Associations of Pain Sensitivity and Conditioned Pain Modulation with Physical Activity: Findings from the Multicenter Osteoarthritis Study (MOST)

OBJECTIVE

Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort.

DESIGN

We used data from the Multicenter Osteoarthritis Study (MOST). Measures of peripheral and central pain sensitivity included pressure pain threshold (PPT) at the knee and mechanical temporal summation (TS) at the wrist, respectively. The presence of descending pain inhibition was assessed by conditioned pain modulation (CPM). Physical activity was quantitatively assessed over 7-days using a lower back-worn activity monitor. Summary metrics included steps/day, activity intensity, and sedentary time. Linear regression analyses were used to evaluate the association of pain sensitivity and the presence of descending pain inhibition with physical activity measures.

RESULTS

Data from 1,873 participants was analyzed (55.9% female, age = 62.8±10.0 years). People having greater peripheral and central sensitivity showed lower step counts. CPM was not significantly related to any of the physical activity measures, and none of the exposures were significantly related to sedentary time.

CONCLUSIONS

In this cohort, greater peripheral and central sensitivity were associated with reduced levels of objectively-assessed daily step counts. Further research may investigate ways to modify or treat heightened pain sensitivity as a means to increase physical activity in older adults with knee OA.

Gait and heart rate: do they measure trait or state physical fatigue in people with multiple sclerosis?

BACKGROUND

Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF.

METHODS

Forty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors.

RESULTS

Both HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF.

DISCUSSION

These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study

BACKGROUND

Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies.

OBJECTIVE

The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors.

METHODS

Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors.

RESULTS

The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids.

CONCLUSIONS

Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health.

TRIAL REGISTRATION

ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2021-050785.

Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study

OBJECTIVE

The objective of this study was to identify gait alterations related to worsening knee pain and worsening physical function, using machine learning approaches applied to wearable sensor-derived data from a large observational cohort.

METHODS

Participants in the Multicenter Osteoarthritis Study (MOST) completed a 20-m walk test wearing inertial sensors on their lower back and ankles. Parameters describing spatiotemporal features of gait were extracted from these data. We used an ensemble machine learning technique ("super learning") to optimally discriminate between those with and without worsening physical function and, separately, those with and without worsening pain over two years. We then used log-binomial regression to evaluate associations of the top 10 influential variables selected with super learning with each outcome. We also assessed whether the relation of altered gait with worsening function was mediated by changes in pain.

RESULTS

Of 2,324 participants, 29% and 24% had worsening knee pain and function over two years, respectively. From the super learner, several gait parameters were found to be influential for worsening pain and for worsening function. After adjusting for confounders, greater gait asymmetry, longer average step length, and lower dominant frequency were associated with worsening pain, and lower cadence was associated with worsening function. Worsening pain partially mediated the association of cadence with function.

CONCLUSION

We identified gait alterations associated with worsening knee pain and those associated with worsening physical function. These alterations could be assessed with wearable sensors in clinical settings. Further research should determine whether they might be therapeutic targets to prevent worsening pain and worsening function.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease

BACKGROUND

Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

OBJECTIVES

The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale.

METHODS

Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time.

RESULTS

Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]).

CONCLUSIONS

Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

Introduction

The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.

Methods

Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.

Results and discussion

The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

A robust walking detection algorithm using a single foot-worn inertial sensor: validation in real-life settings

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

BACKGROUND

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

METHODS

Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.

RESULTS

We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.

CONCLUSIONS

Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Acceptability of wearable devices for measuring mobility remotely: Observations from the Mobilise-D technical validation study

Background

This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health.

Methods

Participants (N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants (n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis.

Results

The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0-20) on a scale from 0-20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0-5.0) on a scale from 1-5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants' as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals.

Conclusions

Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique ("super learning") to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64-1.00) and aOR 0.79 (0.66-0.95), respectively].

Design and validation of a multi-task, multi-context protocol for real-world gait simulation

BACKGROUND

Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety.

METHODS

The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks.

RESULTS

The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life.

CONCLUSIONS

The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy.

TRIAL REGISTRATION

ISRCTN-12246987.

The Potential of Transcranial Alternating Current Stimulation to Alleviate Dual-Task Gait Costs in Older Adults: Insights from a Double-Blinded Pilot Study

BACKGROUND

The performance of an attention-demanding task while walking, i.e., dual-tasking, leads to dual-task costs (e.g., reduced gait speed) in older adults. Previous studies have shown that dual-task costs in gait are associated with future falls and cognitive decline. According to the communication through coherence hypothesis, transcranial alternating current stimulation (tACS) might help alleviate this problem.

OBJECTIVE

The aim of this study was to examine the effects of a single session of theta-tACS targeting the left fronto-parietal network (L-FPN) on dual-task walking and cognitive function compared to sham stimulation and transcranial direct current stimulation (tDCS) targeting the left dorsolateral prefrontal cortex, a node within the L-FPN.

METHODS

Twenty older adults completed a four-visit, double-blinded, within-subject, cross-over study in which usual-walking, dual-task walking, and cognitive function were evaluated before and immediately after 20 min of tACS, tDCS, or sham (order randomized) stimulation. Dual-task costs to gait speed (primary outcome) and other measures were analyzed.

RESULTS

The dual-task cost to gait speed tended to be lower (i.e., better) after tACS (p = 0.067, Cohen's d = 0.433∼small); tDCS significantly reduced this dual-task cost (p = 0.012, Cohen's d = 0.618∼medium), and sham stimulation had no effect (p = 0.467). tACS significantly reduced the dual-task cost to step length (p = 0.037, Cohen's d = 0.502∼medium); a trend was seen after tDCS (p = 0.069, Cohen's d = 0.443∼small). No statistical differences were found for other measures of gait or cognitive function.

CONCLUSIONS

The positive effects of tACS on dual-task gait speed and step length were roughly similar to those seen with tDCS. These results suggest that tACS affects the fronto-parietal network and, similar to tDCS, tACS may improve dual-tasking. Nonetheless, to achieve larger benefits and differentiate the effects of tACS and tDCS on brain function and dual-task walking in older adults, other stimulation montages and protocols should be tested.

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision−recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (&lt;10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Sessions and Systems

Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.

Technical validation of real-world monitoring of gait: a multicentric observational study

INTRODUCTION

Existing mobility endpoints based on functional performance, physical assessments and patient self-reporting are often affected by lack of sensitivity, limiting their utility in clinical practice. Wearable devices including inertial measurement units (IMUs) can overcome these limitations by quantifying digital mobility outcomes (DMOs) both during supervised structured assessments and in real-world conditions. The validity of IMU-based methods in the real-world, however, is still limited in patient populations. Rigorous validation procedures should cover the device metrological verification, the validation of the algorithms for the DMOs computation specifically for the population of interest and in daily life situations, and the users' perspective on the device.

METHODS AND ANALYSIS

This protocol was designed to establish the technical validity and patient acceptability of the approach used to quantify digital mobility in the real world by Mobilise-D, a consortium funded by the European Union (EU) as part of the Innovative Medicine Initiative, aiming at fostering regulatory approval and clinical adoption of DMOs.After defining the procedures for the metrological verification of an IMU-based device, the experimental procedures for the validation of algorithms used to calculate the DMOs are presented. These include laboratory and real-world assessment in 120 participants from five groups: healthy older adults; chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, proximal femoral fracture and congestive heart failure. DMOs extracted from the monitoring device will be compared with those from different reference systems, chosen according to the contexts of observation. Questionnaires and interviews will evaluate the users' perspective on the deployed technology and relevance of the mobility assessment.

ETHICS AND DISSEMINATION

The study has been granted ethics approval by the centre's committees (London-Bloomsbury Research Ethics committee; Helsinki Committee, Tel Aviv Sourasky Medical Centre; Medical Faculties of The University of Tübingen and of the University of Kiel). Data and algorithms will be made publicly available.

TRIAL REGISTRATION NUMBER

ISRCTN (12246987).

Quantification of Daily-Living Gait Quantity and Quality Using a Wrist-Worn Accelerometer in Huntington's Disease

Background: Huntington's disease (HD) leads to altered gait patterns and reduced daily-living physical activity. Accurate measurement of daily-living walking that takes into account involuntary movements (e.g. chorea) is needed. Objective: To evaluate daily-living gait quantity and quality in HD, taking into account irregular movements. Methods: Forty-two individuals with HD and fourteen age-matched non-HD peers completed clinic-based assessments and a standardized laboratory-based circuit of functional activities, wearing inertial measurement units on the wrists, legs, and trunk. These activities were used to train and test an algorithm for the automated detection of walking. Subsequently, 29 HD participants and 22 age-matched non-HD peers wore a tri-axial accelerometer on their non-dominant wrist for 7 days. Measures included gait quantity (e.g., steps per day), gait quality (e.g., regularity) metrics, and percentage of walking bouts with irregular movements. Results: Measures of daily-living gait quantity including step counts, walking time and bouts per day were similar in HD participants and non-HD peers (p > 0.05). HD participants with higher clinician-rated upper body chorea had a greater percentage of walking bouts with irregular movements compared to those with lower chorea (p = 0.060) and non-HD peers (p < 0.001). Even after accounting for irregular movements, within-bout walking consistency was lower in HD participants compared to non-HD peers (p < 0.001), while across-bout variability of these measures was higher (p < 0.001). Many of the daily-living measures were associated with disease-specific measures of motor function. Conclusions: Results suggest that a wrist-worn accelerometer can be used to evaluate the quantity and quality of daily-living gait in people with HD, while accounting for the influence of irregular (choreic-like) movements, and that gait features related to within- and across-bout consistency markedly differ in individuals with HD and non-HD peers.

Automatic Quantification of Tandem Walking Using a Wearable Device: New Insights Into Dynamic Balance and Mobility in Older Adults

BACKGROUND

Wearable sensors are increasingly employed to quantify diverse aspects of mobility. We developed novel tandem walking (TW) metrics, validated these measures using data from community-dwelling older adults, and evaluated their association with mobility disability and measures of gait and postural control.

METHODS

Six hundred ninety-three community-dwelling older adults (age: 78.69 ± 7.12 years) wore a 3D accelerometer on their lower back while performing 3 tasks: TW, usual-walking, and quiet standing. Six new measures of TW were extracted from the sensor data along with the clinician's conventional assessment of TW missteps (ie, trip other loss of balance in which recovery occurred to prevent a fall) and duration. Principal component analysis transformed the 6 new TW measures into 2 summary TW composite factors. Logistic regression models evaluated whether these TW factors were independently associated with mobility disability.

RESULTS

Both TW factors were moderately related to the TW conventional measures (r < 0.454, p < .001) and were mildly correlated with usual-walking (r < 0.195, p < .001) and standing, postural control (r < 0.119, p < .001). The TW frequency composite factor (p = .008), but not TW complexity composite factor (p = .246), was independently associated with mobility disability in a model controlling for age, sex, body mass index, race, conventional measures of TW, and other measures of gait and postural control.

CONCLUSIONS

Sensor-derived TW metrics expand the characterization of gait and postural control and suggest that they reflect a relatively independent domain of mobility. Further work is needed to determine if these metrics improve risk stratification for other adverse outcomes (eg, falls and incident disability) in older adults.

Dopaminergic therapy and prefrontal activation during walking in individuals with Parkinson's disease: does the levodopa overdose hypothesis extend to gait?

The "levodopa-overdose hypothesis" posits that dopaminergic replacement therapy (1) increases performance on tasks that depend on the nigrostriatal-pathway (e.g., motor-control circuits), yet (2) decreases performance on tasks that depend upon the mesocorticolimbic-pathway (e.g., prefrontal cortex, PFC). Previous work in Parkinson's disease (PD) investigated this model while focusing on cognitive function. Here, we evaluated whether this model applies to gait in patients with PD and freezing of gait (FOG). Forty participants were examined in both the OFF anti-Parkinsonian medication state (hypo-dopaminergic) and ON state (hyper-dopaminergic) while walking with and without the concurrent performance of a serial subtraction task. Wireless functional near-infrared spectroscopy measured PFC activation during walking. Consistent with the "overdose-hypothesis", performance on the subtraction task decreased (p = 0.027) after dopamine intake. Moreover, the effect of walking condition on PFC activation depended on the dopaminergic state (i.e., interaction effect p = 0.001). Gait significantly improved after levodopa administration (p < 0.001). Nonetheless, PFC activation was higher (p = 0.013) in this state than in the OFF state during usual-walking. This increase in PFC activation in the ON state suggests that dopamine treatment interfered with PFC functioning. Otherwise, PFC activation, putatively a reflection of cognitive compensation, should have decreased. Moreover, in contrast to the OFF state, in the ON state, PFC activation failed to increase (p = 0.313) during dual-tasking, perhaps due to a "ceiling effect". These findings extend the "levodopa-overdose hypothesis" and suggest that it also applies to gait in PD patients. While dopaminergic therapy improves certain aspects of motor performance, optimal treatment should consider the "double-edged sword" of levodopa.

Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the "ground-truth" for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.

What happens before the first step? A New Approach to Quantifying Gait Initiation Using a Wearable Sensor

BACKGROUND

Walking is a volitional behavior that requires planning and initiation before a step is observed. Following a signal to begin walking, studies of gait initiation in specialized labs have identified three phases that occur during the transition from a standing position via anticipatory postural adjustment (APA) to the first step. Routine instrumented gait testing outside of the laboratory setting focuses on gait execution and does not include gait initiation measures.

RESEARCH QUESTION

Can a single IMU sensor be used for performing gait initiation evaluations outside the lab?

METHODS

We recorded walking in young (N = 41) and older (N = 26) adults using an instrumented gait mat while they were wearing a 3D accelerometer on their lower back. Subjects were instructed to begin walking following an auditory signal. An algorithm was developed to extract the following measures from the acceleration signal: gait initiation time, measured from the start of the auditory cue to begin walking and ends at the heel-strike of the swing leg, time-to-APA (reaction time), APA duration and swing time (execution of the first step).

RESULTS

Intraclass correlation coefficient analysis showed good to excellent agreement between gait initiation metrics obtained with the gait mat and the wearable sensor (mean 0.88, range [0.75-0.96]). Except for swing time, all measures were longer in the older subjects, compared to the young adults (p < 0.01).

SIGNIFICANCE

Extracting gait initiation measures from routine instrumented gait testing may facilitate studies that can better determine the extent to which impaired gait planning and execution contribute to mobility impairments.

AUTOMATIC QUANTIFICATION OF TANDEM WALKING USING A WEARABLE DEVICE: VALIDITY OF THE INSTRUMENTED TANDEM WALK

Tandem walk (TW) is typically assessed by the time to complete the task and the number of missteps, however, these measures suffer from limitations and may not fully capture the range of performance in this task. We developed metrics of TW by using a body-fixed, wearable sensor in young and older adults. Healthy young men (n=40) and older adult men (n=362) were studied. While wearing a 3D accelerometer on their lower back, subjects performed three different tasks: TW, usual-walking, and quiet standing. The extracted measures for TW were: High-to-Low frequency band ratio from the power spectral density from the ML axis [nu], signal vector magnitude[g], step duration[s], sample entropy from ML, AP axis[nu] and CV[%]. All of the TW metrics were significantly different in the young and older men (p<0.001). Older men completed the TW with higher CV, suggesting greater stride-to-stride variability and they walked more slowly, as seen by their step duration. Additionally, the frequency ratio measure suggests that the older adults displayed less complex corrective movements in the ML axis. TW measures were modestly correlated with usual-walking (e.g., average stride time with TW step time, r=0.3; p<0.001) and with quiet standing postural control (e.g., acceleration path length in the ML and AP axis with TW sample entropy in the ML axis, r=0.13; p=0.014). Metrics derived from a wearable device complement conventional TW measures and vary with age. Further work is needed to determine if TW, gait and posture metrics are differentially associated with distinct adverse health outcomes.

A new approach to quantifying the EEG during walking: Initial evidence of gait related potentials and their changes with aging and dual tasking

BACKGROUND

The electroencephalogram (EEG) can be a useful tool to investigate the neurophysiology of gait during walking. Our aims were to develop an approach that identify and quantify event related potentials (ERPs) during a gait cycle and to examine the effects of aging and dual tasking on these gait related potentials (GRPs).

METHODS

10 young and 10 older adults walked on a treadmill while wearing a wireless 20-channels EEG and accelerometers on the ankles. Each heel strike extracted from the accelerometers was used as an event to which the electrical brain activity pattern was locked. The subjects performed usual and dual task walking that included an auditory oddball task. GRPs amplitude and latency were computed, and a new measure referred to as Amplitude Pattern Consistency (APC) was developed to quantify the consistency of these GRP amplitudes within a gait cycle. The results were compared between and within groups using linear mixed model analysis.

RESULTS

The electrical pattern during a gait cycle consisted of two main positive GRPs. Differences in these GRPs between young and older adults were observed in Pz and Cz. In Pz, older adults had higher GRPs amplitude (p = 0.006, p = 0.010), and in Cz lower APC (p = 0.025). Alterations were also observed between the walking tasks. Both groups showed shorter latency during oddball walking compared to usual walking in Cz (p = 0.040). In addition, the APC in Cz was correlated with gait speed (r = 0.599, p = 0.011) in all subjects and with stride time variability in the older adults (r = -0.703, p = 0.023).

CONCLUSIONS

This study is the first to define specific gait related potentials within a gait cycle using novel methods for quantifying waveforms. Our findings show the potential of this approach to be applied broadly to study the EEG during gait in a variety of contexts. The observed changes in GRPs with aging and walking task and the relationship between GRPs and gait may suggest the neurophysiologic foundation for studying walking and for developing new approaches for improving gait.

Using wearables to assess bradykinesia and rigidity in patients with Parkinson's disease: a focused, narrative review of the literature

The potential of using wearable technologies for the objective assessment of motor symptoms in Parkinson's disease (PD) has gained prominence recently. Nonetheless, compared to tremor and gait impairment, less emphasis has been placed on the quantification of bradykinesia and rigidity. This review aimed to consolidate the existing research on objective measurement of bradykinesia and rigidity in PD through the use of wearables, focusing on the continuous monitoring of these two symptoms in free-living environments. A search of PubMed was conducted through a combination of keyword and MeSH searches. We also searched the IEEE, Google Scholar, Embase, and Scopus databases to ensure thorough results and to minimize the chances of missing relevant studies. Papers published after the year 2000 with sample sizes greater than five were included. Studies were assessed for quality and information was extracted regarding the devices used and their location on the body, the setting and duration of the study, the "gold standard" used as a reference for validation, the metrics used, and the results of each paper. Thirty-one and eight studies met the search criteria and evaluated bradykinesia and rigidity, respectively. Several studies reported strong associations between wearable-based measures and the gold-standard references for bradykinesia, and, to a lesser extent, rigidity. Only a few, pilot studies investigated the measurement of bradykinesia and rigidity in the home and free-living settings. While the current results are promising for the future of wearables, additional work is needed on their validation and adaptation in ecological, free-living settings. Doing so has the potential to improve the assessment and treatment of motor fluctuations and symptoms of PD more generally through real-time objective monitoring of bradykinesia and rigidity.

Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24/7 monitoring

Background

The traditional evaluation of gait in the laboratory during structured testing has provided important insights, but is limited by its “snapshot” character and observation in an unnatural environment. Wearables enable monitoring of gait in real-world environments over a week. Initial findings show that in-lab and real-world measures differ. As a step towards better understanding these gaps, we directly compared in-lab usual-walking (UW) and dual-task walking (DTW) to daily-living measures of gait.

Methods

In-lab gait features (e.g., gait speed, step regularity, and stride regularity) derived from UW and DTW were compared to the same gait features during daily-living in 150 elderly fallers (age: 76.5 ± 6.3 years, 37.6% men). In both settings, features were extracted from a lower-back accelerometer. In the real-world setting, subjects were asked to wear the device for 1 week and pre-processing detected 30-s daily-living walking bouts. A histogram of all walking bouts was determined for each walking feature for each subject and then each subject’s typical (percentile 50, median), worst (percentile 10) and the best (percentile 90) values over the week were determined for each feature. Statistics of reliability were assessed using Intra-Class correlations and Bland-Altman plots.

Results

As expected, in-lab gait speed, step regularity, and stride regularity were worse during DTW, compared to UW. In-lab gait speed, step regularity, and stride regularity during UW were significantly higher (i.e., better) than the typical daily-living values (p < 0.001) and different (p < 0.001) from the worst and best values. DTW values tended to be similar to typical daily-living values (p = 0.205, p = 0.053, p = 0.013 respectively). ICC assessment and Bland-Altman plots indicated that in-lab values do not reliably reflect the daily-walking values.

Conclusions

Gait values measured during relatively long (30-s) daily-living walking bouts are more similar to the corresponding values obtained in the lab during dual-task walking, as compared to usual walking. Still, gait performance during most daily-living walking bouts is worse than that measured during usual and dual-tasking in the lab. The values measured in the lab do not reliably reflect daily-living measures. That is, an older adult’s typical daily-living gait cannot be estimated by simply measuring walking in a structured, laboratory setting.

Changes in event-related potentials during dual task walking in aging and Parkinson's disease

OBJECTIVE

To investigate EEG changes during an auditory odd-ball task while walking (dual-task) in young adults, older adults, and patients with Parkinson's disease.

METHODS

11 young adults, 10 older adults, and 10 patients with Parkinson's disease (PD) performed an auditory oddball task during standing and walking on a treadmill, while wearing a wireless EEG cap. The amplitude and latency of P300 were compared between groups and within conditions using linear mix model analysis. Gait was evaluated using wearable sensors and cognition was assessed using the Color Trail Test.

RESULTS

P300 latency became longer during walking in all groups (p = 0.005). During walking, older adults (p = 0.005) and patients with PD (p = 0.001) showed prolonged P300 latency compared to young adults. Significant task by group interaction was found in P300 amplitude (p = 0.008). Patients with PD demonstrated reduced P300 amplitude during walking compared to standing (p = 0.023). Among all subjects, better motor and cognitive performance correlated with shorter P300 latency (r = 0.457, p = 0.014 and r = 0.431, p = 0.040, respectively).

CONCLUSIONS

These findings provide direct evidence of the physiological recruitment of attentional networks during walking and their impact by ageing and disease.

SIGNIFICANCE

This study is the first to report on changes in P300 latency and amplitude during dual-task oddball walking in older adults and patients with PD.

Treadmill walking reduces pre-frontal activation in patients with Parkinson's disease

BACKGROUND

Among patients with Parkinson's disease (PD), gait is typically disturbed and less automatic. These gait changes are associated with impaired rhythmicity and increased prefrontal activation, presumably in an attempt to compensate for reduced automaticity.

RESEARCH QUESTION

We investigated whether during treadmill walking, when the pace is determined and fixed, prefrontal activation in patients with PD is lower, as compared to over-ground walking.

METHODS

Twenty patients with PD (age: 69.8 ± 6.5 yrs.; MoCA: 26.9 ± 2.4; disease duration: 7.9 ± 4.2 yrs) walked at a self-selected walking speed over-ground and on a treadmill. A wireless functional near infrared spectroscopy (fNIRS) system measured prefrontal lobe activation, i.e., oxygenated hemoglobin (Hb02) in the pre-frontal area. Gait was evaluated using 3D-accelerometers attached to the lower back and ankles (Opal™, APDM). Dynamic gait stability was assessed using the maximum Lyapunov exponent to investigate automaticity of the walking pattern.

RESULTS

Hb02 was lower during treadmill walking than during over-ground walking (p = 0.001). Gait stability was greater on the treadmill, compared to over-ground walking, in both the anteroposterior and medio-lateral axes (p < 0.001).

SIGNIFICANCE

These findings support the notion that when gait is externally paced, prefrontal lobe activation is reduced in patients with PD, perhaps reflecting a reduced need for compensatory cognitive mechanisms.

Age-associated changes in obstacle negotiation strategies: Does size and timing matter?

INTRODUCTION

Tripping over an obstacle is one of the most common causes of falls among older adults. However, the effects of aging, obstacle height and anticipation time on negotiation strategies have not been systematically evaluated.

METHODS

Twenty older adults (ages: 77.7±3.4years; 50% women) and twenty young adults (age: 29.3±3.8years; 50% women) walked through an obstacle course while negotiating anticipated and unanticipated obstacles at heights of 25mm and 75mm. Kinect cameras captured the: (1) distance of the subject's trailing foot before the obstacles, (2) distance of the leading foot after the obstacles, (3) clearance of the leading foot above the obstacles, and (4) clearance of the trailing foot above the obstacles. Linear-mix models assessed changes between groups and conditions.

RESULTS

Older adults placed their leading foot closer to the obstacle after landing, compared to young adults (p<0.001). This pattern was enhanced in high obstacles (group*height interaction, p=0.033). Older adults had lower clearance over the obstacles, compared to young adults (p=0.007). This was more pronounced during unanticipated obstacles (group*ART interaction, p=0.003). The distance of the leading foot and clearance of the trailing foot after the obstacles were correlated with motor, cognitive, and functional abilities.

CONCLUSIONS

These findings suggest that there are age-related changes in obstacle crossing strategies that are dependent on the specific characteristics of the obstacle. The results have important implications for clinical practice, suggesting that functional exercise should include obstacle negotiation training with variable practice of height and available response times. Further studies are needed to better understand the effects of motor and cognitive abilities.

Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson's Disease Using Wearable Sensors

Freezing of gait (FOG) is a disabling symptom that is common among patients with advanced Parkinson’s disease (PD). External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens) is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase) is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens). The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.

Triple paternal contribution to a normal/complete molar chimeric singleton placenta

A comprehensive study of unusual cases of placental pathology may provide insight into mechanisms of normal human fertilization and early embryonic development by examining the exception to the rule. A gravida three para two 39-year-old woman was monitored by ultrasound from 16 weeks of gestation for cystic placenta. A female newborn was born at 36 weeks gestation. Pathologic examination of the partially cystic placenta revealed a singleton placenta comprised of 2/3 normal placenta and 1/3 complete hydatidiform mole, largely degenerated. Immunostaining for p57 was negative in stromal cells of the molar villi. Chromogenic in-situ hybridization revealed diploidy in both normal and molar parts. A total of 16 microsatellites were studied by short tandem repeat analysis, 11 of which were informative. The analysis revealed bipaternal molar tissue of dispermic origin. The paternal monospermic contribution to the normal part was different from that in the molar part, thus resulting in tripaternal contribution to the conceptus. A chimera is a single organism composed of two or more different populations of genetically distinct cells that originated from different zygotes (tetragametic) whereas mosaic is a mixture of two cell lines in one organism originating from one zygote. The possible mechanisms leading to the formation of chimeric/mosaic placenta in our case (one of the components being complete hydatidiform mole), including twinning, fusion at an early embryonic stage and diploidization of triploids, are discussed.

A Wearable Assistant for Gait Training for Parkinson’s Disease with Freezing of Gait in Out-of-the-Lab Environments

People with Parkinson’s disease (PD) suffer from declining mobility capabilities, which cause a prevalent risk of falling. Commonly, short periods of motor blocks occur during walking, known as freezing of gait (FoG). To slow the progressive decline of motor abilities, people with PD usually undertake stationary motor-training exercises in the clinics or supervised by physiotherapists. We present a wearable system for the support of people with PD and FoG. The system is designed for independent use. It enables motor training and gait assistance at home and other unsupervised environments. The system consists of three components. First, FoG episodes are detected in real time using wearable inertial sensors and a smartphone as the processing unit. Second, a feedback mechanism triggers a rhythmic auditory signal to the user to alleviate freeze episodes in an assistive mode. Third, the smartphone-based application features support for training exercises. Moreover, the system allows unobtrusive and long-term monitoring of the user’s clinical condition by transmitting sensing data and statistics to a telemedicine service.

We investigate the at-home acceptance of the wearable system in a study with nine PD subjects. Participants deployed and used the system on their own, without any clinical support, at their homes during three protocol sessions in 1 week. Users’ feedback suggests an overall positive attitude toward adopting and using the system in their daily life, indicating that the system supports them in improving their gait. Further, in a data-driven analysis with sensing data from five participants, we study whether there is an observable effect on the gait during use of the system. In three out of five subjects, we observed a decrease in FoG duration distributions over the protocol days during gait-training exercises. Moreover, sensing data-driven analysis shows a decrease in FoG duration and FoG number in four out of five participants when they use the system as a gait-assistive tool during normal daily life activities at home.

Changes in oxygenated hemoglobin link freezing of gait to frontal activation in patients with Parkinson disease: an fNIRS study of transient motor-cognitive failures

Recent studies have suggested that deficits in executive function contribute to freezing of gait (FOG), an episodic disturbance common among patients with Parkinson's disease (PD). To date, most findings provide only indirect evidence of this relationship. Here, we evaluated a more direct link between FOG and frontal lobe dysfunction. Functional, near infrared spectroscopy measured frontal activation, i.e., oxygenated hemoglobin (HbO2) levels in Brodmann area 10 before and during FOG. Eleven patients with PD and eleven healthy older adults were studied. Changes in frontal lobe activation before and during FOG that occurred during turns were determined. Altogether, 49 FOG episodes were observed-28 occurred during turns that were anticipated (i.e., the patient knew in advance that the turn was coming), 21 during unanticipated turns that were performed "abruptly", according to the examiner's request. During anticipated turns, HbO2 increased by 0.22 ± 0.08 µM (p = 0.004) before FOG and by an additional 0.19 ± 0.13 µM (p = 0.072) during FOG. In contrast, during unanticipated turns, HbO2 did not increase before or during FOG. HbO2 decreased by 0.32 ± 0.08 µM (p = 0.004) during turns without FOG; in healthy controls HbO2 did not change during turns. These findings support the existence of an association between FOG episodes and changes in frontal lobe HbO2. Increased activation in Brodmann area 10 before FOG, specifically during anticipated turns, highlights the connections between motor planning, information processing, and FOG. These results support the idea that alterations in executive control play a role in this debilitating motor disturbance.

Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting

BACKGROUND

Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson's disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings.

METHODS

40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings.

RESULTS

In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers.

CONCLUSIONS

These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk.

The RNA polymerase flow model of gene transcription

Gene expression is a fundamental cellular process by which proteins are synthesized based on the information coded in the genes. The two major steps of this process are the transcription of the DNA segment corresponding to a gene to mRNA molecules and the translation of the mRNA molecules to proteins by the ribosome. Thus, understanding, modeling and engineering the different stages of this process have both important biotechnological applications and contributions to basic life science. In previous studies we have introduced the Homogenous Ribosome Flow Model (HRFM) and demonstrated its advantages in analyses of the translation process. In this study we introduce the RNA Polymerase Flow Model (RPFM), a non trivial extension of the HRFM, which also includes a backward flow and can be used for modeling transcription and maybe other similar processes. We compare the HRFM and the RPFM in the three regimes of the transcription process: rate limiting initiation, rate limiting elongation and rate limiting termination via a simulative and analytical analysis. In addition, based on experimental data, we show that RPFM is a better choice for modeling transcription process.

Living related liver transplant following bone marrow transplantation from same donor: long-term survival without immunosuppression

We report long-term (seven yr) immunological tolerance in a 16-yr-old boy, to a liver allograft donated by his father following a bone marrow transplant at age 2.5 yr from the same donor. The bone marrow transplant was complicated by severe GVHD leading to liver failure and the ensuing need for a liver transplant, performed under planned avoidance of immunosuppression. At one wk post-transplant, although a liver biopsy was histologically compatible with acute rejection, favorable clinical and biochemical evolution precluded initiating immunosuppressive therapy, thus highlighting the need for caution when interpreting early histological changes so that administration of unnecessary immunosuppression can be avoided. Induction of tolerance in transplant recipients remains an elusive goal. In those patients who had received conventional bone marrow transplants and had endured the consequences of GVHD, development of macrochimerism may allow immunosuppression-free solid organ transplantation from the same donor.

Patterned arrays of ordered peptide nanostructures

Methods for the deposition of ordered nanostructures on various substrates are a key factor in nanotechnological devices. There is a special interest in the development of methods for the organization of organic nanostructures that are not compatible with some of the conventional fabrication methods. The unique chemical and physical properties of the peptide nanotubes make them excellent component in various devices and the useful application were already demonstrated in the case of biosensors. Here we demonstrate the ability to deposited aromatic dipeptide nanotubes using electron beam treatment of surfaces to control their wettability. The use of a low energy electron irradiation results in the formation of pre-defined surfaces with controlled level of wettability. This treatment allows the precise patterning of the organic tubular assemblies at high resolution. The differential wettability of the surface resulted in organization of the peptide assemblies according to the properties of the different areas of the surface. In the current work, we describe the use of wettability patterned surfaces for the control patterning of horizontal peptide nanotubes and nanospheres. Furthermore, lift-off lithography is used to make patterned arrays of peptide nano-forests, vertically aligned peptide nanotubes. In summary, the novel patterning techniques together with the unique properties of the peptide nanostructures represent an important step in the integration of these assemblies into functional nanosystems and devices.

HLA-G levels in serum and plasma

Soluble human leukocyte antigen (HLA)-G levels are in most cases higher in the plasma than in the serum obtained from the same individual. This is probably due to trapping of the protein during clot formation. In studies where soluble HLA-G is quantified, it is therefore recommended that plasma or serum levels should be compared with the same blood product, namely, serum to serum and plasma to plasma. Because of possible gender differences in HLA-G levels it is also recommended that this should be considered in the construction of a control group especially in studies where there is a preponderance of one of the sexes.

HLA-G levels in serum and plasma

HLA-G belongs to the non-classical HLA class-I family of genes presently designated as class-Ib genes. There are four membrane-bound (HLA-G1 to -G4) and three soluble forms (HLA-G5 to -G7) generated by alternative splicing of the primary transcript. HLA-G in the soluble form is found in the plasma, amniotic fluid, and cord blood of healthy individuals. Quantitative determination suggested that HLA-G levels are genetically controlled. While quantifying soluble HLA-G by ELISA, we observed that when plasma and serum levels were measured for the same individual, HLA-G plasma values were almost invariably higher than those from serum. Our results suggest that HLA-G is trapped and/or consumed during clot formation. The amount trapped within the clot is variable and inconsistent. To obtain values which reflect the true biological levels, it is therefore recommended that HLA-G should be determined in the plasma. If serum levels are determined, they should be compared with matched control sera. It should always be borne in mind that conclusions concerning sera levels might be erroneous, because the true plasma level of the protein can be significantly higher.

Evidence for blood chimerism in dizygotic spontaneous twin pregnancy discordant for Down syndrome

BACKGROUND

A monochorionic-diamniotic placenta (MCDAP) is rare in dizygotic (DZ) twinning. All reported cases have been documented in pregnancies achieved by the induction of ovulation alone or during the IVF cycle.

METHODS AND RESULTS

We report a spontaneous pregnancy in a 39-year-old patient with evidence of MCDAP in DZ twins, discordant for trisomy 21. The first and second-trimester sonographic scans indicated male twins with MCDAP. Amniocentesis, performed because of advanced maternal age, revealed a normal karyotype in one fetus, and trisomy 21 in the other. Molecular studies, performed in order to confirm the zygosity and chorionicity, demonstrated that the fetuses were DZ. In order to identity the affected twin, a detailed sonographic examination was repeated, but no abnormal findings associated with Down syndrome were demonstrated in any of the fetuses. Therefore, umbilical cord blood samples were obtained from both fetuses. Chromosomal analysis revealed in both fetuses two cell lines: a normal cell line of 46,XY and a 47,XY,+ 21 cell line, in 65 and 80% of the cells, respectively. This result was independently confirmed by both FISH and G-banding. DNA extracted from both cord blood samples demonstrated an admixture of two distinct genotypes in each sample.

CONCLUSIONS

We propose that this case represents a monochorionic-dizygotic twin pregnancy with blood chimerism. The most plausible mechanism underlying this phenomenon is placental fusion early in pregnancy, resulting in an architecturally single placenta originating from two distinct zygotes. The newly formed blood vessels created anastomoses between the DZ twins and allowed reciprocal blood chimerism between the normal and the trisomic twin.