scholarly journals Association between Objectively Measured Physical Activity and Gait Patterns in People with Parkinson’s Disease: Results from a 3-Month Monitoring

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Micaela Porta ◽  
Giuseppina Pilloni ◽  
Roberta Pili ◽  
Carlo Casula ◽  
Mauro Murgia ◽  
...  
Keyword(s):  
Physical Activity ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Time Slot ◽  
Moderate Intensity ◽  
Kinematic Parameters ◽  
Gait Patterns ◽  
Flexion Extension ◽  
Vigorous Intensity

Background. Although physical activity (PA) is known to be beneficial in improving motor symptoms of people with Parkinson’s disease (pwPD), little is known about the relationship between gait patterns and features of PA performed during daily life. Objective. To verify the existence of possible relationships between spatiotemporal and kinematic parameters of gait and amount/intensity of PA, both instrumentally assessed. Methods. Eighteen individuals affected by PD (10F and 8M, age 68.0 ± 10.8 years, 1.5 ≤ Hoehn and Yahr (H&Y) < 3) were required to wear a triaxial accelerometer 24 h/day for 3 consecutive months. They also underwent a 3D computerized gait analysis at the beginning and end of the PA assessment period. The number of daily steps and PA intensity were calculated on the whole day, and the period from 6:00 to 24:00 was grouped into 3 time slots, using 3 different cut-point sets previously validated in the case of both pwPD and healthy older adults. 3D gait analysis provided spatiotemporal and kinematic parameters of gait, including summary indexes of quality (Gait Profile Score (GPS) and Gait Variable Score (GVS)). Results. The analysis of hourly trends of PA revealed the existence of two peaks located in the morning (approximately at 10) and in the early evening (between 18 and 19). However, during the morning time slot (06:00–12:00), pwPD performed significantly higher amounts of steps (4313 vs. 3437 in the 12:00–18:00 time slot, p<0.001, and vs. 2889 in the 18:00–24:00 time slot, p=0.021) and of moderate-to-vigorous PA (43.2% vs. 36.3% in the 12:00–18:00 time slot, p=0.002, and vs. 31.4% in the 18:00–24:00 time slot, p=0.049). The correlation analysis shows that several PA intensity parameters are significantly associated with swing-phase duration (rho = −0.675 for sedentary intensity, rho = 0.717 for moderate-to-vigorous intensity, p<0.001), cadence (rho = 0.509 for sedentary intensity, rho = −0.575 for moderate-to-vigorous intensity, p<0.05), and overall gait pattern quality as expressed by GPS (rho = −0.498 to −0.606 for moderate intensity, p<0.05) and GVS of knee flexion-extension (rho = −0.536 for moderate intensity, p<0.05). Conclusions. Long-term monitoring of PA integrated by the quantitative assessment of spatiotemporal and kinematic parameters of gait may represent a useful tool in supporting a better-targeted prescription of PA and rehabilitative treatments in pwPD.

scholarly journals An artificial neural network approach to detect presence and severity of Parkinson’s disease via gait parameters

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244396
Author(s):  
Tiwana Varrecchia ◽  
Stefano Filippo Castiglia ◽  
Alberto Ranavolo ◽  
Carmela Conte ◽  
Antonella Tatarelli ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Disease Progression ◽  
Walking Speed ◽  
Machine Learning Techniques ◽  
Trunk Rotation ◽  
Neural Network Approach ◽  
Gait Patterns ◽  
Artificial Neural

Introduction Gait deficits are debilitating in people with Parkinson’s disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease. Objectives Our study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers. Methods We evaluated 76 PwPD (H-Y stage 1–4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage. Results We identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs). Conclusion The ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson’s disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression.

Effects of Onabotulinum Toxin A on Gait in Parkinson’s Disease Patients with Foot Dystonia

2021 ◽  
pp. 1-6
Author(s):  
Pei Huang ◽  
Yuan-Yuan Li ◽  
Jung E. Park ◽  
Ping Huang ◽  
Qin Xiao ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Botulinum Toxin ◽  
Gait Analysis ◽  
Rating Scale ◽  
Analog Scale ◽  
Toxin A ◽  
Timed Up And Go ◽  
Balance Test ◽  
Onabotulinum Toxin A

ABSTRACT: We investigated the effects of botulinum toxin on gait in Parkinson’s disease (PD) patients with foot dystonia. Six patients underwent onabotulinum toxin A injection and were assessed by Burke–Fahn–Marsden Dystonia Rating Scale (BFMDRS), visual analog scale (VAS) of pain, Timed Up and Go (TUG), Berg Balance Test (BBT), and 3D gait analysis at baseline, 1 month, and 3 months. BFMDRS (p = 0.002), VAS (p = 0.024), TUG (p = 0.028), and BBT (p = 0.034) were improved. Foot pressures at Toe 1 (p = 0.028) and Midfoot (p = 0.018) were reduced, indicating botulinum toxin’s effects in alleviating the dystonia severity and pain and improving foot pressures during walking in PD.

The use of eHealth to promote physical activity in people living with Parkinson’s disease: A systematic review

2021 ◽  
pp. 1-14
Author(s):  
Adam McDermott ◽  
Ciaran Haberlin ◽  
Jonathan Moran
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Risk Of Bias ◽  
Attrition Rate ◽  
Medical Subject Headings ◽  
Eligibility Criteria ◽  
Ehealth Intervention ◽  
High Level

BACKGROUND: People living with Parkinson’s disease (PD) are less active than healthy individuals. Ehealth is an emerging concept in healthcare which presents opportunities to promote physical activity (PA) in people with PD. The aim of this systematic review was to explore the effectiveness of ehealth in the promotion of PA in people living with PD. METHODS: Suitable articles were searched for using EMBASE, PsychInfo, Web of Science and OVID Medline databases using a combination of keywords and medical subject headings. Articles were included if they described an ehealth intervention designed to promote PA in people living with PD. Two reviewers screened studies for suitability and extracted data. Risk of bias was assessed using the Cochrane risk of bias 2 tool and the Downs and Black risk of bias checklist. Due to the heterogeneity of studies, a narrative synthesis of study interventions and results was completed rather than a quantitative analysis. RESULTS: 1449 articles were screened. Four studies met the eligibility criteria which included 652 participants. Web and mobile applications were used to design the PA interventions. PA levels were measured using self-reported questionnaires, Fitbits, activity monitors and accelerometers. Three of the studies reported improvements in aspects of PA. However, this was not consistently reported in all study participants. No adverse effects, a high level of enjoyment and a relatively low attrition rate (∼12.5%) were reported. CONCLUSION: Ehealth is a safe and feasible intervention to promote PA in this population. It is unclear whether ehealth is effective at promoting PA in people with PD. Keywords:

scholarly journals Misreporting of Physical Activity and Sedentary Behavior in Parents-to-Be: A Validation Study across Sex

2021 ◽  
Vol 18 (9) ◽  
pp. 4654
Author(s):  
Tom Deliens ◽  
Vickà Versele ◽  
Jasper Jehin ◽  
Eva D’Hondt ◽  
Yanni Verhavert ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sedentary Behavior ◽  
International Physical Activity Questionnaire ◽  
Moderate Intensity ◽  
Strong Correlations ◽  
Self Reports ◽  
Vigorous Intensity ◽  
Context Specific ◽  
Acceptable Agreement ◽  
Activity Questionnaire

This study validated the International Physical Activity Questionnaire (IPAQ) and the Context-specific Sedentary Behavior Questionnaire (CSBQ) against accelerometry among parents-to-be. Sex-differences in potential misreporting of physical activity (PA) and sedentary behavior (SB) were also investigated. Self-reported total PA (TPA), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate-to-vigorous-intensity PA (MVPA), and SB of 91 parents-to-be (41 men and 50 women) were compared with Actigraph data according to sex. Furthermore, the extent of misreporting was compared between sexes. Strong correlations for TPA and weak-to-moderate correlations for LPA, MPA, VPA, MVPA, and SB were observed. Participants underestimated TPA by 1068 min/week (=17.8 h/week; −50%), LPA by 1593 min/week (=26.6 h/week; −83%), and SB by 428 min/week (=7.1 h/week; −11%) and overestimated MPA by 384 min/week (=6.4 h/week; +176%) and MVPA by 525 min/week (=8.8 h/week; +224%). Males overreported VPA more than females in absolute minutes per week (238 min/week, i.e., 4.0 h/week vs. 62 min/week, i.e., 1.0 h/week), whereas, in relative terms, the opposite (+850% vs. +1033%) was true. The IPAQ and CSBQ can be used with caution to estimate TPA and SB among parents-to-be considering a strong correlation but low agreement for TPA and a weak-to-moderate correlation but acceptable agreement for SB. We disadvise using these self-reports to estimate PA on the distinct intensity levels.

scholarly journals Gait analysis for early Parkinson’s disease detection based on deep learning

2019 ◽  
Vol 5 (1) ◽  
pp. 9-12
Author(s):  
Jyothsna Kondragunta ◽  
Christian Wiede ◽  
Gangolf Hirtz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Gait Analysis ◽  
Pose Estimation ◽  
Early Identification ◽  
Human Gait ◽  
3D Pose Estimation ◽  
Wearable Technologies ◽  
Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

scholarly journals Physical activity and risk of Parkinson’s disease in the Swedish National March Cohort

Brain ◽  
2014 ◽  
Vol 138 (2) ◽  
pp. 269-275 ◽  
Author(s):  
Fei Yang ◽  
Ylva Trolle Lagerros ◽  
Rino Bellocco ◽  
Hans-Olov Adami ◽  
Fang Fang ◽  
...  
Keyword(s):  
Physical Activity ◽  
Parkinson’S Disease ◽  
Parkinson's Disease
Sign in / Sign up

Export Citation Format

Share Document