Objective measures of unobstructed walking and obstacle avoidance in Parkinson’s disease subtypes

Orcioli‐Silva, Diego; Vitório, Rodrigo; Lirani‐Silva, Ellen; Santos, Paulo Cezar Rocha dos; Gobbi, Lilian Teresa Bucken

doi:10.1016/j.gaitpost.2018.03.046

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…Moreover, the stance phase is related to the stability of walking. [30][31] In the current study, we observed changes of gait parameters of healthy individuals under ETAAC conditions during walking. The results revealed that, in the walking cycle, the percentage of stance phase in the ETAAC condition (59.151 ± 1.903)% was significantly lower than in the HTAAC condition (59.825 ± 1.495)% (P < 0.05), while the percentage of single support phase in the ETAAC condition (40.726 ± 1.808)% was significantly higher than in the HTAAC condition (39.986 ± 1.250)% (P < 0.05).…”

Section: Discussionmentioning

confidence: 69%

Enhanced Active Contraction of the Transversus Abdominis During Walking

Deng

Zhong

et al. 2021

Rev Bras Med Esporte

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Introduction: We applied three-dimensional gait analysis to assess the effects of enhanced active contraction of the transversus abdominis (EACTA) during walking. We sought to evaluate the effect of EACTA during walking in order to improve walking quality. Methods: Thirty college students were recruited and trained to perform EACTA during walking. We examined gait parameters under different conditions, including EACTA and habitual ACTA (natural walking with mild contraction of the feedforward mechanism of ACTA, HACTA) during walking using three-dimensional gait analysis. We compared differences in gait parameters under the two walking conditions using SPSS 16.0 statistical software. Results: The following gait parameters were significantly lower under EACTA conditions than under HACTA conditions (P < 0.05): stance phase, 59.151% ± 1.903% vs. 59.825% ± 1.495%; stride time, 1.104 s ± 0.080 s vs. 1.134 s ± 0.073 s:; stance time, 0.656 s ± 0.057 s vs. 0.678 s ± 0.053 s; and swing time, 0.447 s ± 0.028 s vs. 0.454 s ± 0.031 s, respectively. Gait parameters single support phase and mean velocity were significantly higher for EACTA than for HACTA conditions (both P < 0.05). Conclusions: Overall, the results revealed that EACTA during walking can improve gait. This method is simple, and EACTA training during walking to improve gait quality in daily life could provide a positive basis for people to strengthen the transverse abdominal muscle. Level of evidence III; Retrospective comparative study .

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Section: Discussionmentioning

confidence: 69%

Enhanced Active Contraction of the Transversus Abdominis During Walking

Deng

Zhong

et al. 2021

Rev Bras Med Esporte

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“…Gait characteristics of crossing step for both more affected (MAS) and less affected (LAS) limbs of PD group and both dominant limb (DL) and non-dominant limb (NDL) of control group (CG). www.nature.com/scientificreports www.nature.com/scientificreports/ parameters compared to a control group during single obstacle 23,24 and double obstacle conditions 20 . However, these studies did not analyze the differences between lower limb preferences and obstacle crossing role -leading and trailing limbs.…”

Section: Discussionmentioning

confidence: 99%

Double obstacles increase gait asymmetry during obstacle crossing in people with Parkinson’s disease and healthy older adults: A pilot study

Orcioli‐Silva

Barbieri

Santos

et al. 2020

Sci Rep

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Gait asymmetry during unobstructed walking in people with parkinson's disease (pD) has been well documented. However, under complex situations, such as environments with double obstacles, gait asymmetry remains poorly understood in PD. Therefore, the aim of this study was to analyze inter-limb asymmetry while crossing a single obstacle and double obstacles (with different distances between them) in people with PD and healthy older adults. Nineteen people with PD and 19 healthy older people performed three conditions: (i) walking with one obstacle (Single); (ii) walking with two obstacles with a 50 cm distance between them (Double-50); (iii) walking with two obstacles with a 108 cm distance between them (Double-108). The participants performed the obstacle crossing with both lower limbs. Asymmetry Index was calculated. We found that people with PD presented higher leading and trailing toe clearance asymmetry than healthy older people. In addition, participants increased asymmetry in the Double-50 compared to Single condition. It can be concluded that people with PD show higher asymmetry during obstacle crossing compared to healthy older people, independently of the number of obstacles. In addition, a challenging environment induces asymmetry during obstacle crossing in both people with pD and healthy older people. Tripping over obstacles has been identified as one of the prominent causes of falls in Parkinson's disease (PD) 1,2. There are several PD-related factors associated with trips and falls during walking, including unsymmetrical gait parameters 3. Gait asymmetry is characterized by a different pattern between the left and right limb extremities that reflects in unsymmetrical behavioral gait outcomes 4. Under healthy conditions, both limbs behave similarly, indicating a high level of symmetry during gait 3. However, pathologies such as PD result in marked spatial-temporal asymmetries during gait 3-6 , which is related to lesions in the basal ganglia region, the main area affected in PD 7,8. In PD, the depletion of dopamine in the basal ganglia is asymmetric between cerebral hemispheres, resulting in asymmetrical dysfunction of multiple basal ganglia circuitry 9. Because the basal ganglia provide phasic cues to several subcortical and cortical areas, e.g. the corpus callosum and supplementary motor area (SMA), uncoordinated bilateral control of gait can occur with PD 10-13. Substantial data have indicated that during level walking, people with PD present higher asymmetry in step length 13,14 swing phase 3-6 , and step duration 13,15 compared to healthy older people. Notwithstanding, behavioral outcomes underlying gait asymmetries under complex environments remain poorly understood in PD. To the best of our knowledge, to date, the effects on asymmetry of increasingly challenging models have been tested in postural control tasks 16 and during a single obstacle avoidance 17. Barbieri and colleagues 17 showed that people

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“…Approximately 25% of people with PD (PwP) have unstable posture at diagnosis ( Kohat et al, 2021 ), and the incidence increases with time from diagnosis ( Kohat et al, 2021 ) often with the development of freezing of gait (FOG) ( Ge et al, 2020 ). This is associated with a shorter stride length ( Nanhoe-Mahabier et al, 2011 ; Orcioli-Silva et al, 2018 ). These changes have been linked to cognitive dysfunction and anxiety ( Giladi and Hausdorff, 2006 ; Weiss et al, 2015 ; Kueper et al, 2017 ; Yao et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring

Shokouhi¹,

Khodakarami²,

Fernando³

et al. 2022

Front. Aging Neurosci.

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ObjectivesThere are concerns regarding the accuracy of step count in Parkinson’s disease (PD) when wearable sensors are used. In this study, it was predicted that providing the normal rhythmicity of walking was maintained, the autocorrelation function used to measure step count would provide relatively low errors in step count.Materials and MethodsA total of 21 normal walkers (10 without PD) and 27 abnormal walkers were videoed while wearing a sensor [Parkinson’s KinetiGraph (PKG)]. Median step count error rates were observed to be <3% in normal walkers but ≥3% in abnormal walkers. The simultaneous accelerometry data and data from a 6-day PKG were examined and revealed that the 5th percentile of the spectral entropy distribution, among 10-s walking epochs (obtained separately), predicted whether subjects had low error rate on step count with reference to the manual step count from the video recording. Subjects with low error rates had lower Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) scores and UPDRS III Q10–14 scores than the high error rate counterparts who also had high freezing of gait scores (i.e., freezing of gait questionnaire).ResultsPeriods when walking occurred were identified in a 6-day PKG from 190 non-PD subjects aged over 60, and 155 people with PD were examined and the 5th percentile of the spectral entropy distribution, among 10-s walking epochs, was extracted. A total of 84% of controls and 72% of people with PD had low predicted error rates. People with PD with low bradykinesia scores (measured by the PKG) had step counts similar to controls, whereas those with high bradykinesia scores had step counts similar to those with high error rates. On subsequent PKGs, step counts increased when bradykinesia was reduced by treatment and decreased when bradykinesia increased. Among both control and people with PD, low error rates were associated with those who spent considerable time making walks of more than 1-min duration.ConclusionUsing a measure of the loss of rhythmicity in walking appears to be a useful method for detecting the likelihood of error in step count. Bradykinesia in subjects with low predicted error in their step count is related to overall step count but when the predicted error is high, the step count should be assessed with caution.

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Objective measures of unobstructed walking and obstacle avoidance in Parkinson’s disease subtypes

Abstract: PIGD subtype patients showed worse modifications in objective measures of unobstructed walking and obstacle avoidance. The observed modifications may contribute to increased fall occurrence in PIGD patients.

Cited by 16 publications

References 16 publications

Enhanced Active Contraction of the Transversus Abdominis During Walking

Enhanced Active Contraction of the Transversus Abdominis During Walking

Double obstacles increase gait asymmetry during obstacle crossing in people with Parkinson’s disease and healthy older adults: A pilot study

Accuracy of Step Count Estimations in Parkinson’s Disease Can Be Predicted Using Ambulatory Monitoring

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