Analysis of Free-Living Gait in Older Adults With and Without Parkinson's Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics

Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study

BACKGROUND

Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study.

METHODS

Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning.

RESULTS

Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis.

CONCLUSIONS

Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.

Characterizing Walking Behaviors in Aged Residential Care Using Accelerometry, With Comparison Across Care Levels, Cognitive Status, and Physical Function: Cross-Sectional Study

Background

Walking is important for maintaining physical and mental well-being in aged residential care (ARC). Walking behaviors are not well characterized in ARC due to inconsistencies in assessment methods and metrics as well as limited research regarding the impact of care environment, cognition, or physical function on these behaviors. It is recommended that walking behaviors in ARC are assessed using validated digital methods that can capture low volumes of walking activity.

Objective

This study aims to characterize and compare accelerometry-derived walking behaviors in ARC residents across different care levels, cognitive abilities, and physical capacities.

Methods

A total of 306 ARC residents were recruited from the Staying UpRight randomized controlled trial from 3 care levels: rest home (n=164), hospital (n=117), and dementia care (n=25). Participants' cognitive status was classified as mild (n=87), moderate (n=128), or severe impairment (n=61); physical function was classified as high-moderate (n=74) and low-very low (n=222) using the Montreal Cognitive Assessment and the Short Physical Performance Battery cutoff scores, respectively. To assess walking, participants wore an accelerometer (Axivity AX3; dimensions: 23×32.5×7.6 mm; weight: 11 g; sampling rate: 100 Hz; range: ±8 g; and memory: 512 MB) on their lower back for 7 days. Outcomes included volume (ie, daily time spent walking, steps, and bouts), pattern (ie, mean walking bout duration and alpha), and variability (of bout length) of walking. Analysis of covariance was used to assess differences in walking behaviors between groups categorized by level of care, cognition, or physical function while controlling for age and sex. Tukey honest significant difference tests for multiple comparisons were used to determine where significant differences occurred. The effect sizes of group differences were calculated using Hedges g (0.2-0.4: small, 0.5-0.7: medium, and 0.8: large).

Results

Dementia care residents showed greater volumes of walking (P<.001; Hedges g=1.0-2.0), with longer (P<.001; Hedges g=0.7-0.8), more variable (P=.008 vs hospital; P<.001 vs rest home; Hedges g=0.6-0.9) bouts compared to other care levels with a lower alpha score (vs hospital: P<.001; Hedges g=0.9, vs rest home: P=.004; Hedges g=0.8). Residents with severe cognitive impairment took longer (P<.001; Hedges g=0.5-0.6), more variable (P<.001; Hedges g=0.4-0.6) bouts, compared to those with mild and moderate cognitive impairment. Residents with low-very low physical function had lower walking volumes (total walk time and bouts per day: P<.001; steps per day: P=.005; Hedges g=0.4-0.5) and higher variability (P=.04; Hedges g=0.2) compared to those with high-moderate capacity.

Conclusions

ARC residents across different levels of care, cognition, and physical function demonstrate different walking behaviors. However, ARC residents often present with varying levels of both cognitive and physical abilities, reflecting their complex multimorbid nature, which should be considered in further work. This work has demonstrated the importance of considering a nuanced framework of digital outcomes relating to volume, pattern, and variability of walking behaviors among ARC residents.

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

A study protocol to investigate if acipimox improves muscle function and sarcopenia: an open-label, uncontrolled, before-and-after experimental medicine feasibility study in community-dwelling older adults

INTRODUCTION

Sarcopenia is the age-associated loss of muscle mass and strength. Nicotinamide adenine dinucleotide (NAD) plays a central role in both mitochondrial function and cellular ageing processes implicated in sarcopenia. NAD concentrations are low in older people with sarcopenia, and increasing skeletal muscle NAD concentrations may offer a novel therapy for this condition. Acipimox is a licensed lipid-lowering agent known to act as an NAD precursor. This open-label, uncontrolled, before-and-after proof-of-concept experimental medicine study will test whether daily supplementation with acipimox improves skeletal muscle NAD concentrations.

METHODS AND ANALYSIS

Sixteen participants aged 65 and over with probable sarcopenia will receive acipimox 250 mg and aspirin 75 mg orally daily for 4 weeks, with the frequency of acipimox administration being dependent on renal function. Muscle biopsy of the vastus lateralis and MRI scanning of the lower leg will be performed at baseline before starting acipimox and after 3 weeks of treatment. Adverse events will be recorded for the duration of the trial. The primary outcome, analysed in a per-protocol population, is the change in skeletal muscle NAD concentration between baseline and follow-up. Secondary outcomes include changes in phosphocreatine recovery rate by 31P magnetic resonance spectroscopy, changes in physical performance and daily activity (handgrip strength, 4 m walk and 7-day accelerometry), changes in skeletal muscle mitochondrial respiratory function, changes in skeletal muscle mitochondrial DNA copy number and changes in NAD concentrations in whole blood as a putative biomarker for future participant selection.

ETHICS AND DISSEMINATION

The trial is approved by the UK Medicines and Healthcare Products Regulatory Agency (EuDRACT 2021-000993-28) and UK Health Research Authority and Northeast - Tyne and Wear South Research Ethics Committee (IRAS 293565). Results will be made available to participants, their families, patients with sarcopenia, the public, regional and national clinical teams, and the international scientific community.

PROTOCOL

Acipimox feasibility study Clinical Trial Protocol V.2 2/11/21.

TRIAL REGISTRATION NUMBER

The ISRCTN trial database (ISRCTN87404878).

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.

Effects of an Exercise Program to Reduce Falls in Older People Living in Long-Term Care: A Randomized Controlled Trial

OBJECTIVES

To investigate the effect of an exercise program on falls in intermediate and high-level long-term care (LTC) residents and to determine whether adherence, physical capacity, and cognition modified outcomes.

DESIGN

Randomized controlled trial.

SETTING AND PARTICIPANTS

Residents (n = 520, aged 84 ± 8 years) from 25 LTC facilities in New Zealand.

METHODS

Individually randomized to Staying UpRight, a physical therapist-led, balance and strength group exercise program delivered for 1 hour, twice weekly over 12 months. The control arm was dose-matched and used seated activities with no resistance. Falls were collected using routinely collected incident reports.

RESULTS

Baseline fall rates were 4.1 and 3.3 falls per person-year (ppy) for intervention and control groups. Fall rates over the trial period were 4.1 and 4.3 falls ppy respectively [P = .89, incidence rate ratio (IRR) 0.98, 95% CI 0.76, 1.27]. Over the 12-month trial period, 74% fell, with 63% of intervention and 61% of the control group falling more than once. Risk of falls (P = .56, hazard ratio 1.08, 95% CI 0.85, 1.36) and repeat falling or fallers sustaining an injury at trial completion were similar between groups. Fall rates per 100 hours walked did not differ between groups (P = .42, IRR 1.15, 95% CI 0.81, 1.63). Program delivery was suspended several times because of COVID-19, reducing average attendance to 26 hours over 12 months. Subgroup analyses of falls outcomes for those with the highest attendance (≥50% of classes), better physical capacity (Short Physical Performance Battery scores ≥8/12), or cognition (Montreal Cognitive Assessment scores ≥ 18/30) showed no significant impact of the program.

CONCLUSIONS/IMPLICATIONS

In intermediate and high-level care residents, the Staying UpRight program did not reduce fall rates or risk compared with a control activity, independent of age, sex, or care level. Inadequate exercise dose because of COVID-19-related interruptions to intervention delivery likely contributed to the null result.

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.

Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review

INTRODUCTION

Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients.

METHODS

For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed.

RESULTS

All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation.

CONCLUSIONS

Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure.

PROSPERO REGISTRATION

CRD42022355460.

Validation of an algorithm to assess regular and irregular gait using inertial sensors in healthy and stroke individuals

Background

Studies using inertial measurement units (IMUs) for gait assessment have shown promising results regarding accuracy of gait event detection and spatiotemporal parameters. However, performance of such algorithms is challenged in irregular walking patterns, such as in individuals with gait deficits. Based on the literature, we developed an algorithm to detect initial contact (IC) and terminal contact (TC) and calculate spatiotemporal gait parameters. We evaluated the validity of this algorithm for regular and irregular gait patterns against a 3D optical motion capture system (OMCS).

Methods

Twenty healthy participants (aged 59 ± 12 years) and 10 people in the chronic phase after stroke (aged 61 ± 11 years) were equipped with 4 IMUs: on both feet, sternum and lower back (MTw Awinda, Xsens) and 26 reflective makers. Participants walked on an instrumented treadmill for 2 minutes (i) with their preferred stride lengths and (ii) once with irregular stride lengths (±20% deviation) induced by light projected stepping stones. Accuracy of the algorithm was evaluated on stride-by-stride agreement of IC, TC, stride time, length and velocity with OMCS. Bland-Altman-like plots were made for the spatiotemporal parameters, while differences in detection of IC and TC time instances were shown in histogram plots. Performance of the algorithm was compared between regular and irregular gait with a linear mixed model. This was done by comparing the performance in healthy participants in the regular vs irregular walking condition, and by comparing the agreement in healthy participants with stroke participants in the regular walking condition.

Results

For each condition at least 1,500 strides were included for analysis. Compared to OMCS, IMU-based IC detection in both groups and condition was on average 9-17 (SD ranging from 7 to 35) ms, while IMU-based TC was on average 15-24 (SD ranging from 12 to 35) ms earlier. When comparing regular and irregular gait in healthy participants, the difference between methods was 2.5 ms higher for IC, 3.4 ms lower for TC, 0.3 cm lower for stride length, and 0.4 cm/s higher for stride velocity in the irregular walking condition. No difference was found on stride time. When comparing the differences between methods between healthy and stroke participants, the difference between methods was 7.6 ms lower for IC, 3.8 cm lower for stride length, and 3.4 cm/s lower for stride velocity in stroke participants. No differences were found on differences between methods on TC detection and stride time between stroke and healthy participants.

Conclusions

Small irrelevant differences were found on gait event detection and spatiotemporal parameters due to irregular walking by imposing irregular stride lengths or pathological (stroke) gait. Furthermore, IMUs seem equally good compared to OMCS to assess gait variability based on stride time, but less accurate based on stride length.

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Background

In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.

Objective

Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls.

Methods

A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric.

Results

The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent).

Conclusions

These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices.

Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

Transforming Parkinson's Care in Africa (TraPCAf): protocol for a multimethodology National Institute for Health and Care Research Global Health Research Group project

BACKGROUND

Parkinson's disease (PD) is the second most common neurodegenerative disorder and, according to the Global Burden of Disease estimates in 2015, was the fastest growing neurological disorder globally with respect to associated prevalence, disability, and deaths. Information regarding the awareness, diagnosis, phenotypic characteristics, epidemiology, prevalence, risk factors, treatment, economic impact and lived experiences of people with PD from the African perspective is relatively sparse in contrast to the developed world, and much remains to be learned from, and about, the continent.

METHODS

Transforming Parkinson's Care in Africa (TraPCAf) is a multi-faceted, mixed-methods, multi-national research grant. The study design includes multiple sub-studies, combining observational (qualitative and quantitative) approaches for the epidemiological, clinical, risk factor and lived experience components, as appropriate, and interventional methods (clinical trial component). The aim of TraPCAf is to describe and gain a better understanding of the current situation of PD in Africa. The countries included in this National Institute for Health and Care Research (NIHR) Global Health Research Group (Egypt, Ethiopia, Ghana, Kenya, Nigeria, South Africa and Tanzania) represent diverse African geographies and genetic profiles, with differing resources, healthcare systems, health and social protection schemes, and policies. The research team is composed of experts in the field with vast experience in PD, jointly led by a UK-based and Africa-based investigator.

DISCUSSION

Despite the increasing prevalence of PD globally, robust data on the disease from Africa are lacking. Existing data point towards the poor awareness of PD and other neurological disorders on the continent and subsequent challenges with stigma, and limited access to affordable services and medication. This multi-site study will be the first of its kind in Africa. The data collected across the proposed sub-studies will provide novel and conclusive insights into the situation of PD. The selected country sites will allow for useful comparisons and make results relevant to other low- and middle-income countries. This grant is timely, as global recognition of PD and the public health challenge it poses builds. The work will contribute to broader initiatives, including the World Health Organization's Intersectoral global action plan on epilepsy and other neurological disorders.

TRIAL REGISTRATION

https://doi.org/10.1186/ISRCTN77014546 .

"Fall Risk Scoring" in Outpatient Gait Analysis: Validation of a New Fall Risk Assessment for Nursing Home Residents

Falls in senior home residents are common. Individual preventive training can lower the fall risk. To detect the need for training, a systematic assessment of the individual fall risk is needed. The aim of this study was thus to assess whether a fall risk score based on free field insole measurements can distinguish between an at-risk group of senior home residents and a healthy young control group. A published fall risk score was used in senior home residents over the age of 75 and a young (< 40 years) control group to determine the individual fall risk. In addition, the fall events over 12 months were assessed. Statistical analysis including ROC analysis was performed to determine the ability of the score to detect participants at heightened fall risk. In total, 18 nursing home residents and 9 young control participants were included. Of the nursing home residents, 15 had at least one fall, with a total of 37 falls recorded over 12 months. In the control group, no falls were recorded. The fall risk score was significantly different between nursing home residents and the control group (9.2 + 3.2 vs. 5.7 ± 2.2). Furthermore, the score significantly differentiated fallers from non-fallers (10.3 ± 1.8 vs. 5.2 ± 2.5), with a cut-off > 7.5 (AUC: 0.95) and a sensitivity of 86.7% (specificity 83.3%). The fall risk score is able to detect the difference between senior nursing home residents and young, healthy controls, as well as between fallers and non-fallers. Its main proof of concept is demonstrated, as based on movement data outside special gait labs, and it can simplify the risk of fall determination in geriatric nursing home residents and can now be used in further, prospective studies.

Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's

INTRODUCTION

In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility. However, this is typically assessed in controlled environments, during one-off clinical assessments. Digital health technology (DHT) presents a means to overcome this, by continuously and remotely monitoring mobility and medication adherence. This study aims to use a novel DHT system (DHTS) (comprising of a smartphone, smartwatch and inertial measurement unit (IMU)) to assess self-reported medication adherence, and its impact on digital mobility outcomes (DMOs) in PwP.

METHODS AND ANALYSIS

This single-centre, UK-based study, will recruit 55 participants with Parkinson's. Participants will complete a range of clinical, and physical assessments. Participants will interact with a DHTS over 7 days, to assess self-reported medication adherence, and monitor mobility and contextual factors in the real world. Participants will complete a motor complications diary (ON-OFF-Dyskinesia) throughout the monitoring period and, at the end, a questionnaire and series of open-text questions to evaluate DHTS usability. Feasibility of the DHTS and the motor complications diary will be assessed. Validated algorithms will quantify DMOs from IMU walking activity. Time series modelling and deep learning techniques will model and predict DMO response to medication and effects of contextual factors. This study will provide essential insights into medication adherence and its effect on real-world mobility in PwP, providing insights to optimise medication regimens.

ETHICS AND DISSEMINATION

Ethical approval was granted by London-142 Westminster Research Ethics Committee (REC: 21/PR/0469), protocol V.2.4. Results will be published in peer-reviewed journals. All participants will provide written, informed consent.

TRIAL REGISTRATION NUMBER

ISRCTN13156149.

Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit

Cueing and feedback training can be effective in maintaining or improving gait in individuals with Parkinson's disease. We previously designed a rehabilitation assist device that can detect and classify a user's gait at only the swing phase of the gait cycle, for the ease of data processing. In this study, we analyzed the impact of various factors in a gait detection algorithm on the gait detection and classification rate (GDCR). We collected acceleration and angular velocity data from 25 participants (1 male and 24 females with an average age of 62 ± 6 years) using our device and analyzed the data using statistical methods. Based on these results, we developed an adaptive GDCR control algorithm using several equations and functions. We tested the algorithm under various virtual exercise scenarios using two control methods, based on acceleration and angular velocity, and found that the acceleration threshold was more effective in controlling the GDCR (average Spearman correlation -0.9996, p < 0.001) than the gyroscopic threshold. Our adaptive control algorithm was more effective in maintaining the target GDCR than the other algorithms (p < 0.001) with an average error of 0.10, while other tested methods showed average errors of 0.16 and 0.28. This algorithm has good scalability and can be adapted for future gait detection and classification applications.

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.

IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management. This study investigates the feasibility and usability of a multi-component DHTS to remotely assess self-reported medication adherence and monitor mobility in people with Parkinson's (PwP).

Methods

Thirty participants with PD [Hoehn and Yahr stage I (n = 1) and II (n = 29)] were recruited for this cross-sectional study. Participants were required to wear, and where appropriate, interact with a DHTS (smartwatch, inertial measurement unit, and smartphone) for seven consecutive days to assess medication adherence and monitor digital mobility outcomes and contextual factors. Participants reported their daily motor complications [motor fluctuations and dyskinesias (i.e., involuntary movements)] in a diary. Following the monitoring period, participants completed a questionnaire to gauge the usability of the DHTS. Feasibility was assessed through the percentage of data collected, and usability through analysis of qualitative questionnaire feedback.

Results

Adherence to each device exceeded 70% and ranged from 73 to 97%. Overall, the DHTS was well tolerated with 17/30 participants giving a score &gt; 75% [average score for these participants = 89%, from 0 (worst) to 100 (best)] for its usability. Usability of the DHTS was significantly associated with age (ρ = −0.560, BCa 95% CI [−0.791, −0.207]). This study identified means to improve usability of the DHTS by addressing technical and design issues of the smartwatch. Feasibility, usability and acceptability were identified as key themes from PwP qualitative feedback on the DHTS.

Conclusion

This study highlighted the feasibility and usability of our integrated DHTS to remotely assess medication adherence and monitor mobility in people with mild-to-moderate Parkinson's disease. Further work is necessary to determine whether this DHTS can be implemented for clinical decision-making to optimise management of PwP.

Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease

Objectives

To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.

Methods

We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.

Results

Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).

Conclusions

These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.

Multidisciplinary Intensive Rehabilitation Program for People with Parkinson's Disease: Gaps between the Clinic and Real-World Mobility

Intensive rehabilitation programs improve motor and non-motor symptoms in people with Parkinson's disease (PD), however, it is not known whether transfer to daily-living walking occurs. The effects of multidisciplinary-intensive-outpatient rehabilitation (MIOR) on gait and balance in the clinic and on everyday walking were examined. Forty-six (46) people with PD were evaluated before and after the intensive program. A 3D accelerometer placed on the lower back measured daily-living walking during the week before and after the intervention. Participants were also stratified into "responders" and "non-responders" based on daily-living-step-counts. After the intervention, gait and balance significantly improved, e.g., MiniBest scores (p < 0.001), dual-task gait speed increased (p = 0.016) and 6-minute walk distance increased (p < 0.001). Many improvements persisted after 3 months. In contrast, daily-living number of steps and gait quality features did not change in response to the intervention (p > 0.1). Only among the "responders", a significant increase in daily-living number of steps was found (p < 0.001). These findings demonstrate that in people with PD improvements in the clinic do not necessarily carry over to daily-living walking. In a select group of people with PD, it is possible to ameliorate daily-living walking quality, potentially also reducing fall risk. Nevertheless, we speculate that self-management in people with PD is relatively poor; therefore, to maintain health and everyday walking abilities, actions such as long-term engaging in physical activity and preserving mobility may be needed.

Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease

Background

It is unknown whether medication status (off and on levodopa) or laboratory versus home settings plays a role in discriminating fallers and non-fallers in people with Parkinson's disease (PD).

Objectives

To investigate which specific digital gait and turning measures, obtained with body-worn sensors, best discriminated fallers from non-fallers with PD in the clinic and during daily life.

Methods

We recruited 34 subjects with PD (17 fallers and 17 non-fallers based on the past 6 month's falls). Subjects wore three inertial sensors attached to both feet and the lumbar region in the laboratory for a 3-minute walking task (both off and on levodopa) and during daily life activities for a week. We derived 24 digital (18 gait and 6 turn) measures from the 3-minute walk and from daily life.

Results

In clinic, none of the gait and turning measures collected during on levodopa state were significantly different between fallers and non-fallers. In contrast, digital measures collected in the off levodopa state were significantly different between groups, (average turn velocity, average number of steps to complete a turn, and variability of gait speed, P < 0.03). During daily life, the variability of average turn velocity (P = 0.023) was significantly different in fallers than non-fallers. Last, the average number of steps to complete a turn was significantly correlated with the patient-reported outcomes.

Conclusions

Digital measures of turning, but not gait, were different in fallers compared to non-fallers with PD, in the laboratory when off medication and during a daily life.

Associations Between Local Area Deprivation and Physical Activity Participation in People with Cognitive Impairment in the North East of England

BACKGROUND

Promoting physical activity, such as habitual walking behaviors, in people with cognitive impairment may support their ability to remain independent with a good quality of life for longer. However, people with cognitive impairment participate in less physical activity compared to cognitively unimpaired older adults. The local area in which people live may significantly impact abilities to participate in physical activity. For example, people who live in more deprived areas may have less safe and walkable routes.

OBJECTIVE

To examine this further, this study aimed to explore associations between local area deprivation and physical activity in people with cognitive impairment and cognitively unimpaired older adults (controls).

METHODS

87 participants with cognitive impairment (mild cognitive impairment or dementia) and 27 older adult controls from the North East of England were included in this analysis. Participants wore a tri-axial wearable accelerometer (AX3, Axivity) on their lower backs continuously for seven days. The primary physical activity outcome was daily step count. Individuals' neighborhoods were linked to UK government area deprivation statistics. Hierarchical Bayesian models assessed the association between local area deprivation and daily step count in people with cognitive impairment and controls.

RESULTS

Key findings indicated that there was no association between local area deprivation and daily step count in people with cognitive impairment, but higher deprivation was associated with lower daily steps for controls.

CONCLUSION

These findings suggest that cognitive impairment may be associated with lower participation in physical activity which supersedes the influence of local area deprivation observed in normal aging.

Real world validation of activity recognition algorithm and development of novel behavioral biomarkers of falls in aged control and movement disorder patients

The use of wearable sensors in movement disorder patients such as Parkinson's disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.

Should we use both clinical and mobility measures to identify fallers in Parkinson's disease?

BACKGROUND

Although much is known about the multifactorial nature of falls in Parkinson's disease (PD), optimal classification of fallers remains unclear.

OBJECTIVE

To identify clinical (demographic, motor, cognitive and patient-reported) and objective mobility (balance and gait) measures that best discriminate fallers from non-fallers in PD.

METHODS

People with mild-to-moderate idiopathic PD were classified as fallers (at least one fall; n = 54) or non-fallers (n = 90) based on previous six months falls. Clinical characteristics included demographic, motor and cognitive status and patient-reported outcomes. Mobility (balance and gait) characteristics were derived from body-worn, inertial sensors while performing walking and standing tasks. To investigate the combinations of (up to four) measures that best discriminate fallers from non-fallers in each scenario (i.e., clinical-only, mobility-only and combined clinical + mobility models), we applied logistic regression employing a 'best subsets selection strategy' with a 5-fold cross validation, and calculated the area under the curve (AUC).

RESULTS

The highest AUCs for the clinical-only, mobility-only and clinical + mobility models were 0.89, 0.88, and 0.94, respectively. The most consistently selected measures in the top-10 ranked models were freezing of gait status (8x), the root mean square of anterior-posterior trunk acceleration while standing on a foam with eyes open (5x), gait double support duration (4x) and the postural instability and gait disorders score from the MDS UPDRS (4x).

CONCLUSIONS

Findings highlight the importance of considering multiple aspects of clinical as well as objective balance and gait characteristics for the classification of fallers and non-fallers in PD.

Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease?

BACKGROUND

Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD).

OBJECTIVE

Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally.

METHODS

88 PD and 111 OA participants from ICICLE-GAIT (UK) were included. RWS was evaluated using an accelerometer at four time points. RWS was aggregated within walking bout (WB) duration thresholds. Between-group-comparisons in RWS between PD and OAs were conducted cross-sectionally, and longitudinally with mixed effects models (MEMs). Cross-sectional association between RWS and MDS-UPDRS III was explored using linear regression, and longitudinal association explored with MEMs.

RESULTS

RWS was significantly lower in PD (1.04 m/s) in comparison to OAs (1.10 m/s) cross-sectionally. RWS significantly decreased over time for both cohorts and decline was more rapid in PD by 0.02 m/s per year. Significant negative relationship between RWS and the MDS-UPDRS III only existed at a specific WB threshold (30 to 60 s, β= - 3.94 points, p = 0.047). MDS-UPDRS III increased significantly by 1.84 points per year, which was not related to change in RWS.

CONCLUSION

Digital mobility assessment of gait may add unique information to quantify disease progression remotely, but further validation in research and clinical settings is needed.

Fragmentation, circadian amplitude, and fractal pattern of daily-living physical activity in people with multiple sclerosis: Is there relevant information beyond the total amount of physical activity?

BACKGROUND

Physical activity is lower in people with multiple sclerosis (pwMS) compared to healthy controls. Previous work focused on studying activity levels or activity volume, but studies of daily-living rest-activity fragmentation patterns, circadian rhythms, and fractal regulation in pwMS are limited. Based on findings in other cohorts, one could suggest that these aspects of daily-living physical activity will provide additional information about the health and well-being of pwMS. Therefore, here, we aimed to (1) identify which fragmentation, fractal, and circadian amplitude measures differ between pwMS and healthy controls, (2) evaluate the relationship between fragmentation, fractal, and circadian amplitude measures and disease severity, and (3) begin to evaluate the added value of those measures, as compared to more conventional measures of physical activity (e.g., mean signal vector magnitude (SVM). A global measure of the overall volume of physical activity).

METHODS

132 people with relapsing-remitting MS (47±11 yrs, 69.7% female, Expanded Disability Status Scale, EDSS, median (IQR): 3 (2-4)) and 90 healthy controls (46±11 yrs, 47.8% female) were asked to wear a 3D accelerometer on their lower back for 7 days. Rest-activity fragmentation, circadian amplitude, fractal regulation, and mean SVM metrics were extracted. PwMS and healthy controls were compared using independent samples t-tests and linear regression, including comparisons adjusted for mean SVM to control for the effect of physical activity volume. Spearman correlations between measures and logistic regressions were used to identify the clinical condition based on the measures that differed significantly after adjusting for SVM. All analyses included adjustments for demographic and clinical parameters (e.g., age, sex).

RESULTS

Multiple measures of activity fragmentation significantly differed between pwMS and healthy controls, reflecting a more fragmented active behavior in pwMS. PwMS had a lower circadian rhythm amplitude, indicating a smaller amplitude in the circadian changes of daily activity, and weaker temporal correlations as based on the fractal analysis. When taking into account physical activity volume, one circadian amplitude measure and one fractal measure remained significantly different in pwMS and controls. Fragmentation measures and circadian amplitude measures were significantly associated with disability level as measured by the EDSS; the association with circadian amplitude remained significant, even after adjusting for the mean SVM.

CONCLUSION

The physical activity patterns of pwMS differ from those of healthy individuals in rest-activity fragmentation, the amplitude of the circadian rhythm, and fractal regulation. Measures describing these aspects of activity provide information that is not captured in the total volume of physical activity and could, perhaps, augment the monitoring of disease progression and evaluation of the response to interventions.

Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment

Background

Falls in older adults are a critical public health problem. As a means to assess fall risks, free-living digital biomarkers (FLDBs), including spatiotemporal gait measures, drawn from wearable inertial measurement unit (IMU) data have been investigated to identify those at high risk. Although gait-related FLDBs can be impacted by intrinsic (e.g., gait impairment) and/or environmental (e.g., walking surfaces) factors, their respective impacts have not been differentiated by the majority of free-living fall risk assessment methods. This may lead to the ambiguous interpretation of the subsequent FLDBs, and therefore, less precise intervention strategies to prevent falls.

Methods

With the aim of improving the interpretability of gait-related FLDBs and investigating the impact of environment on older adults’ gait, a vision-based framework was proposed to automatically detect the most common level walking surfaces. Using a belt-mounted camera and IMUs worn by fallers and non-fallers (mean age 73.6 yrs), a unique dataset (i.e., Multimodal Ambulatory Gait and Fall Risk Assessment in the Wild (MAGFRA-W)) was acquired. The frames and image patches attributed to nine participants’ gait were annotated: (a) outdoor terrains: pavement (asphalt, cement, outdoor bricks/tiles), gravel, grass/foliage, soil, snow/slush; and (b) indoor terrains: high-friction materials (e.g., carpet, laminated floor), wood, and tiles. A series of ConvNets were developed: EgoPlaceNet categorizes frames into indoor and outdoor; and EgoTerrainNet (with outdoor and indoor versions) detects the enclosed terrain type in patches. To improve the framework’s generalizability, an independent training dataset with 9,424 samples was curated from different databases including GTOS and MINC-2500, and used for pretrained models’ (e.g., MobileNetV2) fine-tuning.

Results

EgoPlaceNet detected outdoor and indoor scenes in MAGFRA-W with 97.36$$\%$$ % and 95.59$$\%$$ % (leave-one-subject-out) accuracies, respectively. EgoTerrainNet-Indoor and -Outdoor achieved high detection accuracies for pavement (87.63$$\%$$ % ), foliage (91.24$$\%$$ % ), gravel (95.12$$\%$$ % ), and high-friction materials (95.02$$\%$$ % ), which indicate the models’ high generalizabiliy.

Conclusions

Encouraging results suggest that the integration of wearable cameras and deep learning approaches can provide objective contextual information in an automated manner, towards context-aware FLDBs for gait and fall risk assessment in the wild.

Suitability of a Low-Cost Wearable Sensor to Assess Turning in Healthy Adults

Background: Turning is a complex measure of gait that accounts for over 50% of daily steps. Traditionally, turning has been measured in a research grade laboratory setting, however, there is demand for a low-cost and portable solution to measure turning using wearable technology. This study aimed to determine the suitability of a low-cost inertial sensor-based device (AX6, Axivity) to assess turning, by simultaneously capturing and comparing to a turn algorithm output from a previously validated reference inertial sensor-based device (Opal), in healthy young adults. Methodology: Thirty participants (aged 23.9 ± 4.89 years) completed the following turning protocol wearing the AX6 and reference device: a turn course, a two-minute walk (including 180° turns) and turning in place, alternating 360° turn right and left. Both devices were attached at the lumbar spine, one Opal via a belt, and the AX6 via double sided tape attached directly to the skin. Turning measures included number of turns, average turn duration, angle, velocity, and jerk. Results: Agreement between the outcomes from the AX6 and reference device was good to excellent for all turn characteristics (all ICCs > 0.850) during the turning 360° task. There was good agreement for all turn characteristics (all ICCs > 0.800) during the two-minute walk task, except for moderate agreement for turn angle (ICC 0.683). Agreement for turn outcomes was moderate to good during the turns course (ICCs range; 0.580 to 0.870). Conclusions: A low-cost wearable sensor, AX6, can be a suitable and fit-for-purpose device when used with validated algorithms for assessment of turning outcomes, particularly during continuous turning tasks. Future work needs to determine the suitability and validity of turning in aging and clinical cohorts within low-resource settings.

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.

Using digital technology to quantify habitual physical activity in community-dwellers with cognitive impairment: A systematic review (Preprint)

BACKGROUND

Participating in habitual physical activity (HPA) can support people with dementia and mild cognitive impairment (MCI) to maintain functional independence. Digital technology can continuously measure HPA objectively, capturing nuanced measures relating to its volume, intensity, pattern, and variability.

OBJECTIVE

To understand HPA participation in people with cognitive impairment, this systematic review aims to (1) identify digital methods and protocols; (2) identify metrics used to assess HPA; (3) describe differences in HPA between people with dementia, MCI, and controls; and (4) make recommendations for measuring and reporting HPA in people with cognitive impairment.

METHODS

Key search terms were input into 6 databases: Scopus, Web of Science, Psych Articles, PsychInfo, MEDLINE, and Embase. Articles were included if they included community dwellers with dementia or MCI, reported HPA metrics derived from digital technology, were published in English, and were peer reviewed. Articles were excluded if they considered populations without dementia or MCI diagnoses, were based in aged care settings, did not concern digitally derived HPA metrics, or were only concerned with physical activity interventions. Key outcomes extracted included the methods and metrics used to assess HPA and differences in HPA outcomes across the cognitive spectrum. Data were synthesized narratively. An adapted version of the National Institute of Health Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was used to assess the quality of articles. Due to significant heterogeneity, a meta-analysis was not feasible.

RESULTS

A total of 3394 titles were identified, with 33 articles included following the systematic review. The quality assessment suggested that studies were moderate-to-good quality. Accelerometers worn on the wrist or lower back were the most prevalent methods, while metrics relating to volume (eg, daily steps) were most common for measuring HPA. People with dementia had lower volumes, intensities, and variability with different daytime patterns of HPA than controls. Findings in people with MCI varied, but they demonstrated different patterns of HPA compared to controls.

CONCLUSIONS

This review highlights limitations in the current literature, including lack of standardization in methods, protocols, and metrics; limited information on validity and acceptability of methods; lack of longitudinal research; and limited associations between HPA metrics and clinically meaningful outcomes. Limitations of this review include the exclusion of functional physical activity metrics (eg, sitting/standing) and non-English articles. Recommendations from this review include suggestions for measuring and reporting HPA in people with cognitive impairment and for future research including validation of methods, development of a core set of clinically meaningful HPA outcomes, and further investigation of socioecological factors that may influence HPA participation.

TRIAL REGISTRATION

PROSPERO CRD42020216744; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=216744  .

Real-World Stair Ambulation Characteristics Differ Between Prospective Fallers and Non-Fallers in Parkinson's Disease

Falls are among the leading causes of injuries or death for individuals from the age of 65 and the prevalence of falls is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Due to advancements in wearable sensor technology, inertial measurement units (IMUs) can be integrated unobtrusively into patients' everyday lives to monitor various mobility and gait parameters, which are related to common risk factors like reduced balance and reduced lower-limb muscle strength, or lower range of joints. Although stair ambulation is a fundamental part of our daily lives and is known for its unique challenges for the gait and balance system, long-term gait analysis studies have not investigated real-world stair ambulation parameters yet. Therefore, we applied a recently published gait analysis pipeline on real-world foot-worn IMU data of 40 PD patients over a recording period of two weeks to extract objective gait parameters from level walking but also from stair ascending and stair descending gait. In combination with fall records from a prospective three-month follow-up phase, we investigated group differences in gait parameters of future fallers compared to non-fallers for each individual gait activity. We found significant differences in stair ascending and descending parameters. Stance time was increased by up to 20% and gait speed reduced by up to 16% for fallers compared to non-fallers during stair walking. These differences were not present in level walking parameters. Hence, these results suggest that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the unique challenges stairs add to the balance and control system. Our work complements existing gait analysis studies by adding new insights into mobility and gait performance during real-world gait.

A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.

Free-living gait does not differentiate chronic mTBI patients compared to healthy controls

Background

Physical function remains a crucial component of mild traumatic brain injury (mTBI) assessment and recovery. Traditional approaches to assess mTBI lack sensitivity to detect subtle deficits post-injury, which can impact a patient’s quality of life, daily function and can lead to chronic issues. Inertial measurement units (IMU) provide an opportunity for objective assessment of physical function and can be used in any environment. A single waist worn IMU has the potential to provide broad/macro quantity characteristics to estimate gait mobility, as well as more high-resolution micro spatial or temporal gait characteristics (herein, we refer to these as measures of quality). Our recent work showed that quantity measures of mobility were less sensitive than measures of turning quality when comparing the free-living physical function of chronic mTBI patients and healthy controls. However, no studies have examined whether measures of gait quality in free-living conditions can differentiate chronic mTBI patients and healthy controls. This study aimed to determine whether measures of free-living gait quality can differentiate chronic mTBI patients from controls.

Methods

Thirty-two patients with chronic self-reported balance symptoms after mTBI (age: 40.88 ± 11.78 years, median days post-injury: 440.68 days) and 23 healthy controls (age: 48.56 ± 22.56 years) were assessed for ~ 7 days using a single IMU at the waist on a belt. Free-living gait quality metrics were evaluated for chronic mTBI patients and controls using multi-variate analysis. Receiver operating characteristics (ROC) and Area Under the Curve (AUC) analysis were used to determine outcome sensitivity to chronic mTBI.

Results

Free-living gait quality metrics were not different between chronic mTBI patients and controls (all p > 0.05) whilst controlling for age and sex. ROC and AUC analysis showed stride length (0.63) was the most sensitive measure for differentiating chronic mTBI patients from controls.

Conclusions

Our results show that gait quality metrics determined through a free-living assessment were not significantly different between chronic mTBI patients and controls. These results suggest that measures of free-living gait quality were not impaired in our chronic mTBI patients, and/or, that the metrics chosen were not sensitive enough to detect subtle impairments in our sample.

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda.

Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning

Parkinson's disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30-60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use.

Clinical Trial Registration

[09/H0906/82].

Exploring Inertial-Based Wearable Technologies for Objective Monitoring in Sports-Related Concussion: A Single-Participant Report

OBJECTIVE

Challenges remain in sports-related concussion (SRC) assessment to better inform return to play. Reliance on self-reported symptoms within the Sports Concussion Assessment Tool means that there are limited data on the effectiveness of novel methods to assess a player's readiness to return to play. Digital methods such as wearable technologies may augment traditional SRC assessment and improve objectivity in making decisions regarding return to play.

METHODS

The participant was a male university athlete who had a recent history of SRC. The single-participant design consisted of baseline laboratory testing immediately after SRC, free-living monitoring, and follow-up supervised testing after 2 months. The primary outcome measures were from traditional assessment (eg, Sports Concussion Assessment Tool and 2-minute instrumented walk/gait test; secondary outcome measures were from remote (free-living) assessment with a single wearable inertial measurement unit (eg, for gait and sleep).

RESULTS

The university athlete (age = 20 years, height = 175 cm, weight = 77 kg [176.37 lb]) recovered and returned to play 20 days after SRC. Primary measures returned to baseline levels after 12 days. However, supervised (laboratory-based) wearable device assessment showed that gait impairments (increased step time) remained even after the athlete was cleared for return to play (2 months). Similarly, a 24-hour remote gait assessment showed changes in step time, step time variability, and step time asymmetry immediately after SRC and at return to play (1 month after SRC). Remote sleep analysis showed differences in sleep quality and disturbance (increased movement between immediately after SRC and once the athlete had returned to play [1 month after SRC]).

CONCLUSION

The concern about missed or delayed SRC diagnosis is growing, but methods to objectively monitor return to play after concussion are still lacking. This report showed that wearable device assessment offers additional objective data for use in monitoring players who have SRC. This work could better inform SRC assessment and return-to-play protocols.

IMPACT

Digital technologies such as wearable technologies can yield additional data that traditional self-report approaches cannot. Combining data from nondigital (traditional) and digital (wearable) methods may augment SRC assessment for improved return-to-play decisions.

LAY SUMMARY

Inertia-based wearable technologies (eg, accelerometers) may be useful to help augment traditional, self-report approaches to sports-related concussion assessment and management by better informing return-to-play protocols.

Feasibility of a Multimodal Telemedical Intervention for Patients with Parkinson's Disease-A Pilot Study

Symptoms of Parkinson’s disease (PD) can be controlled well, but treatment often requires expert judgment. Telemedicine and sensor-based assessments can allow physicians to better observe the evolvement of symptoms over time, in particular with motor fluctuations. In addition, they potentially allow less frequent visits to the expert’s office and facilitate care in rural areas. A variety of systems with different strengths and shortcomings has been investigated in recent years. We designed a multimodal telehealth intervention (TelePark) to mitigate the shortcomings of individual systems and assessed the feasibility of our approach in 12 patients with PD over 12 weeks in preparation for a larger randomized controlled trial. TelePark uses video visits, a smartphone app, a camera system, and wearable sensors. Structured training included setting up the equipment in patients’ homes and group-based online training. Usability was assessed by questionnaires and semi-standardized telephone interviews. Overall, 11 out of 12 patients completed the trial (5 female, 6 male). Mean age was 65 years, mean disease duration 7 years, mean MoCA score 27. Adherence was stable throughout the study and 79% for a short questionnaire administered every second day, 62% for medication confirmation, and 33% for an electronic Hauser diary. Quality of life did not change in the course of the study, and a larger cohort will be required to determine the effect on motor symptoms. Interviews with trial participants identified motivations to use such systems and areas for improvements. These insights can be helpful in designing similar trials.

Characteristics of older Australian community aged care clients who fall: Incidents reported by care staff

Falls are the leading cause of injury and hospitalisation for older adults (aged 65 years or older) worldwide. Data collected by community aged care providers are an underutilised source of information about precipitating risk factors and consequences of falls for older adults living in the community. The objective of this longitudinal, observational study was to describe and compare the characteristics of older Australians who did and did not have falls reported by community aged care staff. We analysed 19 months of routinely collected care management and incident data for 1,596 older clients from a large Australian community care provider. Differences in sociodemographic characteristics, care needs and community care service use were compared between those who had one or more reported falls and those who had none. Fall-related outcomes (injuries, hospitalisations, relocation to residential aged care) were examined. The average age of clients was 82 years and most were women (66%). Seventy-seven (4.8%) clients had one or more reported falls over the study period (total falls = 92). Clients who had falls reported by care staff were more likely to be older adults, male and use more hours of community care services per week. There were 38 falls-related injuries, 5 falls-related hospitalisations and 20 clients relocated to residential aged care after a reported fall. This study demonstrates the potential for using routinely collected community aged care data to understand risk factors and monitor longitudinal outcomes for a population at high risk of falls.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Factors Influencing Habitual Physical Activity in Parkinson’s Disease: Considering the Psychosocial State and Wellbeing of People with Parkinson’s and Their Carers

Participating in habitual physical activity (HPA) may slow onset of dependency and disability for people with Parkinson’s disease (PwP). While cognitive and physical determinants of HPA are well understood, psychosocial influences are not. This pilot study aimed to identify psychosocial factors associated with HPA to guide future intervention development. Sixty-four PwP participated in this study; forty had carer informants. PwP participants wore a tri-axial accelerometer on the lower back continuously for seven days at two timepoints (18 months apart), measuring volume, pattern and variability of HPA. Linear mixed effects analysis identified relationships between demographic, clinical and psychosocial data and HPA from baseline to 18 months. Key results in PwP with carers indicated that carer anxiety and depression were associated with increased HPA volume (p < 0.01), while poorer carer self-care was associated with reduced volume of HPA over 18 months (p < 0.01). Greater carer strain was associated with taking longer walking bouts after 18 months (p < 0.01). Greater carer depression was associated with lower variability of HPA cross-sectionally (p = 0.009). This pilot study provides preliminary novel evidence that psychosocial outcomes from PwP’s carers may impact HPA in Parkinson’s disease. Interventions to improve HPA could target both PwP and carers and consider approaches that also support psychosocial wellbeing.

Concurrent Validity of Zeno Instrumented Walkway and Video-Based Gait Features in Adults With Parkinson’s Disease

Background: Parkinson’s disease (PD) presents with motor symptoms such as bradykinesia, rigidity, and tremor that can affect gait. To monitor changes associated with disease progression or medication use, quantitative gait assessment is often performed during clinical visits. Conversely, vision-based solutions have been proposed for monitoring gait quality in non-clinical settings. Methods: We use three 2D human pose-estimation libraries (AlphaPose, Detectron, OpenPose) and one 3D library (ROMP) to calculate gait features from color video, and correlate them with those extracted by a Zeno instrumented walkway in older adults with PD. We calculate video-based gait features using a manual and automated heel-strike detection algorithm, and compare the correlations when the participants walk towards and away from the camera separately. Results: Based on analysis of 67 bidirectional walking bouts from 25 adults with PD, moderate to strong positive correlations were identified between the number of steps, cadence, as well as the mean and coefficient of variation of step width calculated from Zeno and video using 2D pose-estimation libraries. We noted that our automated heel-strike annotation method struggled to identify short steps. Conclusion: Gait features calculated from 2D joint trajectories are more strongly correlated with the Zeno than analogous gait features calculated from ROMP. Based on our analysis, videos processed with 2D pose-estimation libraries can be used for longitudinal gait monitoring in individuals with PD. Future work will seek to improve the prediction of gait features using a comprehensive machine learning model to predict gait features directly from color video without relying on intermediate extraction of joint trajectories.

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

BACKGROUND

Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living. Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.

RESULTS

A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls.

DISCUSSION

This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.

Wearables in rugby union: A protocol for multimodal digital sports-related concussion assessment

Background

Pragmatic challenges remain in the monitoring and return to play (RTP) decisions following suspected Sports Related Concussion (SRC). Reliance on traditional approaches (pen and paper) means players readiness for RTP is often based on self-reported symptom recognition as a marker for full physiological recovery. Non-digital approaches also limit opportunity for robust data analysis which may hinder understanding of the interconnected nature and relationships in deficit recovery. Digital approaches may provide more objectivity to measure and monitor impairments in SRC. Crucially, there is dearth of protocols for SRC assessment and digital devices have yet to be tested concurrently (multimodal) in SRC rugby union assessment. Here we propose a multimodal protocol for digital assessment in SRC, which could be used to enhance traditional sports concussion assessment approaches.

Methods

We aim to use a repeated measures observational study utilising a battery of multimodal assessment tools (symptom, cognitive, visual, motor). We aim to recruit 200 rugby players (male n≈100 and female n≈100) from University Rugby Union teams and local amateur rugby clubs in the North East of England. The multimodal battery assessment used in this study will compare metrics between digital methods and against traditional assessment.

Conclusion

This paper outlines a protocol for a multimodal approach for the use of digital technologies to augment traditional approaches to SRC, which may better inform RTP in rugby union. Findings may shed light on new ways of working with digital tools in SRC. Multimodal approaches may enhance understanding of the interconnected nature of impairments and provide insightful, more objective assessment and RTP in SRC.

Clinical trial registration

NCT04938570. https://clinicaltrials.gov/ct2/results?cond=NCT04938570&term=&cntry=&state=&city=&dist=

Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson's Disease

People with Parkinson's disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson's disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R² value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

Sports related concussion: an emerging era in digital sports technology

Sports-related concussion (SRC) is defined as a mild traumatic brain injury (mTBI) leading to complex impairment(s) in neurological function with many seemingly hidden or difficult to measure impairments that can deteriorate rapidly without any prior indication. Growing numbers of SRCs in professional and amateur contact sports have prompted closer dialog regarding player safety and welfare. Greater emphasis on awareness and education has improved SRC management, but also highlighted the difficulties of diagnosing SRC in a timely manner, particularly during matches or immediately after competition. Therefore, challenges exist in off-field assessment and return to play (RTP) protocols, with current traditional (subjective) approaches largely based on infrequent snapshot assessments. Low-cost digital technologies may provide more objective, integrated and personalized SRC assessment to better inform RTP protocols whilst also enhancing the efficiency and precision of healthcare assessment. To fully realize the potential of digital technologies in the diagnosis and management of SRC will require a significant paradigm shift in clinical practice and mindset. Here, we provide insights into SRC clinical assessment methods and the translational utility of digital approaches, with a focus on off-field digital techniques to detect key SRC metrics/biomarkers. We also provide insights and recommendations to the common benefits and challenges facing digital approaches as they aim to transition from novel technologies to an efficient, valid, reliable, and integrated clinical assessment tool for SRC. Finally, we highlight future opportunities that digital approaches have in SRC assessment and management including digital twinning and the "digital athlete".

Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis

Background

Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC^© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS.

Methods

We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m.

Results

Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure.

Conclusions

The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.

Developing and exploring a methodology for multi-modal indoor and outdoor gait assessment

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.