by 12 publications

References 24 publications

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

The effects of levodopa in the spatiotemporal gait parameters are mediated by self‐selected gait speed in Parkinson's disease

Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Contact Info

Product

Resources

About