Postural Instability and Risk of Falls in Patients with Parkinson's Disease Treated with Deep Brain Stimulation: A Stabilometric Platform Study

Postural instability (PI) in Parkinson's disease (PD) exposes patients to an increased risk of falls (RF). While dopaminergic therapy and deep brain stimulation (DBS) improve motor performance in advanced PD patients, their effects on PI and RF remain elusive. PI and RF were assessed using a stabilometric platform in six advanced PD patients. Patients were evaluated in OFF and ON dopaminergic medication and under four DBS settings: with DBS off, DBS bilateral, and unilateral DBS of the more- or less-affected side. Our findings indicate that dopaminergic medication by itself exacerbated PI and RF, and DBS alone led to a decline in RF. No combination of medication and DBS yielded a superior improvement in postural control compared to the baseline combination of OFF medication and the DBS-off condition. Yet, for ON medication, DBS significantly improved both PI and RF. Among DBS conditions, DBS bilateral provided the most favorable outcomes, improving PI and RF in the ON medication state and presenting the smallest setbacks in the OFF state. Conversely, the more-affected side DBS was less beneficial. These preliminary results could inform therapeutic strategies for advanced PD patients experiencing postural disorders.

Using Wearable Inertial Sensors to Monitor Effectiveness of Different Types of Customized Orthoses during CrossFit® Training

BACKGROUND

Dynamic balance plays a key role in high-impact sports, such as CrossFit, where athletes are required to maintain balance in various weightlifting exercises. The loss of balance in these sport-specific movements may not only affect athlete performance, but also increase the risk of injuries.

OBJECTIVES

The aim of the study is to achieve greater insight into the balance and athlete position during the CrossFit training by means of inertial sensors, with a particular focus on the role of different custom foot orthoses (CFOs) in order to detect correlations with the role of the cavus foot.

METHODS

A total of 42 CrossFit^® athletes, aged 25 to 42 years, were enrolled in this study. One-way ANOVA tests with post-hoc analysis of variance were used to compare foot posture groups and effects of different types of customized foot orthoses.

RESULTS

When comparing the effects of CFOs with the respective balance basal level during the pistol squat exercise, we observed a significant (p = 0.0001) decrease in the sway area, antero-posterior displacement (APD) and medio-lateral displacement (MLD) compared to the basal using both types of CFOs.

CONCLUSION

No significant positive effects of CFOs were observed in some static tests. On the contrary, positive effects of CFOs and, in particular, postural insoles, are relevant to dynamic balance.

A Comparison among Different Strategies to Detect Potential Unstable Behaviors in Postural Sway

Assistive Technology helps to assess the daily living and safety of frail people, with particular regards to the detection and prevention of falls. In this paper, a comparison is provided among different strategies to analyze postural sway, with the aim of detecting unstable postural status in standing condition as precursors of potential falls. Three approaches are considered: (i) a time-based features threshold algorithm, (ii) a time-based features Neuro-Fuzzy inference system, and (iii) a Neuro-Fuzzy inference fed by Discrete-Wavelet-Transform-based features. The analysis was performed across a wide dataset and exploited performance indexes aimed at assessing the accuracy and the reliability of predictions provided by the above-mentioned strategies. The results obtained demonstrate valuable performances of the three considered strategies in correctly distinguishing among stable and unstable postural status. However, the analysis of robustness against noisy data highlights better performance of Neuro-Fuzzy inference systems with respect to the threshold-based algorithm.

Clinical and laboratory measures of balance and comparison of balance performances according to postural instability and gait disorders in individuals with Parkinson's disease

PURPOSE/AIM

Primary aim was to investigate the association between laboratory measures of balance and clinical balance tests in individuals with Parkinson's disease (PD). The secondary aim was to compare the balance performances according to postural instability and gait disorders (PIGD).

MATERIALS AND METHODS

Sixty-four individuals with PD were included in the study. Clinical data were investigated using modified Hoehn and Yahr Scale and Unified Parkinson's Disease Rating Scale (UPDRS). Berg Balance Scale (BBS), Timed Up&Go Test (TUG), Five Times Sit-to-Stand Test (FTSST) were used for clinical measures of balance. Laboratory measures of balance were evaluated by Balance Master System including the modified Clinical Test of Sensory Interaction of Balance (mCTSIB), Limits of Stability Test (LOS), Sit to Stand Test (STS), and Tandem Walk Test (TW). The relationship between clinical and laboratory measures of balance was determined. After participants were divided into two groups based on UPDRS: patients with and without PIGD, their balance performance was compared.

RESULTS

There were significant correlations between BBS and mCTSIB, LOS-Movement Velocity, and LOS-Endpoint Excursion. FTSST was correlated with STS-Weight Transfer and STS-Rising Index, and TUG was correlated with TW-Speed. Patients with PIGD had worse scores of balance assessments including FTSST, LOS-Movement Velocity, STS-Rising Index.

CONCLUSION

Laboratory measures are associated with clinical balance tests and they may reflect clinical balance outcome measures. Furthermore, PIGD may negatively affect balance performance in patients with PD.

Balance Deficits due to Cerebellar Ataxia: A Machine Learning and Cloud-Based Approach

Cerebellar ataxia (CA) refers to the disordered movement that occurs when the cerebellum is injured or affected by disease. It manifests as uncoordinated movement of the limbs, speech, and balance. This study is aimed at the formation of a simple, objective framework for the quantitative assessment of CA based on motion data. We adopted the Recurrence Quantification Analysis concept in identifying features of significance for the diagnosis. Eighty-six subjects were observed undertaking three standard neurological tests (Romberg's, Heel-shin and Truncal ataxia) to capture 213 time series inertial measurements each. The feature selection was based on engaging six different common techniques to distinguish feature subset for diagnosis and severity assessment separately. The Gaussian Naive Bayes classifier performed best in diagnosing CA with an average double cross-validation accuracy, sensitivity, and specificity of 88.24%, 85.89%, and 92.31%, respectively. Regarding severity assessment, the voting regression model exhibited a significant correlation (0.72 Pearson) with the clinical scores in the case of the Romberg's test. The Heel-shin and Truncal tests were considered for diagnosis and assessment of severity concerning subjects who were unable to stand. The underlying approach proposes a reliable, comprehensive framework for the assessment of postural stability due to cerebellar dysfunction using a single inertial measurement unit.

Quantitative clinical assessment of motor function during and following LSVT-BIG® therapy

BACKGROUND

LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson's disease (PD). However, questions remain surrounding the efficacy of LSVT-BIG® when compared with similar exercise therapies. Instrumented clinical tests using body-worn sensors can provide a means to objectively monitor patient progression with therapy by quantifying features of motor function, yet research exploring the feasibility of this approach has been limited to date. The aim of this study was to use accelerometer-instrumented clinical tests to quantify features of gait, balance and fine motor control in individuals with PD, in order to examine motor function during and following LSVT-BIG® therapy.

METHODS

Twelve individuals with PD undergoing LSVT-BIG® therapy, eight non-exercising PD controls and 14 healthy controls were recruited to participate in the study. Functional mobility was examined using features derived from accelerometry recorded during five instrumented clinical tests: 10 m walk, Timed-Up-and-Go, Sit-to-Stand, quiet stance, and finger tapping. PD subjects undergoing therapy were assessed before, each week during, and up to 13 weeks following LSVT-BIG®.

RESULTS

Accelerometry data captured significant improvements in 10 m walk and Timed-Up-and-Go times with LSVT-BIG® (p <  0.001), accompanied by increased stride length. Temporal features of the gait cycle were significantly lower following therapy, though no change was observed with measures of asymmetry or stride variance. The total number of Sit-to-Stand transitions significantly increased with LSVT-BIG® (p <  0.001), corresponding to a significant reduction of time spent in each phase of the Sit-to-Stand cycle. No change in measures related to postural or fine motor control was observed with LSVT-BIG®. PD subjects undergoing LSVT-BIG® showed significant improvements in 10 m walk (p <  0.001) and Timed-Up-and-Go times (p = 0.004) over a four-week period when compared to non-exercising PD controls, who showed no week-to-week improvement in any task examined.

CONCLUSIONS

This study demonstrates the potential for wearable sensors to objectively quantify changes in motor function in response to therapeutic exercise interventions in PD. The observed improvements in accelerometer-derived features provide support for instrumenting gait and sit-to-stand tasks, and demonstrate a rescaling of the speed-amplitude relationship during gait in PD following LSVT-BIG®.

Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen's kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., "active aging", biofeedback-based rehabilitation for fall prevention, and the management of Parkinson's disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control

Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.

Postural control after unexpected external perturbation: Effects of Parkinson's disease subtype

Different clinical subtypes of Parkinson's disease (PD) have long been recognized. Recent studies have focused on two PD subtypes: Postural Instability and Gait Difficulty (PIGD) and Tremor Dominant (TD). PIGD patients have greater difficulties in postural control in relation to TD. However, knowledge about the differences in reactive adjustment mechanisms following a perturbation in TD and PIGD is limited. This study aimed to compare reactive postural adjustments under unexpected external perturbation in TD, PIGD, and control group (CG) subjects. Forty-five individuals (15 TD, 15 PIGD, and 15 CG) participated in this study. Postural perturbation was applied by the posterior displacement of the support surface in an unexpected condition. The velocity (15 cm/s) and displacement (5 cm/s) of perturbation were the same for all participants. Center of pressure (CoP) and center of mass (CoM) were analyzed for two reactive windows after the perturbation (0-200 ms and 200-700 ms). The Bonferroni post hoc test indicated a higher range of CoP in the PIGD when compared to the CG (p = 0.021). The PIGD demonstrated greater time to recover the stable posture compared to the TD (p = 0.017) and CG (p = 0.003). Furthermore, the TD showed higher AP-acceleration peak of CoM when compared to the PIGD (p = 0.048) and CG (p = 0.013), and greater AP-acceleration range of CoM in relation to the CG (p = 0.022). These findings suggest that PD patients present worse reactive postural control after perturbation compared to healthy older individuals. CoP and CoM parameters are sensitive to understand and detect the differences in reactive postural mechanisms in PD subtypes.

From A to Z: Wearable technology explained

Wearable technology (WT) has become a viable means to provide low-cost clinically sensitive data for more informed patient assessment. The benefit of WT seems obvious: small, worn discreetly in any environment, personalised data and possible integration into communication networks, facilitating remote monitoring. Yet, WT remains poorly understood and technology innovation often exceeds pragmatic clinical demand and use. Here, we provide an overview of the common challenges facing WT if it is to transition from novel gadget to an efficient, valid and reliable clinical tool for modern medicine. For simplicity, an A-Z guide is presented, focusing on key terms, aiming to provide a grounded and broad understanding of current WT developments in healthcare.

Predictive ability of functional tests for postural instability and gait difficulty in Parkinson's disease

The objective of this study is to identify clinical determinants for postural instability and gait difficulty in persons with Parkinson's disease (PD). Ninety-one persons (68 males; 74.7%) with PD were studied. Their mean age was 68.73 ± 8.74 years. The average time since diagnosis was 7.69 ± 5.23 years. The average Hoehn and Yahr stage was 2.43 ± 0.44. Age, gender, disease duration, disease severity and motor impairment were recorded. Participants were asked to perform timed clinical mobility tests that included a 5-step test, turns, forward walk, backward walk, and a sideways walk. The mobility tests were investigated for their contribution to predict the postural instability and gait difficulty (PIGD) score (falling, freezing, walking, gait and postural stability) of the Unified Parkinson Disease Rating Scale (UPDRS). PIGD score was significantly correlated with age, disease duration, Hoehn and Yahr score, comorbidity, UPDRS motor score, gait speed of forward, backward and sideways walks, and time to turn. PIGD score was marginally significantly correlated with timed 5-step test. After controlling for age, disease duration, disease severity, comorbidity, and motor impairment, sideway gait speed (β = - 0.335; p = 0.024), timed 5-step test (β = - 0.397; p = 0.003) and time to turn (β = 0.289; p = 0.028) significantly predicted postural instability and gait difficulty. Walking sideways, 5-step test, and turning are significant predictors of PIGD score. These simple mobility tests can be quickly applied in clinical practice to determine postural instability and gait problems in persons with PD.

How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology.

OBJECTIVES

This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring).

DATA SOURCES

The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016.

STUDY ELIGIBILITY CRITERIA

Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers.

RESULTS

Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

Multivariate Analyses and Classification of Inertial Sensor Data to Identify Aging Effects on the Timed-Up-and-Go Test

Many tests can crudely quantify age-related mobility decrease but instrumented versions of mobility tests could increase their specificity and sensitivity. The Timed-up-and-Go (TUG) test includes several elements that people use in daily life. The test has different transition phases: rise from a chair, walk, 180° turn, walk back, turn, and sit-down on a chair. For this reason the TUG is an often used test to evaluate in a standardized way possible decline in balance and walking ability due to age and or pathology. Using inertial sensors, qualitative information about the performance of the sub-phases can provide more specific information about a decline in balance and walking ability. The first aim of our study was to identify variables extracted from the instrumented timed-up-and-go (iTUG) that most effectively distinguished performance differences across age (age 18-75). Second, we determined the discriminative ability of those identified variables to classify a younger (age 18-45) and older age group (age 46-75). From healthy adults (n = 59), trunk accelerations and angular velocities were recorded during iTUG performance. iTUG phases were detected with wavelet-analysis. Using a Partial Least Square (PLS) model, from the 72-iTUG variables calculated across phases, those that explained most of the covariance between variables and age were extracted. Subsequently, a PLS-discriminant analysis (DA) assessed classification power of the identified iTUG variables to discriminate the age groups. 27 variables, related to turning, walking and the stand-to-sit movement explained 71% of the variation in age. The PLS-DA with these 27 variables showed a sensitivity and specificity of 90% and 85%. Based on this model, the iTUG can accurately distinguish young and older adults. Such data can serve as a reference for pathological aging with respect to a widely used mobility test. Mobility tests like the TUG supplemented with smart technology could be used in clinical practice.

Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor

Body-fixed sensors (BFS), e.g., accelerometers worn for several days, can be used to augment the traditional clinical assessment. Long-term recordings obtained with BFS have been applied to study tremor, postural control, freezing of gait, turning abilities, motor response fluctuations and fall risk among older adults and patients with Parkinson's disease (PD). We aimed to test whether BFS-derived measures of transitions differ between patients with PD and healthy controls, and to evaluate whether there are differences among patients with mild PD, compared to more severe patients, and to controls. We also explored the added value of the metrics extracted from the sensor as compared to traditional testing in the lab. Ninety-nine patients with PD and 38 healthy older adults (HOA) participated in this study and wore a body-fixed sensor for 3 days. Walk-to-sit (n = 3286) and Sit-to-walk (n = 2858) transitions were analyzed and a machine learning algorithm was applied to distinguish between the groups. Significant differences in transitions were observed between PD patients and HOA, between mild and severe PD, and between mild PD and HOA, both in temporal and distribution features. The machine learning algorithm discriminated patients from HOA (accuracy = 92.3 %), mild from severe patients (accuracy = 89.8 %), and mild patients from HOA (accuracy = 85.9 %). These initial results suggest that body-fixed sensor-derived metrics of everyday transitions can characterize disease severity and differentiate mild PD patients from healthy older adults. Perhaps this approach can help with the integration of BFS into clinical care and the tracking of disease progression and the response to therapy.

Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications

Parkinson's disease (PD) is a common neurodegenerative disorder with high morbidity because of the coming aged society. Currently, disease management and the development of new treatment strategies mainly depend on the clinical information derived from rating scales and patients' diaries, which have various limitations with regard to validity, inter-rater variability and continuous monitoring. Recently the prevalence of mobile medical equipment has made it possible to develop an objective, accurate, remote monitoring system for motor function assessment, playing an important role in disease diagnosis, home-monitoring, and severity evaluation. This review discusses the recent development in sensor technology, which may be a promising replacement of the current rating scales in the assessment of motor function of PD.

Toward precision medicine in Parkinson's disease

Precision medicine refers to an innovative approach selected for disease prevention and health promotion according to the individual characteristics of each patient. The goal of precision medicine is to formulate prevention and treatment strategies based on each individual with novel physiological and pathological insights into a certain disease. A multidimensional data-driven approach is about to upgrade "precision medicine" to a higher level of greater individualization in healthcare, a shift towards the treatment of individual patients rather than treating a certain disease including Parkinson's disease (PD). As one of the most common neurodegenerative diseases, PD is a lifelong chronic disease with clinical and pathophysiologic complexity, currently it is treatable but neither preventable nor curable. At its advanced stage, PD is associated with devastating chronic complications including both motor dysfunction and non-motor symptoms which impose an immense burden on the life quality of patients. Advances in computational approaches provide opportunity to establish the patient's personalized disease data at the multidimensional levels, which finally meeting the need for the current concept of precision medicine via achieving the minimal side effects and maximal benefits individually. Hence, in this review, we focus on highlighting the perspectives of precision medicine in PD based on multi-dimensional information about OMICS, molecular imaging, deep brain stimulation (DBS) and wearable sensors. Precision medicine in PD is expected to integrate the best evidence-based knowledge to individualize optimal management in future health care for those with PD.

Wearable sensor use for assessing standing balance and walking stability in people with Parkinson's disease: a systematic review

Background

Postural instability and gait disability threaten the independence and well-being of people with Parkinson’s disease and increase the risk of falls and fall-related injuries. Prospective research has shown that commonly-used clinical assessments of balance and walking lack the sensitivity to accurately and consistently identify those people with Parkinson’s disease who are at a higher risk of falling. Wearable sensors provide a portable and affordable alternative for researchers and clinicians who are seeking to objectively assess movements and falls risk in the clinical setting. However, no consensus currently exists on the optimal placements for sensors and the best outcome measures to use for assessing standing balance and walking stability in Parkinson’s disease patients. Hence, this systematic review aimed to examine the available literature to establish the best sensor types, locations and outcomes to assess standing balance and walking stability in this population.

Methods

Papers listed in three electronic databases were searched by title and abstract to identify articles measuring standing balance or walking stability with any kind of wearable sensor among adults diagnosed with PD. To be eligible for inclusion, papers were required to be full-text articles published in English between January 1994 and December 2014 that assessed measures of standing balance or walking stability with wearable sensors in people with PD. Articles were excluded if they; i) did not use any form of wearable sensor to measure variables associated with standing balance or walking stability; ii) did not include a control group or control condition; iii) were an abstract and/or included in the proceedings of a conference; or iv) were a review article or case study. The targeted search of the three electronic databases identified 340 articles that were potentially eligible for inclusion, but following title, abstract and full-text review only 26 articles were deemed to meet the inclusion criteria. Included articles were assessed for methodological quality and relevant data from the papers were extracted and synthesized.

Results

Quality assessment of these included articles indicated that 31% were of low methodological quality, while 58% were of moderate methodological quality and 11% were of high methodological quality. All studies adopted a cross-sectional design and used a variety of sensor types and outcome measures to assess standing balance or walking stability in people with Parkinson’s disease. Despite the typically low to moderate methodological quality, 81% of the studies reported differences in sensor-based measures of standing balance or walking stability between different groups of Parkinson’s disease patients and/or healthy controls.

Conclusion

These data support the use of wearable sensors for detecting differences in standing balance and walking stability between people with PD and controls. Further high-quality research is needed to better understand the utility of wearable sensors for the early identification of Parkinson’s disease symptoms and for assessing falls risk in this population.

PROSPERO Registration

CRD42014010838