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Abstract Background How the joints exactly move and interact and how this reflects PD-related gait abnormalities and the response to dopaminergic treatment is poorly understood. A detailed understanding of these kinematics can inform clinical management and treatment decisions. The aim of the study was to investigate the influence of different gait speeds and medication on/off conditions on inter-joint coordination, as well as kinematic differences throughout the whole gait cycle in well characterized pwPD. Methods 29 controls and 29 PD patients during medication on, 8 of them also during medication off walked a straight walking path in slow, preferred and fast walking speeds. Gait data was collected using optical motion capture system. Kinematics of the hip and knee and coordinated hip-knee kinematics were evaluated using Statistical Parametric Mapping (SPM) and cyclograms (angle-angle plots). Values derived from cyclograms were compared using repeated-measures ANOVA for within group, and ttest for between group comparisons. Results PD gait differed from controls mainly by lower knee range of motion (ROM). Adaptation to gait speed in PD was mainly achieved by increasing hip ROM. Regularity of gait was worse in PD but only during preferred speed. The ratios of different speed cyclograms were smaller in the PD groups. SPM analyses revealed that PD participants had smaller hip and knee angles during the swing phase, and PD participants reached peak hip flexion later than controls. Withdrawal of medication showed an exacerbation of only a few parameters. Conclusions Our findings demonstrate the potential of granular kinematic analyses, including > 1 joint, for disease and treatment monitoring in PD. Our approach can be extended to further mobility-limiting conditions and other joint combinations. Trial registration The study is registered in the German Clinical Trials Register (DRKS00022998, registered on 04 Sep 2020).

We present an extension to the Brain Imaging Data Structure (BIDS) for motion data. Motion data is frequently recorded alongside human brain imaging and electrophysiological data. The goal of Motion-BIDS is to make motion data interoperable across different laboratories and with other data modalities in human brain and behavioral research. To this end, Motion-BIDS standardizes the data format and metadata structure. It describes how to document experimental details, considering the diversity of hardware and software systems for motion data. This promotes findable, accessible, interoperable, and reusable data sharing and Open Science in human motion research.

Abstract Background Maintaining static balance is relevant and common in everyday life and it depends on a correct intersegmental coordination. A change or reduction in postural capacity has been linked to increased risk of falls. People with Parkinson’s disease (pwPD) experience motor symptoms affecting the maintenance of a stable posture. The aim of the study is to understand the intersegmental changes in postural sway and to apply a trend change analysis to uncover different movement strategies between pwPD and healthy adults. Methods In total, 61 healthy participants, 40 young (YO), 21 old participants (OP), and 29 pwPD (13 during medication off, PDoff; 23 during medication on, PDon) were included. Participants stood quietly for 10 s as part of the Short Physical Performance Battery. Inertial measurement units (IMU) at the head, sternum, and lumbar region were used to extract postural parameters and a trend change analysis (TCA) was performed to compare between groups. Objective This study aims to explore the potential application of TCA for the assessment of postural stability using IMUs, and secondly, to employ this analysis within the context of neurological diseases, specifically Parkinson’s disease. Results Comparison of sensors locations revealed significant differences between head, sternum and pelvis for almost all parameters and cohorts. When comparing PDon and PDoff, the TCA revealed differences that were not seen by any other parameter. Conclusions While all parameters could differentiate between sensor locations, no group differences could be uncovered except for the TCA that allowed to distinguish between the PD on/off. The potential of the TCA to assess disease progression, response to treatment or even the prodromal PD phase should be explored in future studies. Trial registration The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register (DRKS00022998).

Abstract Background Isolated REM sleep behavior disorder (iRBD) is an early α-synucleinopathy often accompanied by incipient cognitive impairment. As executive dysfunctions predict earlier phenotypic conversion from iRBD to Parkinson’s disease and Lewy body dementia, cognitive training focusing on executive functions could have disease-modifying effects for individuals with iRBD. Methods The study CogTrAiL-RBD investigates the short- and long-term effectiveness and the feasibility and underlying neural mechanisms of a cognitive training intervention for individuals with iRBD. The intervention consists of a 5-week digital cognitive training accompanied by a module promoting a healthy, active lifestyle. In this monocentric, single-blinded, delayed-start randomized controlled trial, the intervention’s effectiveness will be evaluated compared to an initially passive control group that receives the intervention in the second, open-label phase of the study. Eighty individuals with iRBD confirmed by polysomnography will be consecutively recruited from the continuously expanding iRBD cohort at the University Hospital Cologne. The evaluation will focus on cognition and additional neuropsychological and motor variables. Furthermore, the study will examine the feasibility of the intervention, effects on physical activity assessed by accelerometry, and interrogate the intervention’s neural effects using magnetic resonance imaging and polysomnography. Besides, a healthy, age-matched control group (HC) will be examined at the first assessment time point, enabling a cross-sectional comparison between individuals with iRBD and HC. Discussion This study will provide insights into whether cognitive training and psychoeducation on a healthy, active lifestyle have short- and long-term (neuro-)protective effects for individuals with iRBD. Trial registration The study was prospectively registered in the German Clinical Trial Register (DRKS00024898) on 2022–03-11, https://drks.de/search/de/trial/DRKS00024898 . Protocol version: V5 2023–04-24.

Abstract Background In the Climb Up! Head Up! trial, we showed that sport climbing reduces bradykinesia, tremor, and rigidity in mildly to moderately affected participants with Parkinson’s disease. This secondary analysis aimed to evaluate the effects of sport climbing on gait and functional mobility in this cohort. Methods Climb Up! Head Up! was a 1:1 randomized controlled trial. Forty-eight PD participants (Hoehn and Yahr stage 2–3) either participated in a 12-week, 90-min-per-week sport climbing course (intervention group) or were engaged in regular unsupervised physical activity (control group). Relevant outcome measures for this analysis were extracted from six inertial measurement units placed on the extremities, chest, and lower back, that were worn during supervised gait and functional mobility assessments before and after the intervention. Assessments included normal and fast walking, dual-tasking walking, Timed Up and Go test, Instrumented Stand and Walk test, and Five Times Sit to Stand test. Results Compared to baseline, climbing improved gait speed during normal walking by 0.09 m/s (p = 0.005) and during fast walking by 0.1 m/s. Climbing also reduced the time spent in the stance phase during fast walking by 0.03 s. Climbing improved the walking speed in the 7-m- Timed Up and Go test by 0.1 m/s (p

Abstract Idiopathic REM sleep Behavior Disorder (iRBD) is a condition at high risk of developing Parkinson’s disease (PD) and other alpha-synucleinopathies. The aim of the study was to evaluate subtle turning alterations by using Mobile health technology in iRBD individuals without subthreshold parkinsonism. A total of 148 participants (23 persons with polysomnography-confirmed iRBD without subthreshold parkinsonism, 60 drug-naïve PD patients, and 65 age-matched controls were included in this prospective cross-sectional study. All underwent a multidimensional assessment including cognitive and non-motor symptoms assessment. Then a Timed-Up-and-Go test (TUG) at normal and fast speed was performed using mobile health technology on the lower back (Rehagait®, Hasomed, Germany). Duration, mean, and peak angular velocities of the turns were compared using a multivariate model correcting for age and sex. Compared to controls, PD patients showed longer turn durations and lower mean and peak angular velocities of the turns in both TUGs (all p ≤ 0.001). iRBD participants also showed a longer turn duration and lower mean (p = 0.006) and peak angular velocities (p

Abstract 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.

BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785

Abstract Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors

IntroductionFatigue is a common and disabling symptom in Parkinson’s disease (PD), also affecting gait. Detection of fatigue-associated changes of gait using mobile health technologies (MHT) could become increasingly effective.MethodsCognitively unimpaired PD patients without fluctuations (UPDRS IV < 1) underwent a standard neurological assessment including the PD-Fatigue scale (PFS-16). PD patients with (PD-F) and without fatigue (PD-N) were matched for age, sex, cognitive function and disease severity. Each participant underwent MHT gait assessment under supervised condition (SC) and unsupervised condition (UC).ResultsGait parameters of 21 PD-F and 21 PD-N did not significantly differ under SC. Under UC, PD-F showed higher step time, step time variability and asymmetry index compared to PD-N and the PFS-16 correlated with step time.ConclusionThis is the first MHT-based study with PD patients showing a correlation between fatigue and gait parameters. In addition, the data collected suggest that UC is clearly superior to SC in addressing this question.

IntroductionThe clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.MethodsHere, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.Results and discussionThe results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and

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

Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a “gold standard” and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.

Background Gait characteristics are important risk factors for falls, hospitalisations and mortality in older adults, but the impact of COPD on gait performance remains unclear. We aimed to identify differences in gait characteristics between adults with COPD and healthy age-matched controls during 1) laboratory tests that included complex movements and obstacles, 2) simulated daily-life activities (supervised) and 3) free-living daily-life activities (unsupervised). Methods This case–control study used a multi-sensor wearable system (INDIP) to obtain seven gait characteristics for each walking bout performed by adults with mild-to-severe COPD (n=17; forced expiratory volume in 1 s 57±19% predicted) and controls (n=20) during laboratory tests, and during simulated and free-living daily-life activities. Gait characteristics were compared between adults with COPD and healthy controls for all walking bouts combined, and for shorter (≤30 s) and longer (>30 s) walking bouts separately. Results Slower walking speed (−11 cm·s−1, 95% CI: −20 to −3) and lower cadence (−6.6 steps·min−1, 95% CI: −12.3 to −0.9) were recorded in adults with COPD compared to healthy controls during longer (>30 s) free-living walking bouts, but not during shorter (≤30 s) walking bouts in either laboratory or free-living settings. Double support duration and gait variability measures were generally comparable between the two groups. Conclusion Gait impairment of adults with mild-to-severe COPD mainly manifests during relatively long walking bouts (>30 s) in free-living conditions. Future research should determine the underlying mechanism(s) of this impairment to facilitate the development of interventions that can improve free-living gait performance in adults with COPD.

Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design – from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.

Abstract Background Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. Methods The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants’ strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson’s disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. Results The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. Conclusions The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. Trial registration: ISRCTN—12246987.

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

Daily steps could be a valuable indicator of real-world ambulation in Parkinson’s disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland–Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2–5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann–Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84–0.90; SAM range: 7.8–9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD.

BackgroundTurning during walking is a relevant and common everyday movement and it depends on a correct top-down intersegmental coordination. This could be reduced in several conditions (en bloc turning), and an altered turning kinematics has been linked to increased risk of falls. Smartphone use has been associated with poorer balance and gait; however, its effect on turning-while-walking has not been investigated yet. This study explores turning intersegmental coordination during smartphone use in different age groups and neurologic conditions. ObjectiveThis study aims to evaluate the effect of smartphone use on turning behavior in healthy individuals of different ages and those with various neurological diseases. MethodsYounger (aged 18-60 years) and older (aged >60 years) healthy individuals and those with Parkinson disease, multiple sclerosis, subacute stroke (

Background This study aimed to explore the acceptability of a wearable device for remotely measuring mobility in the Mobilise-D technical validation study (TVS), and to explore the acceptability of using digital tools to monitor health. Methods Participants ( N = 106) in the TVS wore a waist-worn device (McRoberts Dynaport MM + ) for one week. Following this, acceptability of the device was measured using two questionnaires: The Comfort Rating Scale (CRS) and a previously validated questionnaire. A subset of participants ( n = 36) also completed semi-structured interviews to further determine device acceptability and to explore their opinions of the use of digital tools to monitor their health. Questionnaire results were analysed descriptively and interviews using a content analysis. Results The device was considered both comfortable (median CRS (IQR; min-max) = 0.0 (0.0; 0–20) on a scale from 0–20 where lower scores signify better comfort) and acceptable (5.0 (0.5; 3.0–5.0) on a scale from 1–5 where higher scores signify better acceptability). Interviews showed it was easy to use, did not interfere with daily activities, and was comfortable. The following themes emerged from participants’ as being important to digital technology: altered expectations for themselves, the use of technology, trust, and communication with healthcare professionals. Conclusions Digital tools may bridge existing communication gaps between patients and clinicians and participants are open to this. This work indicates that waist-worn devices are supported, but further work with patient advisors should be undertaken to understand some of the key issues highlighted. This will form part of the ongoing work of the Mobilise-D consortium.

Abstract Physical mobility is essential to health, and patients often rate it as a high-priority clinical outcome. Digital mobility outcomes (DMOs), such as real-world gait speed or step count, show promise as clinical measures in many medical conditions. However, current research is nascent and fragmented by discipline. This scoping review maps existing evidence on the clinical utility of DMOs, identifying commonalities across traditional disciplinary divides. In November 2019, 11 databases were searched for records investigating the validity and responsiveness of 34 DMOs in four diverse medical conditions (Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease, hip fracture). Searches yielded 19,672 unique records. After screening, 855 records representing 775 studies were included and charted in systematic maps. Studies frequently investigated gait speed (70.4% of studies), step length (30.7%), cadence (21.4%), and daily step count (20.7%). They studied differences between healthy and pathological gait (36.4%), associations between DMOs and clinical measures (48.8%) or outcomes (4.3%), and responsiveness to interventions (26.8%). Gait speed, step length, cadence, step time and step count exhibited consistent evidence of validity and responsiveness in multiple conditions, although the evidence was inconsistent or lacking for other DMOs. If DMOs are to be adopted as mainstream tools, further work is needed to establish their predictive validity, responsiveness, and ecological validity. Cross-disciplinary efforts to align methodology and validate DMOs may facilitate their adoption into clinical practice.

Abstract The evidence of the responsiveness of dopaminergic medication on gait in patients with Parkinson’s disease is contradicting. This could be due to differences in complexity of the context gait was in performed. This study analysed the effect of dopaminergic medication on arm swing, an important movement during walking, in different contexts. Forty-five patients with Parkinson’s disease were measured when walking at preferred speed, fast speed, and dual-tasking conditions in both OFF and ON medication states. At preferred, and even more at fast speed, arm swing improved with medication. However, during dual-tasking, there were only small or even negative effects of medication on arm swing. Assuming that dual-task walking most closely reflects real-life situations, the results suggest that the effect of dopaminergic medication on mobility-relevant movements, such as arm swing, might be small in everyday conditions. This should motivate further studies to look at medication effects on mobility in Parkinson’s disease, as it could have highly relevant implications for Parkinson’s disease treatment and counselling.

IntroductionLinks between cognition and walking performance in patients with Parkinson’s disease (PD), which both decline with disease progression, are well known. There is lack of knowledge regarding the predictive value of cognition for changes in walking performance after individualized therapy. The aim of this study is to identify relevant predictive cognitive and affective parameters, measurable in daily clinical routines, for change in quantitative walking performance after early geriatric rehabilitation.MethodsForty-seven acutely hospitalized patients with advanced PD were assessed at baseline (T1) and at the end (T2) of a 2-week early rehabilitative geriatric complex treatment (ERGCT). Global cognitive performance (Montreal Cognitive Assessment, MoCA), EF and divided attention (Trail Making Test B minus A, delta TMT), depressive symptoms, and fear of falling were assessed at T1. Change in walking performance was determined by the difference in quantitative walking parameters extracted from a sensor-based movement analysis over 20 m straight walking in single (ST, fast and normal pace) and dual task (DT, with secondary cognitive, respectively, motor task) conditions between T1 and T2. Bayesian regression (using Bayes Factor BF10) and multiple linear regression models were used to determine the association of non-motor characteristics for change in walking performance.ResultsUnder ST, there was moderate evidence (BF10 = 7.8, respectively, BF10 = 4.4) that lower performance in the ∆TMT at baseline is associated with lower reduction of step time asymmetry after treatment (R2adj = 0.26, p ≤ 0.008, respectively, R2adj = 0.18, p ≤ 0.009). Under DT walking-cognitive, there was strong evidence (BF10 = 29.9, respectively, BF10 = 27.9) that lower performance in the ∆TMT is associated with more reduced stride time and double limb support (R2adj = 0.62, p ≤ 0.002, respectively, R2adj = 0.51, p ≤ 0.009). There was moderate evidence (BF10 = 5.1) that a higher MoCA total score was associated with increased gait speed after treatment (R2adj = 0.30, p ≤ 0.02).DiscussionOur results indicate that the effect of ERGT on change in walking performance is limited for patients with deficits in EF and divided attention. However, these patients also seem to walk more cautiously after treatment in walking situations with additional cognitive demand. Therefore, future development of individualized treatment algorithms is required, which address individual needs of these vulnerable patients.

IntroductionDual-tasking (DT) while walking is common in daily life and can affect both gait and cognitive performance depending on age, attention prioritization, task complexity and medical condition. The aim of the present study was to investigate the effects of DT on cognitive DT cost (DTC) (i) in a dataset including participants of different age groups, with different neurological disorders and chronic low-back pain (cLBP) (ii) at different levels of cognitive task complexity, and (iii) in the context of a setting relevant to daily life, such as combined straight walking and turning.Materials and methodsNinety-one participants including healthy younger and older participants and patients with Parkinson's disease, Multiple Sclerosis, Stroke and cLBP performed a simple reaction time (SRT) task and three numerical Stroop tasks under the conditions congruent (StC), neutral (StN) and incongruent (StI). The tasks were performed both standing (single task, ST) and walking (DT), and DTC was calculated. Mixed ANOVAs were used to determine the effect of group and task complexity on cognitive DTC.ResultsA longer response time in DT than in ST was observed during SRT. However, the response time was shorter in DT during StI. DTC decreased with increasing complexity of the cognitive task. There was no significant effect of age and group on cognitive DTC.ConclusionOur results suggest that regardless of age and disease group, simple cognitive tasks show the largest and most stable cognitive effects during DT. This may be relevant to the design of future observational studies, clinical trials and for clinical routine.

Introduction: Parkinson’s disease (PD) is a complex neurodegenerative disease with motor and nonmotor symptoms with a multitude of disease variations and severity. Physical activity can improve the management of disease symptoms and increase patients’ quality of life. Technological development of small wearable devices allows objective activity measurement such as daily step count. Objective: To synthesize ongoing and past research on objective walking activity measurements using wearable devices in patients with PD. Methods: PubMed, Cochrane, Web of Science, and PEDro database are systematically searched with no limitation on publication date. Keywords are relative to (1) the population, (2) the measurement tool, and (3) the measured outcomes. Only full-text English articles published in a peer-reviewed journal will be included. Participants do not have to undergo any type of intervention. Included studies must report an objective measurement of walking activity using wearable devices in PD patients. After an independent screening process done by 2 reviewers, data will be extracted from the articles according to the following 5 set of data: (1) the study metrics, (2) the population characteristics, (3) the measurement tools, (4) the experimental procedure, and (5) the reported outcomes. Results: The results will contain inter alia summaries of the wearables’ specifications, wearing location, and recommendations for feasible methodologies to capture daily walking activity. Discussion: This review aims to synthesize the evidence of objective walking activity assessment with wearable devices in patients with PD. It will also provide recommendations with regard to device selection and suggest key points when monitoring walking activity in this specific population.

Gait changes during aging and differs between sexes. Inertial measurement units (IMUs) enable accurate quantitative evaluations of gait in ambulatory environments and in large populations. This study aims to provide IMU-based gait parameters’ values derived from a large longitudinal cohort study in older adults. We measured gait parameters, such as velocity, step length, time, variability, and asymmetry, from straight, self-paced 20-m walks in older adults (four visits: 715/1102/1017/957 participants) every second year over 6 years using an IMU at the lower back. Moreover, we calculated the associations of gait parameters with sex and age. Women showed lower gait speed, step length, step time, stride time, swing time, and stance time, compared to men. Longitudinal analyses suggest that these parameters are at least partly deteriorating within the assessment period of 2 years, especially in men and at an older age. Variability and asymmetry parameters show a less clear sex- and age-associated pattern. Altogether, our large longitudinal dataset provides the first sex-specific information on which parameters are particularly promising for the detection of age-related gait changes that can be extracted from an IMU on the lower back. This information may be helpful for future observational and treatment studies investigating sex and age-related effects on gait, as well as for studies investigating age-related diseases.

IntroductionIt is well-known that, in Parkinson's disease (PD), executive function (EF) and motor deficits lead to reduced walking performance. As previous studies investigated mainly patients during the compensated phases of the disease, the aim of this study was to investigate the above associations in acutely hospitalized patients with PD.MethodsA total of seventy-four acutely hospitalized patients with PD were assessed with the delta Trail Making Test (ΔTMT, TMT-B minus TMT-A) and the Movement Disorder Society-revised version of the motor part of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS III). Walking performance was assessed with wearable sensors under single (ST; fast and normal pace) and dual-task (DT; walking and checking boxes as the motor secondary task and walking and subtracting seven consecutively from a given three-digit number as the cognitive secondary task) conditions over 20 m. Multiple linear regression and Bayes factor BF10 were performed for each walking parameter and their dual-task costs while walking (DTC) as dependent variables and also included ΔTMT, MDS-UPDRS III, age, and gender.ResultsUnder ST, significant negative effects of the use of a walking aid and MDS-UPDRS III on gait speed and at a fast pace on the number of steps were observed. Moreover, depending on the pace, the use of a walking aid, age, and gender affected step time variability. Under walking-cognitive DT, a resolved variance of 23% was observed in the overall model for step time variability DTC, driven mainly by age (β = 0.26, p = 0.09). Under DT, no other significant effects could be observed. ΔTMT showed no significant associations with any of the walking conditions.DiscussionThe results of this study suggest that, in acutely hospitalized patients with PD, reduced walking performance is mainly explained by the use of a walking aid, motor symptoms, age, and gender, and EF deficits surprisingly do not seem to play a significant role. However, these patients with PD should avoid walking-cognitive DT situations, as under this condition, especially step time variability, a parameter associated with the risk of falling in PD worsens.

BackgroundAlpine skiers typically train using repeated practice runs requiring high bursts of muscle activity but there is little field-based evidence characterizing neuromuscular function across successive runs.PurposeTo examine the impact of repeated ski runs on electromyographic activity (EMG) of the knee extensors and flexors in elite alpine skiers.MethodsNineteen national team alpine skiers were tested during regular ski training [Slalom (SL), Giant Slalom (GS), Super Giant Slalom and Downhill (Speed)] for a total of 39 training sessions. The surface EMG of the vastus lateralis (VL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and semimembranosus/semitendinosus (SMST) muscles was continuously recorded along with right knee and hip angles. The EMG root mean square signal was normalized to a maximal voluntary contraction (%MVC). The first and fourth runs of the training session were compared.ResultsThere was no meaningful main effect of run on EMG relative activation time or mean power frequency beyond the skier's intrinsic variability. However, EMG activity of the vastii increased from the first to the fourth run in SL [VM, ~+3%MVC for IL and outside leg (OL), p = 0.035)], speed (VL, IL:+6%/OL:+11%, p = 0.015), and GS (VM, IL:0/OL:+7%, p < 0.001); the later with an interaction with leg (p < 0.001) due to a localized increase on the OL. The run time and turn time did not change from the first to the fourth run. There were no meaningful changes in angular velocities, amplitude of movement, or maximal and minimal angles.ConclusionNeuromuscular activity remains highly stable in elite skiers with low variability across four runs.

Abstract Background Human well-being has been linked to the composition and functional capacity of the intestinal microbiota. As regular exercise is known to improve human health, it is not surprising that exercise was previously described to positively modulate the gut microbiota, too. However, most previous studies mainly focused on either elite athletes or animal models. Thus, we conducted a randomised intervention study that focused on the effects of different types of training (endurance and strength) in previously physically inactive, healthy adults in comparison to controls that did not perform regular exercise. Overall study duration was ten weeks including six weeks of intervention period. In addition to 16S rRNA gene amplicon sequencing of longitudinally sampled faecal material of participants (six time points), detailed body composition measurements and analysis of blood samples (at baseline and after the intervention) were performed to obtain overall physiological changes within the intervention period. Activity tracker devices (wrist-band wearables) provided activity status and sleeping patterns of participants as well as exercise intensity and heart measurements. Results Different biometric responses between endurance and strength activities were identified, such as a significant increase of lymphocytes and decrease of mean corpuscular haemoglobin concentration (MCHC) only within the strength intervention group. In the endurance group, we observed a significant reduction in hip circumference and an increase in physical working capacity (PWC). Though a large variation of microbiota changes were observed between individuals of the same group, we did not find specific collective alterations in the endurance nor the strength groups, arguing for microbiome variations specific to individuals, and therefore, were not captured in our analysis. Conclusions We could show that different types of exercise have distinct but moderate effects on the overall physiology of humans and very distinct microbial changes in the gut. The observed overall changes during the intervention highlight the importance of physical activity on well-being. Future studies should investigate the effect of exercise on a longer timescale, investigate different training intensities and consider high-resolution shotgun metagenomics technology. Trial registration DRKS, DRKS00015873 . Registered 12 December 2018; Retrospectively registered.

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

Abstract Background Anti-gravity treadmills are used to decrease musculoskeletal loading during treadmill running often in return to play rehabilitation programs. The effect different gradients (uphill/downhill running) have on kinetics and spatiotemporal parameters when using an AlterG® treadmill is unclear with previous research focused on level running only. Methods Ten well-trained healthy male running athletes ran on the AlterG® treadmill at varying combinations of bodyweight support (60, 80, and 100% BW), speed (12 km/hr., 15 km/hr., 18 km/hr., 21 km/hr., and 24 km/hr), and gradients (− 15% decline, − 10, − 5, 0, + 5, + 10 + 15% incline), representing a total of 78 conditions performed in random order. Maximum plantar force and contact time were recorded using a wireless in-shoe force sensor insole system. Results Regression analysis showed a linear relationship for maximum plantar force with bodyweight support and running speeds for level running (p

Abstract Background Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson’s disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. Methods Participants (older adults, people with Parkinson’s disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. Results The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$=$$ = 100%, F1 score $$=$$ = 100%), slalom walking (IC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%; FC: recall $$=$$ = 100%, precision $$\ge$$ ≥ 99%, F1 score $$=$$ = 100%), and turning (IC: recall $$\ge$$ ≥ 85%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 91%; FC: recall $$\ge$$ ≥ 84%, precision $$\ge$$ ≥ 95%, F1 score $$\ge$$ ≥ 89%). Conclusions Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.

Clinical gait analysis has a long-standing tradition in biomechanics. However, the use of kinematic data or segment coordination has not been reported based on wearable sensors in “real-life” environments. In this work, the skeletal kinematics of 21 healthy and 24 neurogeriatric participants was collected in a magnetically disturbed environment with inertial measurement units (IMUs) using an accelerometer-based functional calibration method. The system consists of seven IMUs attached to the lower back, the thighs, the shanks, and the feet to acquire and process the raw sensor data. The Short Physical Performance Battery (SPPB) test was performed to relate joint kinematics and segment coordination to the overall SPPB score. Participants were then divided into three subgroups based on low (0–6), moderate (7–9), or high (10–12) SPPB scores. The main finding of this study is that most IMU-based parameters significantly correlated with the SPPB score and the parameters significantly differed between the SPPB subgroups. Lower limb range of motion and joint segment coordination correlated positively with the SPPB score, and the segment coordination variability correlated negatively. The results suggest that segment coordination impairments become more pronounced with a decreasing SPPB score, indicating that participants with low overall SPPB scores produce a peculiar inconsistent walking pattern to counteract lower extremity impairment in strength, balance, and mobility. Our findings confirm the usefulness of SPPB through objectively measured parameters, which may be relevant for the design of future studies and clinical routines.

Neurological diseases are associated with static postural instability. Differences in postural sway between neurological diseases could include “conceptual” information about how certain symptoms affect static postural stability. This information might have the potential to become a helpful aid during the process of finding the most appropriate treatment and training program. Therefore, this study investigated static postural sway performance of Parkinson's disease (PD) and multiple sclerosis (MS) patients, as well as of a cohort of healthy adults. Three increasingly difficult static postural tasks were performed, in order to determine whether the postural strategies of the two disease groups differ in response to the increased complexity of the balance task. Participants had to perform three stance tasks (side-by-side, semi-tandem and tandem stance) and maintain these positions for 10 s. Seven static sway parameters were extracted from an inertial measurement unit that participants wore on the lower back. Data of 47 healthy adults, 14 PD patients and 8 MS patients were analyzed. Both healthy adults and MS patients showed a substantial increase in several static sway parameters with increasingly complex stance tasks, whereas PD patients did not. In the MS patients, the observed substantial change was driven by large increases from semi-tandem and tandem stance. This study revealed differences in static sway adaptations between PD and MS patients to increasingly complex stance tasks. Therefore, PD and MS patients might require different training programs to improve their static postural stability. Moreover, this study indicates, at least indirectly, that rigidity/bradykinesia and spasticity lead to different adaptive processes in static sway.

Abstract Background Sit-to-stand and stand-to-sit transitions are frequent daily functional tasks indicative of muscle power and balance performance. Monitoring these postural transitions with inertial sensors provides an objective tool to assess mobility in both the laboratory and home environment. While the measurement depends on the sensor location, the clinical and everyday use requires high compliance and subject adherence. The objective of this study was to propose a sit-to-stand and stand-to-sit transition detection algorithm that works independently of the sensor location. Methods For a location-independent algorithm, the vertical acceleration of the lower back in the global frame was used to detect the postural transitions in daily activities. The detection performance of the algorithm was validated against video observations. To investigate the effect of the location on the kinematic parameters, these parameters were extracted during a five-time sit-to-stand test and were compared for different locations of the sensor on the trunk and lower back. Results The proposed detection method demonstrates high accuracy in different populations with a mean positive predictive value (and mean sensitivity) of 98% (95%) for healthy individuals and 89% (89%) for participants with diseases. Conclusions The sensor location around the waist did not affect the performance of the algorithm in detecting the sit-to-stand and stand-to-sit transitions. However, regarding the accuracy of the kinematic parameters, the sensors located on the sternum and L5 vertebrae demonstrated the highest reliability.

Abstract Background Motor and cognitive deficits and consequently mobility problems are common in geriatric patients. The currently available methods for diagnosis and for the evaluation of treatment in this vulnerable cohort are limited. The aims of the ComOn (COgnitive and Motor interactions in the Older populatioN) study are (i) to define quantitative markers with clinical relevance for motor and cognitive deficits, (ii) to investigate the interaction between both motor and cognitive deficits and (iii) to assess health status as well as treatment outcome of 1000 geriatric inpatients in hospitals of Kiel (Germany), Brescia (Italy), Porto (Portugal), Curitiba (Brazil) and Bochum (Germany). Methods This is a prospective, explorative observational multi-center study. In addition to the comprehensive geriatric assessment, quantitative measures of reduced mobility and motor and cognitive deficits are performed before and after a two week’s inpatient stay. Components of the assessment are mobile technology-based assessments of gait, balance and transfer performance, neuropsychological tests, frailty, sarcopenia, autonomic dysfunction and sensation, and questionnaires to assess behavioral deficits, activities of daily living, quality of life, fear of falling and dysphagia. Structural MRI and an unsupervised 24/7 home assessment of mobility are performed in a subgroup of participants. The study will also investigate the minimal clinically relevant change of the investigated parameters. Discussion This study will help form a better understanding of symptoms and their complex interactions and treatment effects in a large geriatric cohort.

In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervised tests in the lab and also during daily activities. To our best knowledge, the effect of FOF on mobility has not been investigated in both of these settings simultaneously. Our goal was to evaluate the effect of FOF on the mobility of 26 patients with PD during clinical assessments and 14 days of daily activity monitoring. Parameters related to gait, sit-to-stand transitions, and turns were extracted from IMU signals on the lower back. Fear of falling was assessed using the Falls Efficacy Scale-International (FES-I) and the patients were grouped as with (PD-FOF+) and without FOF (PD-FOF−). Mobility parameters between groups were compared using logistic regression as well as the effect size values obtained using the Wilcoxon rank-sum test. The peak angular velocity of the turn-to-sit transition of the timed-up-and-go (TUG) test had the highest discriminative power between PD-FOF+ and PD-FOF− (r-value of effect size = 0.61). Moreover, PD-FOF+ had a tendency toward lower gait speed at home and a lower amount of walking bouts, especially for shorter walking bouts. The combination of lab and daily activity parameters reached a higher discriminative power [area under the curve (AUC) = 0.75] than each setting alone (AUC = 0.68 in the lab, AUC = 0.54 at home). Comparing the gait speed between the two assessments, the PD-FOF+ showed higher gait speeds in the capacity area compared with their TUG test in the lab. The mobility parameters extracted from both lab and home-based assessments contribute to the detection of FOF in PD. This study adds further evidence to the usefulness of mobility assessments that include different environments and assessment strategies. Although this study was limited in the sample size, it still provides a helpful method to consider the daily activity measurement of the patients with PD into clinical evaluation. The obtained results can help the clinicians with a more accurate prevention and treatment strategy.

Commercial smartwatches could be useful for step counting and monitoring ambulatory activity. However, in Parkinson’s disease (PD) patients, an altered gait, pharmacological condition, and symptoms lateralization may affect their accuracy and potential usefulness in research and clinical routine. Steps were counted during a 6 min walk in 47 patients with PD and 47 healthy subjects (HS) wearing a Garmin Vivosmart 4 (GV4) on each wrist. Manual step counting was used as a reference. An inertial sensor (BTS G-Walk), placed on the lower back, was used to compute spatial-temporal gait parameters. Intraclass correlation coefficient (ICC) and mean absolute percentage error (MAPE) were used for accuracy evaluation and the Spearman test was used to assess the correlations between variables. The GV4 overestimated steps in PD patients with only a poor-to-moderate agreement. The OFF pharmacological state and wearing the device on the most-affected body side led to an unacceptable accuracy. The GV4 showed an excellent agreement and MAPE in HS at a self-selected speed, but an unacceptable performance at a slow speed. In PD patients, MAPE was not associated with gait parameters and clinical variables. The accuracy of commercial smartwatches for monitoring step counting might be reduced in PD patients and further influenced by the pharmacological condition and placement of the device.

Monitoring disease progression in Parkinson’s disease is challenging. Postural transfers by sit-to-stand motions are adapted to trace the motor performance of subjects. Wearable sensors such as inertial measurement units allow for monitoring motion performance. We propose quantifying the sit-to-stand performance based on two scores compiling kinematics, dynamics, and energy-related variables. Three groups participated in this research: asymptomatic young participants (n = 33), senior asymptomatic participants (n = 17), and Parkinson’s patients (n = 20). An unsupervised classification was performed of the two scores to differentiate the three populations. We found a sensitivity of 0.4 and a specificity of 0.96 to distinguish Parkinson’s patients from asymptomatic subjects. In addition, seven Parkinson’s patients performed the sit-to-stand task “ON” and “OFF” medication, and we noted the scores improved with the patients’ medication states (MDS-UPDRS III scores). Our investigation revealed that Parkinson’s patients demonstrate a wide spectrum of mobility variations, and while one inertial measurement unit can quantify the sit-to-stand performance, differentiating between PD patients and healthy adults and distinguishing between “ON” and “OFF” periods in PD patients is still challenging.

Gait and balance dysfunctions are common in neurological disorders and have a negative effect on quality of life. Regularly quantifying these mobility limitations can be used to measure disease progression and the effect of treatment. This information can be used to provide a more individualized treatment. Inertial measurement units (IMUs) can be utilized to quantify mobility in different contexts. However, algorithms are required to extract valuable parameters out of the raw IMU data. These algorithms need to be validated to make sure that they extract the features they should extract. This validation should be performed per disease since different mobility limitations or symptoms can influence the performance of an algorithm in different ways. Therefore, this dataset contains data from both healthy subjects and patients with neurological diseases (Parkinson’s disease, stroke, multiple sclerosis, chronic low back pain). The full bodies of 167 subjects were measured with IMUs and an optical motion capture (reference) system. Subjects performed multiple standardized mobility assessments and non-standardized activities of daily living. The data of 21 healthy subjects are shared online, data of the other subjects and patients can only be obtained after contacting the corresponding author and signing a data sharing agreement.

Parkinson’s disease (PD) is a complex neurodegenerative disease with a multitude of disease variations including motor and non-motor symptoms. Quality of life and symptom management may be improved with physical activity. Due to technological advancement, development of small new wearable devices recently emerged and allowed objective measurement of walking activity in daily life. This review was specifically designed to synthesize literature on objective walking activity measurements using wearable devices of patients with PD. Inclusion criteria included patients with a diagnosis of PD and exclusion criteria included studies using animal models or mixed syndromes. Participants were not required to undergo any type of intervention and the studies must have reported at least one output that quantifies daily walking activity. Three databases were systematically searched with no limitation on publication date. Twenty-six studies were eligible and included in the systematic review. The most frequently used device was the ActiGraph GT3X which was used in 10 studies. Duration of monitoring presented a range from 8 h to one year. Nevertheless, 11 studies measured walking activity during a 7-day period. On-body sensor wearing location differed throughout the included studies showing eight positions, with the waist, ankle, and wrist being the most frequently used locations. The main procedures consisted of measurement of walking hours during a 2-day period or more, equipped with a triaxial accelerometer at the dominant hip or ankle. It is also important for further research to take care of different factors such as the population, their pathology, the period, and the environment.

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Objective Anterior cruciate ligament reconstruction (ACLR) predisposes footballers for subsequent ACL and hamstring (HS) injury. This case series examines HS muscle activation patterns during the running in ACLR patients (bone-patellar tendon-bone (BTB) and (HS) graft) after completion of functional criteria allowing return to training.Methods Electromyography (EMG) recorded from medial and lateral HS bilaterally during treadmill running (12, 14 and 16 km/hour) from 21 male ACLR patients on average 7 months from surgery (5-9) that underwent (HS) (n=12) or BTB reconstruction (n=9) were compared with 19 healthy runners. Main outcome measures: EMG signal was normalised to peak during the running. Pairwise comparisons were made for each muscle group examining stance and swing activation for mean and peak EMG for each patient group and leg.Results Significantly lower relative peak activation in stance (not swing) phase for medial HS was seen for all conditions with effect sizes ranging from −0.63 (controls, BTB non-injured leg) to −1.09 (HS injured). For lateral HS only BTB injured were significantly lower in stance phase (−1.05)Conclusion ACLR patients show neuromuscular alterations during different phases of running. The finding of reduced medial HS activity in stance phase might have implications for knee instability and HS muscle injury on resumption of sport.

Digital mobility assessment using wearable sensor systems has the potential to capture walking performance in a patient's natural environment. It enables monitoring of health status and disease progression and evaluation of interventions in real-world situations. In contrast to laboratory settings, real-world walking occurs in non-conventional environments and under unconstrained and uncontrolled conditions. Despite the general understanding, there is a lack of agreed definitions about what constitutes real-world walking, impeding the comparison and interpretation of the acquired data across systems and studies. The goal of this study was to obtain expert-based consensus on specific aspects of real-world walking and to provide respective definitions in a common terminological framework. An adapted Delphi method was used to obtain agreed definitions related to real-world walking. In an online survey, 162 participants from a panel of academic, clinical and industrial experts with experience in the field of gait analysis were asked for agreement on previously specified definitions. Descriptive statistics was used to evaluate whether consent (> 75% agreement as defined a priori) was reached. Of 162 experts invited to participate, 51 completed all rounds (31.5% response rate). We obtained consensus on all definitions ("Walking" > 90%, "Purposeful" > 75%, "Real-world" > 90%, "Walking bout" > 80%, "Walking speed" > 75%, "Turning" > 90% agreement) after two rounds. The identification of a consented set of real-world walking definitions has important implications for the development of assessment and analysis protocols, as well as for the reporting and comparison of digital mobility outcomes across studies and systems. The definitions will serve as a common framework for implementing digital and mobile technologies for gait assessment and are an important link for the transition from supervised to unsupervised gait assessment.

Evaluating gait is part of every neurological movement disorder assessment. Generally, the physician assesses the patient based on their experience, but nowadays inertial measurement units (IMUs) are also often integrated in the assessment. Instrumented gait analysis has a longstanding tradition and temporal parameters are used to compare patient groups or trace disease progression over time. However, the day-to-day variability needs to be considered especially in specific patient cohorts. The aim of the study was to examine day-to-day variability of temporal gait parameters of two experimental conditions in a cohort of neurogeriatric patients using data extracted from a lower back-worn IMU. We recruited 49 participants (24 women (age: 78 years ± 6 years, BMI = 25.1 kg/m2 and 25 men (age: 77 years ± 6 years, BMI = 26.5 kg/m2)) from the neurogeriatric ward. Two gait distances (4 m and 20 m) were performed during the first session and repeated the following day. To evaluate reliability, the Intraclass Correlation Coefficient (ICC2,k) and minimal detectable change (MDC) were calculated for the number of steps, step time, stride time, stance time, swing time, double limb support time, double limb support time variability, stride time variability and stride time asymmetry. The temporal gait parameters showed poor to moderate reliability with mean ICC and mean MDC95% values of 0.57 ± 0.18 and 52% ± 53%, respectively. Overall, only four out of the nine computed temporal gait parameters showed high relative reliability and good absolute reliability values. The reliability increased with walking distance. When only investigating steady-state walking during the 20 m walking condition, the relative and absolute reliability improved again. The most reliable parameters were swing time, stride time, step time and stance time. Study results demonstrate that reliability is an important factor to consider when working with IMU derived gait parameters in specific patient cohorts. This advocates for a careful parameter selection as not all parameters seem to be suitable when assessing gait in neurogeriatric patients.

Preferred walking speed (PWS) is considered a robust measure for assessing mobility and overall health. Healthy reference data are unavailable for Qatar. The aim of this study was to investigate PWS and underlying gait parameters around PWS among healthy young adults living in Qatar. PWS was assessed for 18 Qataris (9 females) and 16 non-Qatari Arabs residing in Qatar (9 females). Within- and between-gender group comparisons were carried out using Mann–Whitney U -tests. Metabolic cost of transport, heart rate, rating of perceived exertion, and spatiotemporal parameters were compared between Qatari and non-Qatari groups of similar gender at seven speed levels relative to PWS using two-way analyses of variance (ANOVAs). Similar comparisons were done at two absolute speeds using Mann–Whitney U -tests. While PWS did not differ significantly between the female groups, it was on average 19% slower for the Qatari males as compared to non-Qatari males. At similar relative speeds, differences appeared solely in physiological parameters between female groups. Only spatiotemporal differences were revealed between the male groups where longer stride and support phase durations and slower stride frequencies characterized the Qatari male group. It is suggested that differences in PWS could be due to potential cultural factors (e.g., cultural clothing) differentiating the Qatari and non-Qatari groups. PWS values reported in this study also appear systematically lower when compared to Western references found in the literature. Findings suggest that the assessment of normative gait values needs to take both cultural habits and geographic disparity into account.

Introduction Advances in wearable sensor technology now enable frequent, objective monitoring of real-world walking. Walking-related digital mobility outcomes (DMOs), such as real-world walking speed, have the potential to be more sensitive to mobility changes than traditional clinical assessments. However, it is not yet clear which DMOs are most suitable for formal validation. In this review, we will explore the evidence on discriminant ability, construct validity, prognostic value and responsiveness of walking-related DMOs in four disease areas: Parkinson’s disease, multiple sclerosis, chronic obstructive pulmonary disease and proximal femoral fracture.Methods and analysis Arksey and O’Malley’s methodological framework for scoping reviews will guide study conduct. We will search seven databases (Medline, CINAHL, Scopus, Web of Science, EMBASE, IEEE Digital Library and Cochrane Library) and grey literature for studies which (1) measure differences in DMOs between healthy and pathological walking, (2) assess relationships between DMOs and traditional clinical measures, (3) assess the prognostic value of DMOs and (4) use DMOs as endpoints in interventional clinical trials. Two reviewers will screen each abstract and full-text manuscript according to predefined eligibility criteria. We will then chart extracted data, map the literature, perform a narrative synthesis and identify gaps.Ethics and dissemination As this review is limited to publicly available materials, it does not require ethical approval. This work is part of Mobilise-D, an Innovative Medicines Initiative Joint Undertaking which aims to deliver, validate and obtain regulatory approval for DMOs. Results will be shared with the scientific community and general public in cooperation with the Mobilise-D communication team.Registration Study materials and updates will be made available through the Center for Open Science’s OSFRegistry (https://osf.io/k7395).