l-DOPA and Freezing of Gait in Parkinson’s Disease: Objective Assessment through a Wearable Wireless System

Abstract

Freezing of gait (FOG) is a leading cause of falls and fractures in Parkinson’s disease (PD). The episodic and rather unpredictable occurrence of FOG, coupled with the variable response to l-DOPA of this gait disorder, makes the objective evaluation of FOG severity a major clinical challenge in the therapeutic management of patients with PD. The aim of this study was to examine and compare gait, clinically and objectively, in patients with PD, with and without FOG, by means of a new wearable system. We also assessed the effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters in patients with and without FOG. To this purpose, we recruited 28 patients with FOG, 16 patients without FOG, and 16 healthy subjects. In all participants, gait was evaluated clinically by video recordings and objectively by means of the wearable wireless system, during a modified 3-m Timed Up and Go (TUG) test. All patients performed the modified TUG test under and not under dopaminergic therapy (ON and OFF therapy). By comparing instrumental data with the clinical identification of FOG based on offline video-recordings, we also assessed the performance of the wearable system to detect FOG automatically in terms of sensitivity, specificity, positive and negative predictive values, and finally accuracy. TUG duration was longer in patients than in controls, and the amount of gait abnormalities was prominent in patients with FOG compared with those without FOG. l-DOPA improved gait significantly in patients with PD and particularly in patients with FOG mainly by reducing FOG duration and increasing specific spatiotemporal gait parameters. Finally, the overall wireless system performance in automatic FOG detection was characterized by excellent sensitivity (93.41%), specificity (98.51%), positive predictive value (89.55%), negative predictive value (97.31%), and finally accuracy (98.51%). Our study overall provides new information on the beneficial effect of l-DOPA on FOG severity and specific spatiotemporal gait parameters as objectively measured by a wearable sensory system. The algorithm here reported potentially opens to objective long-time sensing of FOG episodes in patients with PD.

Gait Analysis in Parkinson’s Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

“…In line with cardinal motor symptoms, to date, gait problems are evaluated with semiquantitative rating scales like the unified Parkinson's disease rating scale (UPDRS) [13] or the movement disorders society unified Parkinson's disease rating scale (MDS-UPDRS) [14]. In an effort to improve PD management and move towards a quantitative and home-oriented assessment and recognition of PD motor symptoms, different technologies have been used to evaluate bradykinesia [15][16][17], rigidity [17][18][19][20], tremor [21][22][23] and axial symptoms [24][25][26][27].…”

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

“…Suppa et al [66] used the TUG test to examine and compare the gait in patients with Parkinson's disease for the recognition of freezing of gait based on the duration of the TUG test, and implemented treatment for the disease, reporting accuracy of 98% in recognition of the different phases of the test.…”

Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data

“… The data provide a set of data acquired during the performance of the Timed-Up and Go test [1] , [2] , [3] with the sensors available in a mobile [ 4 , 5 ] and a BITalino devices [6] , including accelerometer, magnetometer, Electroencephalography and Electrocardiography sensors; The data is important for the creation of solutions for automatic validation of Timed-Up and Go test, and, as we acquired Electroencephalography and Electrocardiography data, it will allows to the creation of patterns of different diseases [7] , [8] , [9] , [10] for further developments; The acquired data may be used for the recognition of different stages and activities during the Timed-Up and Go test, as well as the identification of diseases with machine learning techniques [10] , [11] , [12] ; The data are valid for the creation of disease patterns associated with movement, cardiac and brain frequency, and other problems related to walking activity, applying different techniques to reduce the artefacts [13] , [14] , [15] . It also allows further research with the sensors available in off-the-shelf mobile devices for further creation of Mobile Health solutions [ 16 , 17 ].…”

“…The data is important for the creation of solutions for automatic validation of Timed-Up and Go test, and, as we acquired Electroencephalography and Electrocardiography data, it will allows to the creation of patterns of different diseases [7] , [8] , [9] , [10] for further developments;…”