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Purpose: The Freezing of Gait Severity Tool (FOG Tool) was developed because of limitations in existing assessments. This cross-sectional study investigated its validity and reliability., Methods: People with Parkinson's disease (PD) were recruited consecutively from clinics. Those who could not walk eight-metres independently (with or without an assistive device), comprehend instructions, or with co-morbidities affecting walking were excluded. Participants completed a set of assessments including the FOG Tool, Timed Up and Go (TUG), and Freezing of Gait Questionnaire. The FOG Tool was repeated and those reporting no medication state change evaluated for test-retest reliability. Validity and reliability were investigated through Spearman's correlations and ICC (two-way, random). McNemar's test was applied to compare the FOG Tool and TUG on the proportion of people with freezing., Results: Thirty-nine participants were recruited [79.5%( n  = 31) male; Median(IQR): age-73.0(9.0) years; disease duration-4.0(5.8) years]. Fifteen (38.5%) contributed to test-retest reliability analyses. The FOG Tool demonstrated strongest associations with the Freezing of Gait Questionnaire (ρ = 0.67, 95%CI 0.43-0.83). Test-retest reliability was excellent (ICC = 0.96, 95%CI 0.88-0.99). The FOG Tool had 6.2 times the odds (95%CI 2.4-20.4, p  < 0.001) of triggering freezing compared to the TUG., Conclusions: The FOG Tool appeared adequately valid and reliable in this small sample of people with PD. It was more successful in triggering freezing than the TUG.

Objectives: There is no standardisation of tasks or measures for evaluation of freezing of gait severity in people with Parkinson's disease. This study aimed to develop a clinician-rated tool for freezing of gait severity (i.e. Freezing of Gait Severity Tool), through determining clinicians' ratings of the most important triggering circumstances to be examined and aspects of freezing of gait to be measured., Design: A three-round, web-based Delphi study., Participants: Healthcare professionals, with at least five years' experience in managing freezing of gait in people with Parkinson., Main Outcome Measures: Round 1 required participants ( n   =  28) to rate items on a 5-point Likert scale, based on priority for inclusion in the Freezing of Gait Severity Tool. In Round 2, participants ( n   =  18) ranked the items based on priority for inclusion. In Round 3, participants ( n   =  18) confirmed or rejected the shortlisted items by judging their ability, on a binary scale, to screen for freezing of gait, detect changes in freezing severity, and discriminate between degrees of severity., Results: Participants agreed with the triggering circumstances of turning hesitation, narrow space hesitation, start hesitation, cognitive dual-tasking, and open space hesitation should be assessed; and the aspects of gait freezing to be measured included freezing type, number of freezing episodes during a task, and average duration of freezing episodes., Conclusions: This study attained a consensus for the items to be included in a clinician-rated tool for freezing of gait severity. Future studies should investigate psychometric properties and clinical feasibility of the Freezing of Gait Severity Tool.

Parkinson's disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients' quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%.

Objective: Parkinson disease (PD) is the second-most common neurodegenerative condition worldwide. Approximately 50% of people with PD experience freezing of gait, a motor symptom associated with falls, disability, and poorer quality of life. Accurate assessment of freezing of gait severity is important for guiding management. The aim of this systematic review was to determine the measurement properties of subjective and objective clinical assessments of freezing of gait severity using the COSMIN methodology to facilitate better outcome measure selection., Methods: Three databases (MEDLINE, EMBASE, and CINAHL) were searched. The COSMIN Risk of Bias checklist was used for evaluating quality of included studies. Data on measurement properties were extracted. Where possible, meta-analysis was performed., Results: Nineteen studies investigating measurement properties of 7 outcome measures (patient-reported outcome measures, n = 3; objective assessment tools, n = 4) were included. Ten studies evaluated the Freezing of Gait Questionnaire. Based on COSMIN criteria, the best performing outcome measures were the Freezing of Gait Questionnaire and the Dynamic Parkinson Gait Scale. Both outcome measures demonstrated sufficient structural validity, internal consistency, reliability, construct validity, and responsiveness. The Freezing of Gait Questionnaire was supported with mostly moderate to high quality of evidence, but the evidence for the Dynamic Parkinson Gait Scale was of low quality due to inadequate sample size., Conclusions: Content validity and measurement invariance remain unknown for all outcome measures. The Freezing of Gait Questionnaire is the best measure currently available. Further external validation and reliability studies would strengthen the evidence to support the use of the Dynamic Parkinson Gait Scale., Impact: Awareness of limitations of outcome measures facilitates better selection. Without evidence for content validity and measurement invariance, clinicians should consider if their chosen test accurately measures freezing of gait severity and whether it can be applied in populations different to the original study's population., Lay Summary: Accurately assessing freezing of gait severity is important for guiding management of this disabling symptom. The best clinical assessment currently available is one that relies on the self-report of patients., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Physical Therapy Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Freezing of Gait is the most disabling gait disturbance in Parkinson's disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson's disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.

Freezing of Gait (FoG) is a common motor-related impairment among Parkinson's disease patients, which substantially reduces their quality of life and puts them at risk of falls. These patients benefit from wearable FoG detection systems that provide timely biofeedback cues and hence help them regain control over their gait. Unfortunately, the systems proposed thus far are bulky and obtrusive when worn. The objective of this paper is to demonstrate the first integration of an FoG detection system into a single sensor node. To achieve such an integration, features with low computational load are selected and dedicated hardware is designed that limits area and memory utilization. Classification is achieved with a neural network that is capable of learning in real time and thus allows the system to adapt to a patient during run-time. A small form factor FPGA implements the feature extraction and classification, whereas a custom PCB integrates the system into a single node. The system fits into a 4.5 × 3.5 × 1.5 cm 3 housing case, weighs 32 g, and achieves 95.6% sensitivity and 90.2% specificity when adapted to a patient. Biofeedback cues are provided either through auditory or somatosensory means and the system can remain operational for longer than 9 h while providing cues. The proposed system is highly competitive in terms of classification performance and excels with respect to wearability and real-time patient adaptivity.

Instrumenting physical assessments in people with Parkinson's disease can provide valuable and sensitive information. This study aimed to investigate whether variables derived from a Kinect-based system can provide incremental value over standard habitual gait speed (HGS) and timed up and go (TUG) variables by evaluating associations with (1) motor and (2) postural instability and gait difficulty (PIGD) subscales of the Unified Parkinson's Disease Rating Scale (UPDRS). Sixty-two individuals with Parkinson's disease (age 66 ± 7 years; 74% male) undertook an instrumented HGS and modified TUG tests, in addition to the UPDRS. Multivariable regression models were used to evaluate the associations of the Kinect measures with UPDRS motor and PIGD scores. First step length during the TUG and average step length and vertical pelvic displacement during the HGS were significantly associated with the PIGD subscale (P < 0.05). The only Kinect-derived variable showing additive benefits over the standard measures for the PIGD association was HGS vertical pelvic displacement. The only standard or Kinect-derived variable significantly associated with the motor subscale was first step length during the TUG (P < 0.01). This study provides preliminary evidence to support the use of a low-cost, non-invasive method of instrumenting gait and TUG tests in people with Parkinson's disease. Graphical abstract ᅟ.

Objectives: To systematically review the evidence investigating the effectiveness of resistance training on strength and physical function in people with Parkinson's disease., Data Sources: Seven electronic databases (COCHRANE, CINAHL, Medline ISI, Psycinfo, Scopus, Web of Science ISI and Embase) were systematically searched for full-text articles published in English between 1946 and November 2014 using relevant search terms., Review Methods: Only randomized controlled trials investigating the effects of resistance training on muscle strength and physical function in people with Parkinson's disease were considered. The PEDro scale was used to assess study quality. Studies with similar outcomes were pooled by calculating standardized mean differences (SMD) using fixed or random effects model, depending on study heterogeneity., Results: Seven studies, comprising of 401 participants with early to advanced disease (Hoehn & Yahr stage 1 to 4), were included. The median quality score was 6/10. The meta-analyses demonstrated significant SMD in favour of resistance training compared to non-resistance training or no intervention controls for muscle strength (0.61; 95% CI, 0.35 to 0.87; P <0.001), balance (0.36; 95% CI, 0.08 to 0.64; P = 0.01) and parkinsonian motor symptoms (0.48; 95% CI, 0.21 to 0.75; P < 0.001) but not for gait, balance confidence and quality of life., Conclusion: This review demonstrates that moderate intensity progressive resistance training, 2-3 times per week over 8-10 weeks can result in significant strength, balance and motor symptoms gains in people with early to moderate Parkinson's disease., (© The Author(s) 2015.)

Background: A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson's disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application ("SmartMOVE") to address both needs., Methods: The accuracy of smartphone-based gait analysis (utilizing the smartphone's built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact-based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously., Results: Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes-while at the same time, device-related measurement error yielded small-to-negligible effect sizes., Conclusion: These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient.

Gait disorders are common in people with Parkinson's disease (PD) yet their associations with activity limitations remain poorly understood. The objective of this review is to investigate the relationships between gait impairments and activity limitations in people with idiopathic PD. Six electronic databases (MEDLINE ISI, CINAHL, Web of Science ISI, EMBASE, PsycINFO and Scopus) were systematically searched using relevant terms to source for English-language studies published between 1900 and January 2011. Only studies that reported associations between impairments and activity limitations were considered for the review. The methodological quality of eligible studies was evaluated using a customized checklist by two independent reviewers. Data on participant characteristics, disease severity and associations between variables were extracted using a standardized data extraction form. Correlation coefficients were transformed to standardized scores and pooled using the fixed or random effects models. 16 of the 1735 studies screened were included, comprising 1882 participants. Large effect sizes were found between postural instability measured by the Berg Balance scale and activity limitation measured by the Unified Parkinson's Disease Rating Scale Activities of Daily Living (UPDRS ADL) section (-0.81; 95% CI, -0.99 to -0.63; p < 0.001). Gait hypokinesia measured by gait speed was significantly correlated with UPDRS ADL (-0.41; 95% CI, -0.56 to -0.26; p < 0.001). Gait freezing measured by the Freezing of Gait Questionnaire was associated with reduction in ADL (0.45; 95% CI, 0.36 to 0.54; p < 0.001). Knowledge of gait disturbances and their associations with disability may help to guide clinicians in assessing patients to optimize functional outcomes., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Objectives: To investigate the relationships between freezing of gait (FOG) and activity limitations in ambulant people with Parkinson's disease (PD), and to explore the contribution of FOG and gait hypokinesia to activity limitations after adjusting for the effects of disease severity., Design: Retrospective, cross-sectional design., Setting: Participants were recruited from neurologists' clinics and the general public in metropolitan Melbourne, Australia., Participants: Volunteers were screened for eligibility using the following inclusion criteria: diagnosis of idiopathic PD, modified Hoehn and Yahr stages 0 to IV, without dementia. Participants (N=210) were tested (mean age ± SD, 67.9±9.6y; mean PD duration ± SD, 6.7±5.6y; median Hoehn and Yahr stage=2.5)., Interventions: Not applicable., Main Outcome Measures: FOG was measured using the FOG questionnaire. Gait hypokinesia was quantified using both the 6-meter walk test and the Timed Up and Go test. Activity limitation was measured using the modified Unified Parkinson's Disease Rating Scale activities of daily living (ADL) section and the Schwab and England ADL scale., Results: Severity of gait freezing correlated significantly with the level of activity limitation (Spearman correlation coefficient, ρ=-.49 to .48; P<.001). A hierarchic regression model showed that disease severity explained 37.5% of the variance in Schwab and England ADL score (P<.001). Gait hypokinesia and FOG severity scores explained an additional 9.1% of the variance in activity limitation (R(2) change=.091; P<.001)., Conclusions: FOG severity and gait hypokinesia were associated with reduced levels of activity after adjusting for disease severity., (Copyright © 2011 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Objective Parkinson disease (PD) is the second-most common neurodegenerative condition worldwide. Approximately 50% of people with PD experience freezing of gait, a motor symptom associated with falls, disability, and poorer quality of life. Accurate assessment of freezing of gait severity is important for guiding management. The aim of this systematic review was to determine the measurement properties of subjective and objective clinical assessments of freezing of gait severity using the COSMIN methodology to facilitate better outcome measure selection. Methods Three databases (MEDLINE, EMBASE, and CINAHL) were searched. The COSMIN Risk of Bias checklist was used for evaluating quality of included studies. Data on measurement properties were extracted. Where possible, meta-analysis was performed. Results Nineteen studies investigating measurement properties of 7 outcome measures (patient-reported outcome measures, n = 3; objective assessment tools, n = 4) were included. Ten studies evaluated the Freezing of Gait Questionnaire. Based on COSMIN criteria, the best performing outcome measures were the Freezing of Gait Questionnaire and the Dynamic Parkinson Gait Scale. Both outcome measures demonstrated sufficient structural validity, internal consistency, reliability, construct validity, and responsiveness. The Freezing of Gait Questionnaire was supported with mostly moderate to high quality of evidence, but the evidence for the Dynamic Parkinson Gait Scale was of low quality due to inadequate sample size. Conclusions Content validity and measurement invariance remain unknown for all outcome measures. The Freezing of Gait Questionnaire is the best measure currently available. Further external validation and reliability studies would strengthen the evidence to support the use of the Dynamic Parkinson Gait Scale. Impact Awareness of limitations of outcome measures facilitates better selection. Without evidence for content validity and measurement invariance, clinicians should consider if their chosen test accurately measures freezing of gait severity and whether it can be applied in populations different to the original study's population. Lay Summary Accurately assessing freezing of gait severity is important for guiding management of this disabling symptom. The best clinical assessment currently available is one that relies on the self-report of patients. [ABSTRACT FROM AUTHOR]

Background: Deviation in gait performance from normative data of healthy cohorts is used to quantify gait ability. However, normative data is influenced by anthropometry and such differences among subjects impede accurate assessment. De-correlation of anthropometry from gait parameters and mobility measures is therefore desirable. Methods: 87 (42 male) healthy subjects varying form 21 to 84 years of age were assessed on gait parameters (cadence, ankle velocity, stride time, stride length) and mobility measures (the 3-meter/7-meter Timed Up-and-Go, 10-meter Walk Test). Multiple linear regression models were derived for each gait parameter and mobility measure, with anthropometric measurements (age, height, body mass, gender) and self-selected walking speed as independent variables. The resulting models were used to normalize the gait parameters and mobility measures. The normalization’s capability in de-correlating data and reducing data dispersion were evaluated. Results: Gait parameters were predominantly influenced by height and walking speed, while mobility measures were affected by age and walking speed. Normalization de-correlated data from anthropometric measurements from |rs| < 0.74 to |rs| < 0.23, and reduced data dispersion by up to 69%. Conclusion: Normalization of gait parameters and mobility measures through linear regression models augment the capability to compare subjects with varying anthropometric measurements. [ABSTRACT FROM AUTHOR]