Dataset Information

Progressive Gait Deficits in Parkinson's Disease: A Wearable-Based Biannual 5-Year Prospective Study.

ABSTRACT: Background: Gait changes occur during all Parkinson's disease (PD) stages and wearable sensor-derived gait parameters may quantify PD progression. However, key aspects that may qualify quantitative gait parameters as progression markers in PD remain elusive. Objectives: Longitudinal changes in gait parameters from a lower-back sensor under convenient and challenging walking conditions in early- and mid-stage PD patients (E-PD, M-PD) compared to controls were investigated. Methods: Normal- and fast-pace parameters (step: number, time, velocity, variability) were assessed every 6 months for up to 5 years in 22 E-PD (<4 years baseline disease duration), 18 M-PD (>5 years) and 24 controls. Parameter trajectories and associations with MDS-UPDRS-III were tested using generalized estimating equations. Results: Normal-pace step number (annual change in E-PD: 2.1%, Time?Group: p = 0.001) and step time variability (8.5%, p < 0.05) longitudinally increased in E-PD compared to controls (0.7%, -12%). For fast pace, no significant progression differences between groups were observed. Longitudinal changes in M-PD did not differ significantly from controls. MDS-UPDRS-III was largely associated with normal-pace parameters in M-PD. Conclusion: Wearables can quantify progressive gait deficits indicated by increasing step number and step time variability in E-PD. In M-PD, and for fast-pace, gait parameters possess limited potential as PD progression markers.

SUBMITTER: Hobert MA

PROVIDER: S-EPMC6381067 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Progressive Gait Deficits in Parkinson's Disease: A Wearable-Based Biannual 5-Year Prospective Study.

Hobert Markus A MA Nussbaum Susanne S Heger Tanja T Berg Daniela D Maetzler Walter W Heinzel Sebastian S

Frontiers in aging neuroscience 20190213

Background: Gait changes occur during all Parkinson's disease (PD) stages and wearable sensor-derived gait parameters may quantify PD progression. However, key aspects that may qualify quantitative gait parameters as progression markers in PD remain elusive. Objectives: Longitudinal changes in gait parameters from a lower-back sensor under convenient and challenging walking conditions in early- and mid-stage PD patients (E-PD, M-PD) compared to controls were investigated. Method ...[more]

PMID: 30814947

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Project description:Distinct gait characteristics like short steps and shuffling gait are prototypical signs commonly observed in Parkinson's disease. Routinely assessed by observation through clinicians, gait is rated as part of categorical clinical scores. There is an increasing need to provide quantitative measurements of gait, e.g. to provide detailed information about disease progression. Recently, we developed a wearable sensor-based gait analysis system as diagnostic tool that objectively assesses gait parameter in Parkinson's disease without the need of having a specialized gait laboratory. This system consists of inertial sensor units attached laterally to both shoes. The computed target of measures are spatiotemporal gait parameters including stride length and time, stance phase time, heel-strike and toe-off angle, toe clearance, and inter-stride variation from gait sequences. To translate this prototype into medical care, we conducted a cross-sectional study including 190 Parkinson's disease patients and 101 age-matched controls and measured gait characteristics during a 4x10 meter walk at the subjects' preferred speed. To determine intraindividual changes in gait, we monitored the gait characteristics of 63 patients longitudinally. Cross-sectional analysis revealed distinct spatiotemporal gait parameter differences reflecting typical Parkinson's disease gait characteristics including short steps, shuffling gait, and postural instability specific for different disease stages and levels of motor impairment. The longitudinal analysis revealed that gait parameters were sensitive to changes by mirroring the progressive nature of Parkinson's disease and corresponded to physician ratings. Taken together, we successfully show that wearable sensor-based gait analysis reaches clinical applicability providing a high biomechanical resolution for gait impairment in Parkinson's disease. These data demonstrate the feasibility and applicability of objective wearable sensor-based gait measurement in Parkinson's disease reaching high technological readiness levels for both, large scale clinical studies and individual patient care.

Project description:To examine regional cerebral vesicular acetylcholine transporter (VAChT) ligand [18F]fluoroethoxybenzovesamicol ([18F]-FEOBV) PET binding in Parkinson' disease (PD) patients with and without vestibular sensory conflict deficits (VSCD). To examine associations between VSCD-associated cholinergic brain deficits and postural instability and gait difficulties (PIGD). PD persons (M70/F22; mean age 67.6 ± 7.4 years) completed clinical assessments for imbalance, falls, freezing of gait (FoG), modified Romberg sensory conflict testing, and underwent VAChT PET. Volumes of interest (VOI)-based analyses included detailed thalamic and cerebellar parcellations. VSCD-associated VAChT VOI selection used stepwise logistic regression analysis. Vesicular monoamine transporter type 2 (VMAT2) [11C]dihydrotetrabenazine (DTBZ) PET imaging was available in 54 patients. Analyses of covariance were performed to compare VSCD-associated cholinergic deficits between patients with and without PIGD motor features while accounting for confounders. PET sampling passed acceptance criteria in 73 patients. This data-driven analysis identified cholinergic deficits in five brain VOIs associating with the presence of VSCD: medial geniculate nucleus (MGN) (P < 0.0001), para-hippocampal gyrus (P = 0.0043), inferior nucleus of the pulvinar (P = 0.047), fusiform gyrus (P = 0.035) and the amygdala (P = 0.019). Composite VSCD-associated [18F]FEOBV-binding deficits in these 5 regions were significantly lower in patients with imbalance (- 8.3%, F = 6.5, P = 0.015; total model: F = 5.1, P = 0.0008), falls (- 6.9%, F = 4.9, P = 0.03; total model F = 4.7, P = 0.0015), and FoG (- 14.2%, F = 9.0, P = 0.0043; total model F = 5.8, P = 0.0003), independent of age, duration of disease, gender and nigrostriatal dopaminergic losses. Post hoc analysis using MGN VAChT binding as the single cholinergic VOI demonstrated similar significant associations with imbalance, falls and FoG. VSCD-associated cholinergic network changes localize to distinct structures involved in multi-sensory, in particular vestibular, and multimodal cognitive and motor integration brain regions. Relative clinical effects of VSCD-associated cholinergic network deficits were largest for FoG followed by postural imbalance and falls. The MGN was the most significant region identified.

Project description:The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events-bout segmentation, initial contact (IC), and final contact (FC)-from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson's disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56-64.66 and 40.19-72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06-48.42, 40.19-72.70 and 36.06-60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.

Dataset Information

Progressive Gait Deficits in Parkinson's Disease: A Wearable-Based Biannual 5-Year Prospective Study.

Publications

Progressive Gait Deficits in Parkinson's Disease: A Wearable-Based Biannual 5-Year Prospective Study.

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