Quantifying turning behavior and gait in Parkinson's disease using mobile technology

"TiC-TUG": technology in clinical practice using the instrumented timed up and go test-a scoping review

Digitized assessments have a considerable potential to guide clinicial decision making and monitor progress and disease trajectories. The Timed Up and Go test (TUG) has been long established for assessment in geriatric medicine and instrumented versions (iTUG) have been developed and validated. This scoping review includes studies that applied the iTUG and aims to identify use cases to show where and how iTUG assessment could guide interventions and clinical management. The literature search was limited to peer-reviewed studies that performed pre- and post-intervention measurements with a 3-meter TUG instrumented with body-worn technology in samples of at least 20 subjects aged 60+ years. Of 3018 identified articles 20 were included. Four clinical use cases were identified: stratification for subsequent therapy, monitoring of disease or treatment-associated changes and evaluation of interventions in patients with idiopathic normal pressure hydrocephalus (1), and patients with Parkinson's disease (2); monitoring after joint replacement surgery (3), and evaluation after different exercise and rehabilitation interventions (4). The included studies show diversity in terms of iTUG technology and procedures. The identified use cases highlight clinical relevance and high potential for the clinical application of the iTUG. A consensual approach as well as comprehensive reporting would help to further exploit the potential of the iTUG to support clinical management. Future studies should investigate the benefits of segmental iTUG analysis, responsiveness and participants' perspectives on clinically meaningful changes in iTUG.

Smartphone applications for Movement Disorders: Towards collaboration and re-use

BACKGROUND

Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing.

OBJECTIVES

To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders.

METHODS

We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores.

RESULTS

We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package.

CONCLUSIONS

The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.

Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Classification of mild Parkinson's disease: data augmentation of time-series gait data obtained via inertial measurement units

Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson's disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in the medical field. This study investigated effective data-augmentation methods to classify mild Parkinson's disease and healthy participants with deep learning using a time-series gait dataset recorded via a shank-worn inertial measurement unit. Four magnitude-domain-transformation and three time-domain-transformation data-augmentation methods, and four methods involving mixtures of the aforementioned methods were applied to a representative convolutional neural network for the classification, and their performances were compared. In terms of data-augmentation, compared with baseline classification accuracy without data-augmentation, the magnitude-domain transformation performed better than the time-domain transformation and mixed-data augmentation. In the magnitude-domain transformation, the rotation method significantly contributed to the best performance improvement, yielding accuracy and F1-score improvements of 5.5 and 5.9%, respectively. The augmented data could be varied while maintaining the features of the time-series data obtained via the sensor for detecting mild Parkinson's in gait; this data attribute may have caused the aforementioned trend. Notably, the selection of appropriate data extensions will help improve the classification performance for mild Parkinson's disease.

Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review

The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information on mobility status. To identify and compare current methods for subtask segmentation using inertial sensor data, a scoping review of the literature was conducted using PubMed, Scopus, and Google Scholar. Articles were identified that described subtask segmentation methods for the TUG and L Test using only inertial sensor data. The filtering method, ground truth estimation device, demographic, and algorithm type were compared. One article segmenting the L Test and 24 articles segmenting the TUG met the criteria. The articles were published between 2008 and 2022. Five studies used a mobile smart device’s inertial measurement system, while 20 studies used a varying number of external inertial measurement units. Healthy adults, people with Parkinson’s Disease, and the elderly were the most common demographics. A universally accepted method for segmenting the TUG test and the L Test has yet to be published. Angular velocity in the vertical and mediolateral directions were common signals for subtask differentiation. Increasing sample sizes and furthering the comparison of segmentation methods with the same test sets will allow us to expand the knowledge generated from these clinically accessible tests.

Identification of Gait Unbalance and Fallers Among Subjects with Cerebellar Ataxia by a Set of Trunk Acceleration-Derived Indices of Gait

This study aimed to assess the ability of 25 gait indices to characterize gait instability and recurrent fallers among persons with primary degenerative cerebellar ataxia (pwCA), regardless of gait speed, and investigate their correlation with clinical and kinematic variables. Trunk acceleration patterns were acquired during the gait of 34 pwCA, and 34 age- and speed-matched healthy subjects (HS_matched) using an inertial measurement unit. We calculated harmonic ratios (HR), percent recurrence, percent determinism, step length coefficient of variation, short-time largest Lyapunov exponent (sLLE), normalized jerk score, log-dimensionless jerk (LDLJ-A), root mean square (RMS), and root mean square ratio of accelerations (RMSR) in each spatial direction for each participant. Unpaired t-tests or Mann-Whitney tests were performed to identify significant differences between the pwCA and HS_matched groups. Receiver operating characteristics were plotted to assess the ability to characterize gait alterations in pwCA and fallers. Optimal cutoff points were identified, and post-test probabilities were calculated. The HRs showed to characterize gait instability and pwCA fallers with high probabilities. They were correlated with disease severity and stance, swing, and double support duration, regardless of gait speed. sLLEs, RMSs, RMSRs, and LDLJ-A were slightly able to characterize the gait of pwCA but failed to characterize fallers.

Development of a mobile application for assessing reaction time in walking and TUG duration: Concurrent validity in female older adults

Introduction

The TUG can be used to distinguish between people who fall and people who don't fall. To evaluate cognitive dual-task performance while walking for fall prediction, TUG-dual was frequently employed. A recent study has created a mobile application that enables simple interaction to provide greater convenience for monitoring the duration of TUG, TUG-subtraction, and reaction time.

Objective

The research aim was to ascertain the concurrent validity of the mobile application that was developed for the clinical assessment of TUG, TUG-subtraction, and reaction time.

Methods

Twenty-nine older persons participated in this study. The testing protocol involved the TUG, TUG-subtraction, and reaction time assessment. For TUG and TUG-subtraction, the duration to complete the task was recorded by the APDM Mobility Lab system and the mobile application. For the reaction time tests, the reaction times (msec) were recorded by the Multi Choice Reaction timer and the Mobile application. The TUG durations recorded by the APDM Mobility Lab system were correlated with those recorded by the mobile application to verify the concurrent validity using Pearson's product moment correlation coefficient. Also, the reaction time by the Multi Choice Reaction timer was correlated with the mobile application. Bland-Altman plots were used to explore the existence of any systematic differences between the measurements.

Results

Our results showed very strong correlations between the TUG and TUG-subtraction duration derived from the APDM Mobility Lab system and the mobile application (r = 0.96 and 0.96, respectively). For the reaction time, the results showed a moderate correlation between the reaction time derived from the mobile application and the Multi Choice Reaction Timer (r = 0.67).

Conclusion

The mobile application, which allows measurement in TUG and TUG-subtraction, is a highly valid tool for TUG duration assessment. However, this application is capable for assess the reaction time with moderate validity for reaction time assessment.

Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity

Introduction

Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.

Methods

Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.

Results

3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.

Conclusion

We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.

An investigation of the contribution of different turn speeds during standing turns in individuals with and without Parkinson's disease

Issues around turning can impair daily tasks and trigger episodes of freezing of gait in individuals with Parkinson's disease (PD). Slow speeds associated with aging produce a more en-bloc movement strategy which have been linked with falls while turning. However, the influence of speed of turning on the complex whole-body coordination considering eye movements, turning kinematics, and stepping characteristics during turning has not been examined. The aim of this study was to investigate if individuals with PD have a different response to changes in turning speed compared to healthy older adults during 180° standing turns. 20 individuals with PD and 20 healthy age matched adults participated in this study. Data were collected during clockwise and counter-clockwise turns at three self-selected speeds in a randomised order: (a) normal; (b) faster than normal; and (c) slower than normal. Eye movement and turning kinematics were investigated using electrooculography and Inertial Measurement Units. Mixed Model Analysis of Variance (MM ANOVA) tests with post hoc pairwise comparisons were performed to assess the differences between groups and turning speed. In addition, further post hoc Repeated Measures ANOVA (RM ANOVA) tests were performed if any significant interactions were seen between groups and turning speed. Significant interaction effects were found in eye movement and turning kinematics, and the RM ANOVA showed significant main effects for turning speeds within the PD and the control groups. Turning slowly resulted in similar alterations in eye movement, turning kinematics and stepping characteristics in the PD group and the healthy controls. However, individuals with PD showed a different response to the healthy controls, with a greater delay in eye movement and onset latency of segments in turning kinematics and step variables between the different speeds. These findings help our understanding regarding the turning strategies in individuals with PD. The incorporation of guidance with regard to faster turning speeds may be useful in the management of individuals with PD. Clinical training using different turn directions and speeds may improve coordination, increase confidence and reduce the risk of falling.

Vestibular Rehabilitation Improves Gait Quality and Activities of Daily Living in People with Severe Traumatic Brain Injury: A Randomized Clinical Trial

Neurorehabilitation research in patients with traumatic brain injury (TBI) showed how vestibular rehabilitation (VR) treatments positively affect concussion-related symptoms, but no studies have been carried out in patients with severe TBI (sTBI) during post-acute intensive neurorehabilitation. We aimed at testing this effect by combining sensor-based gait analysis and clinical scales assessment. We hypothesized that integrating VR in post-acute neurorehabilitation training might improve gait quality and activity of daily living (ADL) in sTBI patients. A two-arm, single-blind randomized controlled trial with 8 weeks of follow-up was performed including thirty sTBI inpatients that underwent an 8-week rehabilitation program including either a VR or a conventional program. Gait quality parameters were obtained using body-mounted magneto-inertial sensors during instrumented linear and curvilinear walking tests. A 4X2 mixed model ANOVA was used to investigate session−group interactions and main effects. Patients undergoing VR exhibited improvements in ADL, showing early improvements in clinical scores. Sensor-based assessment of curvilinear pathways highlighted significant VR-related improvements in gait smoothness over time (p < 0.05), whereas both treatments exhibited distinct improvements in gait quality. Integrating VR in conventional neurorehabilitation is a suitable strategy to improve gait smoothness and ADL in sTBI patients. Instrumented protocols are further promoted as an additional measure to quantify the efficacy of neurorehabilitation treatments.

Use of Filters to Smooth Out Signals Collected through Mobile Devices in the Static and Dynamic Balance Assessment: A Systematic Review

Background: When performing motion analysis using sensors, the signal often comes with noise and it is necessary to use filters to exclude unwanted frequencies. For this reason, the objective of this work was to carry out a systematic review on the filters used in data recorded from smartphone applications for static and dynamic balance assessment. Methods: A systematic literature review was performed on the PubMed, ScienceDirect, Scopus, Technology Research and Web of Science databases, using the search strategy: smartphone, “mobile technology”, evaluation, “postural stability”, and balance. Results: 427 articles were found (PubMed = 107; ScienceDirect = 67; Scopus = 106; Web of Science = 95; Technology research database = 52). After applying the inclusion criteria and removing duplicates, nine studies were eligible for this review. In these studies, the fourth-order Butterworth low-pass filter was the most applied (N = 6) and the cutoff frequency of 4 Hz (N = 2) was the most frequent. Conclusions: In general, few studies have adequately described the filter used in signal processing. This step, when hidden, negatively affects the reproducibility of studies. Understanding and describing the signal processing is important not only for the correct description of the results but also for the reproducibility of the studies.

Sensitivity to gait improvement after levodopa intake in Parkinson's disease: A comparison study among synthetic kinematic indices

The synthetic indices are widely used to describe balance and stability during gait. Some of these are employed to describe the gait features in Parkinson's disease (PD). However, the results are sometimes inconsistent, and the same indices are rarely used to compare the individuals affected by PD before and after levodopa intake (OFF and ON condition, respectively). Our aim was to investigate which synthetic measure among Harmonic Ratio, Jerk Ratio, Golden Ratio and Trunk Displacement Index is representative of gait stability and harmony, and which of these are more sensitive to the variations between OFF and ON condition. We found that all indices, except the Jerk Ratio, significantly improve after levodopa. Only the improvement of the Trunk Displacement Index showed a direct correlation with the motor improvement measured through the clinical scale UPDRS-III (Unified Parkinson's Disease Rating Scale-part III). In conclusion, we suggest that the synthetic indices can be useful to detect motor changes induced by, but not all of them clearly correlate with the clinical changes achieved with the levodopa administration. In our analysis, only the Trunk Displacement Index was able to show a clear relationship with the PD clinical motor improvement.

The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Background

Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.

Methods

Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.

Results

The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively.

Conclusions

Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-00992-x.

Quantitative Gait Analysis Using a Pose-Estimation Algorithm with a Single 2D-Video of Parkinson's Disease Patients

BACKGROUND

Clinician-based rating scales or questionnaires for gait in Parkinson's disease (PD) are subjective and sensor-based analysis is limited in accessibility.

OBJECTIVE

To develop an easily accessible and objective tool to evaluate gait in PD patients, we analyzed gait from a single 2-dimensional (2D) video.

METHODS

We prospectively recorded 2D videos of PD patients (n = 16) and healthy controls (n = 15) performing the timed up and go test (TUG). The gait was simultaneously evaluated with a pressure-sensor (GAITRite). We estimated the 3D position of toes and heels with a deep-learning based pose-estimation algorithm and calculated gait parameters including step length, step length variability, gait velocity and step cadence which was validated with the result from the GAITRite. We further calculated the time and steps required for turning. Then, we applied the algorithm to previously recorded and archived videos of PD patients (n = 32) performing the TUG.

RESULTS

From the validation experiment, gait parameters derived from video tracking were in excellent agreement with the parameters obtained with the GAITRite. (Intraclass correlation coefficient > 0.9). From the analysis with the archived videos, step length, gait velocity, number of steps, and the time required for turning were significantly correlated (Absolute R > 0.4, p < 0.005) with the Freezing of gait questionnaire, Unified PD Rating scale part III total score, HY stage and postural instability. Furthermore, the video-based tracking objectively measured significant improvement of step length, gait velocity, steps and the time required for turning with antiparkinsonian medication.

CONCLUSION

2D video-based tracking could objectively evaluate gait in PD patients.

Effectiveness of a Long-Term, Home-Based Aerobic Exercise Intervention on Slowing the Progression of Parkinson Disease: Design of the Cyclical Lower Extremity Exercise for Parkinson Disease II (CYCLE-II) Study

OBJECTIVE

Previous short duration studies have demonstrated that high-intensity aerobic exercise improves aspects of motor and non-motor function in people with Parkinson disease (PwPD); however, the effectiveness of a long-term exercise intervention on slowing disease progression is unknown. The primary aim of this study is to determine the disease-altering effects of high-intensity aerobic exercise, administered on an upright stationary cycle, on the progression of PD. A secondary aim is to develop a prognostic model for 12-month changes in the Movement Disorder Society Unified Parkinson's Disease Rating Scale III (MDS-UPDRS III) of PwPD undergoing an aerobic exercise intervention.

METHODS

This pragmatic, multisite, single-rater blinded, randomized controlled trial will recruit PwPD from 2 large, urban, academic medical centers. Participants (N = 250 PwPD) will be randomized to (1) home-based aerobic exercise or (2) usual and customary care. Those in the aerobic exercise arm will be asked to complete in-home aerobic exercise sessions at 60% to 80% of heart rate reserve 3 times per week for 12 months utilizing a commercially available upright exercise cycle. The usual and customary care group will continue normal activity levels. Daily activity will be monitored for both groups throughout the 12-month study period. The primary outcome, both to assess disease-modifying response to aerobic exercise and for prognostic modeling in the aerobic exercise arm, is 12-month rate of change in the MDS-UPDRS III. Clinical and biomechanical measures will also be used to assess upper and lower extremity motor function as well as non-motor functions.

IMPACT

Should long-term aerobic exercise demonstrate disease-modifying capability, this study will provide evidence that "Exercise is Medicine" for PwPD. Further, the derived prognostic model will inform a patient-specific exercise prescription for PwPD and expected effects on PD progression.

Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study

Background

Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD).

Objective

Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC).

Methods

Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects.

Results

Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.

Conclusions

These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications.

Ability of a Set of Trunk Inertial Indexes of Gait to Identify Gait Instability and Recurrent Fallers in Parkinson's Disease

The aims of this study were to assess the ability of 16 gait indices to identify gait instability and recurrent fallers in persons with Parkinson’s disease (pwPD), regardless of age and gait speed, and to investigate their correlation with clinical and kinematic variables. The trunk acceleration patterns were acquired during the gait of 55 pwPD and 55 age-and-speed matched healthy subjects using an inertial measurement unit. We calculated the harmonic ratios (HR), percent recurrence, and percent determinism (RQAdet), coefficient of variation, normalized jerk score, and the largest Lyapunov exponent for each participant. A value of ≤1.50 for the HR in the antero-posterior direction discriminated between pwPD at Hoehn and Yahr (HY) stage 3 and healthy subjects with a 67% probability, between pwPD at HY 3 and pwPD at lower HY stages with a 73% probability, and it characterized recurrent fallers with a 77% probability. Additionally, HR in the antero-posterior direction was correlated with pelvic obliquity and rotation. RQAdet in the antero-posterior direction discriminated between pwPD and healthy subjects with 67% probability, regardless of the HY stage, and was correlated with stride duration and cadence. Therefore, HR and RQA_det in the antero-posterior direction can both be used as age- and-speed-independent markers of gait instability.

Use of a Smartphone to Gather Parkinson's Disease Neurological Vital Signs during the COVID-19 Pandemic

Introduction

To overcome travel restrictions during the COVID-19 pandemic, consumer-based technology was rapidly deployed to the smartphones of individuals with Parkinson's disease (PD) participating in a 12-month exercise trial. The aim of the project was to determine the feasibility of utilizing a combined synchronous and asynchronous self-administered smartphone application to characterize PD symptoms.

Methods

A synchronous video virtual visit was completed for the administration of virtual Movement Disorder Society-Unified Parkinson's Disease Rating Scale III (vMDS-UPDRS III). Participants asynchronously completed a mobile application consisting of a measure of upper extremity bradykinesia (Finger Tapping Test) and information processing.

Results

Twenty-three individuals completed the assessments. The mean vMDS-UPDRS III was 23.65 ± 8.56 points. On average, the number of taps was significantly greater for the less affected limb, 97.96 ± 17.77 taps, compared to the more affected, 89.33 ± 18.66 taps (p = 0.025) with a significantly greater number of freezing episodes for the more affected limb (p < 0.05). Correlation analyses indicated the number of errors and the number of freezing episodes were significantly related to clinical ratings of vMDS-UPDRS III bradykinesia (Rho = 0.44, p < 0.01; R = 0.43, p < 0.01, resp.) and finger tapping performance (Rho = 0.31, p = 0.03; Rho = 0.32, p = 0.03, resp.). Discussion. The objective characterization of bradykinesia, akinesia, and nonmotor function and their relationship with clinical disease metrics indicate smartphone technology provides a remote method of characterizing important aspects of PD performance. While theoretical and position papers have been published on the potential of telemedicine to aid in the management of PD, this report translates the theory into a viable reality.

Impact of dual-tasking on mobility tasks in Parkinson's disease as described through 2D kinematic analysis

BACKGROUND

Walking for people with Parkinson's disease (PD) degrades during motor-cognitive interplay (i.e., dual task conditions). Declining gait mechanics and turning ability result in more frequent falls and an interruption of daily activities in persons with PD.

AIMS

To determine the impact of dual-tasking on key mobility elements during a walking task in people with PD with 2D motion analysis.

METHODS

Participants performed Timed Up and Go (TUG) single, dual task conditions (TUG_alone, TUG_motor, and TUG_cognitive). 2D motion analysis application was used to quantify seven key mobility elements including: sit-to-walk (STW) (s), walking turn time (WTT) (s), number of turn steps, turn-to-sit (TTS) (s), total number of TUG steps, total TUG time and turn strategy (on-the-spot or u-shaped).

RESULTS

Thirty-one participants with PD completed this study [age M= 69 ± 8.19, UPDRSm M= 23.21 ± 10.03, HY MED= 2 (range 1-4)]. All key elements were significantly different between TUG conditions with the exception of sit-to-walk and turn strategy. Turn strategy was consistent across TUG tasks despite added cognitive loading. Repeated-measures MANOVA differences were observed in WTT (p = 0.01), number of turn steps (p = 0.03), TTS (p < 0.001), total number of TUG steps (p = 0.01), and total TUG time (p = 0.01). No significant relationships were found between disease severity (HY/UPDRSm) and turn strategy.

DISCUSSION/CONCLUSION

Key mobility elements were significantly affected across dual task walking conditions in persons with PD. The use of 2D motion analysis assisted with identification of key mobility elements impacted during the single and dual task conditions.

Measures of balance and falls risk prediction in people with Parkinson's disease: a systematic review of psychometric properties

OBJECTIVE

To investigate the psychometric properties of measures of balance and falls risk prediction in people with Parkinson's disease (PD).

DATA SOURCES

PubMed, Embase, CINAHL, Ovid Medline, Scopus, and Web of Science were searched from inception to August 2019.

REVIEW METHOD

Studies testing psychometric properties of measures of balance and falls risk prediction in PD were included. The four-point COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) assessed quality.

RESULTS

Eighty studies testing 68 outcome measures were reviewed; 43 measures assessed balance, 9 assessed falls risk prediction, and 16 assessed both. The measures with robust psychometric estimation with acceptable properties were the (1) Mini-Balance Evaluation Systems Test (Mini-BEST), (2) Berg Balance Scale, (3) Timed Up and Go test, (4) Falls Efficacy Scale International, and (5) Activities-Specific Balance Confidence scale. These measures assess balance and falls risk prediction at the body, structure and function level, falls risk and balance, and falls risk at the activity level. The motor examination of the Unified Parkinson's Disease Rating Scale (UPDRS-ME) with robust psychometric analysis is a condition-specific measure with acceptable properties. Except the UPDRS-ME and Mini-BESTest, the responsiveness of the other four measures has yet to be established.

CONCLUSION

Six of the 68 outcome measures have strong psychometric properties for the assessment of balance and falls risk prediction in PD. Measures assessing balance and falls risk prediction at the participatory level are limited in number with a lack of psychometric validation.

Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait - an analysis using inertial measurement units

Background

Impairments of functional mobility may affect locomotion and quality of life in subjects with Parkinson’s disease (PD). Movement smoothness measurements, such as the spectral arc length (SPARC), are novel approaches to quantify movement quality. Previous studies analyzed SPARC in simple walking conditions. However, SPARC outcomes during functional mobility tasks in subjects with PD and freezing of gait (FOG) were never investigated. This study aimed to analyze SPARC during the Timed Up and Go (TUG) test in individuals with PD and FOG.

Methods

Thirty-one participants with PD and FOG and six healthy controls were included. SPARC during TUG test was calculated for linear and angular accelerations using an inertial measurement unit system. SPARC data were correlated with clinical parameters: motor section of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr scale, Freezing of Gait Questionnaire, and TUG test.

Results

We reported lower SPARC values (reduced smoothness) during the entire TUG test, turn and stand to sit in subjects with PD and FOG, compared to healthy controls. Unlike healthy controls, individuals with PD and FOG displayed a broad spectral range that encompassed several dominant frequencies. SPARC metrics also correlated with all the above-mentioned clinical parameters.

Conclusion

SPARC values provide valid and relevant clinical data about movement quality (e.g., smoothness) of subjects with PD and FOG during a functional mobility test.

Electronic supplementary material

The online version of this article (10.1186/s12984-019-0579-8) contains supplementary material, which is available to authorized users.

The use of smartphone in measuring stance and gait patterns in patients with orthostatic tremor

Orthostatic tremor (OT) is a rare movement disorder characterized by a fast tremor (13–18 Hz) in the lower extremities during stance. Patients with OT typically complain of instability while standing/walking. However, due to the geographical limitation, the standing instability or gait problems in patients with OT cannot be assessed and monitored frequently. The increasing popularity of using smartphone-based accelerometers could be a solution to eliminate this limitation. This study examined the feasibility of using smartphone-based accelerometers to identify the changes in body movement in different standing and locomotor tasks. Twenty patients with OT and seven healthy controls were consented to participate in this study. Subjects stood with eyes open or eyes closed for 20 seconds. They also performed four different locomotor tasks (normal walking, tandem walk, walking on an elevated surface, and obstacle negotiation). When performed different locomotor tasks, patients with OT had a larger acceleration of body movement than controls in the medial-lateral direction (tandem walk: p = 0.026, walking on an elevated surface: p = 0.002, and stepping over the obstacle: p = 0.028). Patients with OT had smaller acceleration of body movement than controls while standing with eyes open in the vertical direction (p = 0.012), in the anterior-posterior direction (p = 0.013) and in the medial-lateral direction (p = 0.011). This study provides objective evidence of balance instability in patients with OT not only while standing but also during different challenging locomotor tasks by using smartphone-based accelerometers.

Which Gait Parameters and Walking Patterns Show the Significant Differences Between Parkinson's Disease and Healthy Participants?

This study investigated the difference in the gait of patients with Parkinson’s disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using inertial measurement units (IMU). Group differences between the mean and variance of the gait parameters (stride interval, stance interval, swing interval and double support interval) for the three groups were calculated and statistical significance was tested. The results showed that the variance in each of the four gait parameters of PD patients was significantly higher compared with the controls, irrespective of the three walking patterns. This study showed that the variance of any of the gait interval parameters obtained using IMU during any of the walking patterns could be used to differentiate between the gait of PD and control people.

Mobility improves after high intensity aerobic exercise in individuals with Parkinson's disease

Emerging literature indicates aerobic exercise improves the motor symptoms associated with Parkinson's disease (PD). However, the impact of aerobic exercise on functional locomotor performance has not been evaluated systematically. The aim of this project was to determine the impact of an 8-week high intensity aerobic exercise intervention on Timed Up and Go (TUG) performance in PD. Fifty-nine participants with idiopathic PD completed 24 aerobic exercise sessions over 8 weeks. Two modes of exercise were utilized: forced (FE) and voluntary (VE). A mobile application was used to gather biomechanical data for the characterization of the TUG subtasks: Sit-Stand, Gait, Turning, and Stand-Sit. Participants were assessed in an off medication state at: 1) baseline, prior to any exercise intervention, and 2) after completion of exercise treatment. At baseline, the VE group completed the TUG in 9.41 s, while the FE group completed the TUG significantly faster in 8.0 s. Following the exercise intervention, the VE group decreased TUG time to 8.9 s (p < .01). Both exercise groups demonstrated significant improvements in Turning Velocity, time of Gait phase and Stand-Sit duration. Overall mobility in participants with PD was significantly improved after high intensity aerobic exercise training. Improvements in turning and gait speed, and in Stand-Sit times indicate exercise is effective in improving functional aspects of mobility that are often associated with falls and quality of life measures. These results support the use of high intensity aerobic exercise for improvements in functional lower extremity performance in a PD population.

K-D Balance: An objective measure of balance in tandem and double leg stances

Background and objective

Subjective grade-based scoring balance assessments tend to be lengthy and have demonstrated poor repeatability and reliability. This study examined the reliability of a mobile balance assessment tool and differences in balance measurements between individuals at risk for a balance deficit secondary to a diagnosed neurological or musculoskeletal condition and a control group of healthy individuals.

Methods

Objective balance testing was measured using K-D Balance on a compatible iPhone. Seventy-seven participants were enrolled (control group, n = 44; group at risk for balance deficits, n = 33). Mean and standard deviation of K-D Balance were recorded for each stance. Intra-rater reliability was calculated by repeating the trial.

Results

Overall balance scores were superior for the control group compared with the group at risk for balance deficits in double leg stance (mean (SD): 0.15 (0.12) versus 0.18 (0.13), p = 0.260), tandem stance right leg (mean (SD): 0.27 (0.17) versus 0.45 (0.49), p = 0.028), and tandem stance left leg (mean (SD): 0.26 (0.17) versus 0.35 (0.35), p = 0.136). Intra-rater reliability was good to excellent for K-D Balance double leg stance (intra-class correlation coefficient (ICC) = 0.80, 95% confidence interval (CI) 0.58–1.03), tandem stance right leg (ICC = 0.96, 95% CI 0.86–1.06) and tandem stance left leg (ICC = 0.98, 95% CI 0.95–1.0).

Conclusions

K-D Balance revealed differences in balance performance between healthy individuals compared with individuals with neurological or musculoskeletal impairment. Objective balance measures may improve the accuracy and reliability of clinical balance assessment by detecting subtle differences in balance and aid in early detection of diseases that impair balance.