Effects of physiotherapy and home-based training in parkinsonian syndromes: protocol for a randomised controlled trial (MobilityAPP).

INTRODUCTION: Gait and mobility impairment are pivotal signs of parkinsonism, and they are particularly severe in atypical parkinsonian disorders including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). A pilot study demonstrated a significant improvement of gait in patients with MSA of parkinsonian type (MSA-P) after physiotherapy and matching home-based exercise, as reflected by sensor-based gait parameters. In this study, we aim to investigate whether a gait-focused physiotherapy (GPT) and matching home-based exercise lead to a greater improvement of gait performance compared with a standard physiotherapy/home-based exercise programme (standard physiotherapy, SPT). METHODS AND ANALYSIS: This protocol was deployed to evaluate the effects of a GPT versus an active control undergoing SPT and matching home-based exercise with regard to laboratory gait parameters, physical activity measures and clinical scales in patients with Parkinson's disease (PD), MSA-P and PSP. The primary outcomes of the trial are sensor-based laboratory gait parameters, while the secondary outcome measures comprise real-world derived parameters, clinical rating scales and patient questionnaires. We aim to enrol 48 patients per disease group into this double-blind, randomised-controlled trial. The study starts with a 1 week wearable sensor-based monitoring of physical activity. After randomisation, patients undergo a 2 week daily inpatient physiotherapy, followed by 5 week matching unsupervised home-based training. A 1 week physical activity monitoring is repeated during the last week of intervention. ETHICS AND DISSEMINATION: This study, registered as 'Mobility in Atypical Parkinsonism: a Trial of Physiotherapy (Mobility_APP)' at clinicaltrials.gov (NCT04608604), received ethics approval by local committees of the involved centres. The patient's recruitment takes place at the Movement Disorders Units of Innsbruck (Austria), Erlangen (Germany), Lausanne (Switzerland), Luxembourg (Luxembourg) and Bolzano (Italy). The data resulting from this project will be submitted to peer-reviewed journals, presented at international congresses and made publicly available at the end of the trial. TRIAL REGISTRATION NUMBER: NCT04608604.

[How the implementation of a school for people with Parkinson's disease can succeed-Results of a consensus study and a formative evaluation]. / Wie die Umsetzung einer Schule für Menschen mit Parkinson-Krankheit gelingen kann ­ Ergebnisse eines Konsensusverfahrens und einer formativen Evaluation.

BACKGROUND: As the most rapidly increasing neurodegenerative disease worldwide, Parkinson's disease is highly relevant to society. Successful treatment requires active patient participation. Patient education has been successfully implemented for many chronic diseases, such as diabetes and could also provide people with Parkinson's disease with skills to manage the disease better and to participate in shared decision making. MATERIAL AND METHODS: To prepare the implementation of a concept for patient education for people with Parkinson's disease, a structured consensus study was conducted and a pilot project formatively evaluated. The structured consensus study included experts from all over Germany. It consisted of two online surveys and an online consensus conference. The formative evaluation was conducted as three focus groups. Transcripts were evaluated using content-structuring qualitative content analysis. RESULTS: From the consensus procedure 59 consented statements emerged, mainly regarding the contents of a patient school and a group size of 6-8 persons. Only two statements could not be consented. The formative evaluation detected a tendency towards a positive attitude for a digital training format and a very positive evaluation of the contents. DISCUSSION: Overall, important recommendations for a patient school can be drawn from this study. The following subjects require further investigation: format, inclusion criteria, group composition and inclusion of caregivers.

The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson's disease: a long-term feasibility study.

BACKGROUND: Exercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson's disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients. Training effects were assessed using patient-reported outcome measures combined with sensor-based and clinical scores. METHODS: 16 PD patients (smartphone group, SG) completed a home-based, individualized training program over 6-8 months using a smartphone app, remotely supervised by a therapist, and tailored to the patient's motor impairments and capacity. A control group (CG, n = 16) received medical treatment without participating in digital exercise training. The usability of the app was validated using System Usability Scale (SUS) and User Version of the Mobile Application Rating Scale (uMARS). Outcome measures included among others Unified Parkinson Disease Rating Scale, part III (UPDRS-III), sensor-based gait parameters derived from standardized gait tests, Parkinson's Disease Questionnaire (PDQ-39), and patient-defined motor activities of daily life (M-ADL). RESULTS: Exercise frequency of 74.5% demonstrated high adherence in this cohort. The application obtained 84% in SUS and more than 3.5/5 points in each subcategory of uMARS, indicating excellent usability. The individually assessed additional benefit showed at least 6 out of 10 points (Mean = 8.2 ± 1.3). From a clinical perspective, patient-defined M-ADL improved for 10 out of 16 patients by 15.5% after the training period. The results of the UPDRS-III remained stable in the SG while worsening in the CG by 3.1 points (24%). The PDQ-39 score worsened over 6-8 months by 83% (SG) and 59% (CG) but the subsection mobility showed a smaller decline in the SG (3%) compared to the CG (77%) without reaching significance level for all outcomes. Sensor-based gait parameters remained constant in both groups. CONCLUSIONS: Long-term training over 6-8 months with the app is considered feasible and acceptable, representing a cost-effective, individualized approach to complement dopaminergic treatment. This study indicates that personalized, digital, high-frequency training leads to benefits in motor sections of ADL and Quality of Life.

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning.

BACKGROUND: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed. METHODS: This study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 [Formula: see text] 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme. RESULTS: The results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%. CONCLUSION: Here, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.

Association between cognition and gait in multiple sclerosis: A smartphone-based longitudinal analysis.

BACKGROUND: Gait and cognition impairments are common problems among People with Multiple Sclerosis (PwMS). Previous studies have investigated cross-sectional associations between gait and cognition. However, there is a lack of evidence regarding the longitudinal association between these factors in PwMS. Therefore, the objective of this study was to explore this longitudinal relationship using smartphone-based data from the Floodlight study. METHODS: Using the publicly available Floodlight dataset, which contains smartphone-based longitudinal data, we used a linear mixed model to investigate the longitudinal relationship between cognition, measured by the Symbol Digit Modalities Test (SDMT), and gait, measured by the 2 Minute Walking test (2 MW) step count and Five-U-Turn Test (FUTT) turning speed. Four mixed models were fitted to explore the association between: 1) SDMT and mean step count; 2) SDMT and variability of step count; 3) SDMT and mean FUTT turning speed; and 4) SDMT and variability of FUTT turningt speed. RESULTS: After controlling for age, sex, weight, and height, there were significant correlations between SDMT and the variability of 2 MW step count, the mean of FUTT turning speed. No significant correlation was observed between SDMT and the 2 MW mean step count. SIGNIFICANCE: Our findings support the evidence that gait and cognition are associated in PwMS. This may support clinicians to adjust treatment and intervention programs that address both gait and cognitive impairments.

Validity of an inertial sensor-based system for the assessment of spatio-temporal parameters in people with multiple sclerosis.

Background: Gait variability in people with multiple sclerosis (PwMS) reflects disease progression or may be used to evaluate treatment response. To date, marker-based camera systems are considered as gold standard to analyze gait impairment in PwMS. These systems might provide reliable data but are limited to a restricted laboratory setting and require knowledge, time, and cost to correctly interpret gait parameters. Inertial mobile sensors might be a user-friendly, environment- and examiner-independent alternative. The purpose of this study was to evaluate the validity of an inertial sensor-based gait analysis system in PwMS compared to a marker-based camera system. Methods: A sample N = 39 PwMS and N = 19 healthy participants were requested to repeatedly walk a defined distance at three different self-selected walking speeds (normal, fast, slow). To measure spatio-temporal gait parameters (i.e., walking speed, stride time, stride length, the duration of the stance and swing phase as well as max toe clearance), an inertial sensor system as well as a marker-based camera system were used simultaneously. Results: All gait parameters highly correlated between both systems (r > 0.84) with low errors. No bias was detected for stride time. Stance time was marginally overestimated (bias = -0.02 ± 0.03 s) and gait speed (bias = 0.03 ± 0.05 m/s), swing time (bias = 0.02 ± 0.02 s), stride length (0.04 ± 0.06 m), and max toe clearance (bias = 1.88 ± 2.35 cm) were slightly underestimated by the inertial sensors. Discussion: The inertial sensor-based system captured appropriately all examined gait parameters in comparison to a gold standard marker-based camera system. Stride time presented an excellent agreement. Furthermore, stride length and velocity presented also low errors. Whereas for stance and swing time, marginally worse results were observed.

Mobile digital gait analysis objectively measures progression in hereditary spastic paraplegia.

Progressive spasticity and gait impairment is the functional hallmark of hereditary spastic paraplegia (HSP), but due to inter-individual variability, longitudinal studies on its progression are scarce. We investigated the progression of gait deficits via mobile digital measurements in conjunction with clinical and patient-reported outcome parameters. Our cohort included adult HSP patients (n = 55) with up to 77 months of follow-up. Gait speed showed a significant association with SPRS progression. Changes in stride time and gait variability correlated to fear of falling and quality of life, providing evidence that gait parameters are meaningful measures of HSP progression.

The Effects of an Individualized Smartphone-Based Exercise Program on Self-defined Motor Tasks in Parkinson Disease: Pilot Interventional Study.

BACKGROUND: Bradykinesia and rigidity are prototypical motor impairments of Parkinson disease (PD) highly influencing everyday life. Exercise training is an effective treatment alternative for motor symptoms, complementing dopaminergic medication. High frequency training is necessary to yield clinically relevant improvements. Exercise programs need to be tailored to individual symptoms and integrated in patients' everyday life. Due to the COVID-19 pandemic, exercise groups in outpatient setting were largely reduced. Developing remotely supervised solutions is therefore of significant importance. OBJECTIVE: This pilot study aimed to evaluate the feasibility of a digital, home-based, high-frequency exercise program for patients with PD. METHODS: In this pilot interventional study, patients diagnosed with PD received 4 weeks of personalized exercise at home using a smartphone app, remotely supervised by specialized therapists. Exercises were chosen based on the patient-defined motor impairment and depending on the patients' individual capacity (therapists defined 3-5 short training sequences for each participant). In a first education session, the tailored exercise program was explained and demonstrated to each participant and they were thoroughly introduced to the smartphone app. Intervention effects were evaluated using the Unified Parkinson Disease Rating Scale, part III; standardized sensor-based gait analysis; Timed Up and Go Test; 2-minute walk test; quality of life assessed by the Parkinson Disease Questionnaire; and patient-defined motor tasks of daily living. Usability of the smartphone app was assessed by the System Usability Scale. All participants gave written informed consent before initiation of the study. RESULTS: In total, 15 individuals with PD completed the intervention phase without any withdrawals or dropouts. The System Usability Scale reached an average score of 72.2 (SD 6.5) indicating good usability of the smartphone app. Patient-defined motor tasks of daily living significantly improved by 40% on average in 87% (13/15) of the patients. There was no significant impact on the quality of life as assessed by the Parkinson Disease Questionnaire (but the subsections regarding mobility and social support improved by 14% from 25 to 21 and 19% from 15 to 13, respectively). Motor symptoms rated by Unified Parkinson Disease Rating Scale, part III, did not improve significantly but a descriptive improvement of 14% from 18 to 16 could be observed. Clinically relevant changes in Timed Up and Go test, 2-minute walk test, and sensor-based gait parameters or functional gait tests were not observed. CONCLUSIONS: This pilot interventional study presented that a tailored, digital, home-based, and high-frequency exercise program over 4 weeks was feasible and improved patient-defined motor activities of daily life based on a self-developed patient-defined impairment score indicating that digital exercise concepts may have the potential to beneficially impact motor symptoms of daily living. Future studies should investigate sustainability effects in controlled study designs conducted over a longer period.

Connecting real-world digital mobility assessment to clinical outcomes for regulatory and clinical endorsement-the Mobilise-D study protocol.

BACKGROUND: The development of optimal strategies to treat impaired mobility related to ageing and chronic disease requires better ways to detect and measure it. Digital health technology, including body worn sensors, has the potential to directly and accurately capture real-world mobility. Mobilise-D consists of 34 partners from 13 countries who are working together to jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes have the potential to provide a better, safer, and quicker way to assess, monitor, and predict the efficacy of new interventions on impaired mobility. The overarching objective of the study is to establish the clinical validity of digital outcomes in patient populations impacted by mobility challenges, and to support engagement with regulatory and health technology agencies towards acceptance of digital mobility assessment in regulatory and health technology assessment decisions. METHODS/DESIGN: The Mobilise-D clinical validation study is a longitudinal observational cohort study that will recruit 2400 participants from four clinical cohorts. The populations of the Innovative Medicine Initiative-Joint Undertaking represent neurodegenerative conditions (Parkinson's Disease), respiratory disease (Chronic Obstructive Pulmonary Disease), neuro-inflammatory disorder (Multiple Sclerosis), fall-related injuries, osteoporosis, sarcopenia, and frailty (Proximal Femoral Fracture). In total, 17 clinical sites in ten countries will recruit participants who will be evaluated every six months over a period of two years. A wide range of core and cohort specific outcome measures will be collected, spanning patient-reported, observer-reported, and clinician-reported outcomes as well as performance-based outcomes (physical measures and cognitive/mental measures). Daily-living mobility and physical capacity will be assessed directly using a wearable device. These four clinical cohorts were chosen to obtain generalizable clinical findings, including diverse clinical, cultural, geographical, and age representation. The disease cohorts include a broad and heterogeneous range of subject characteristics with varying chronic care needs, and represent different trajectories of mobility disability. DISCUSSION: The results of Mobilise-D will provide longitudinal data on the use of digital mobility outcomes to identify, stratify, and monitor disability. This will support the development of widespread, cost-effective access to optimal clinical mobility management through personalised healthcare. Further, Mobilise-D will provide evidence-based, direct measures which can be endorsed by regulatory agencies and health technology assessment bodies to quantify the impact of disease-modifying interventions on mobility. TRIAL REGISTRATION: ISRCTN12051706.

MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data.

Developing machine learning algorithms for time-series data often requires manual annotation of the data. To do so, graphical user interfaces (GUIs) are an important component. Existing Python packages for annotation and analysis of time-series data have been developed without addressing adaptability, usability, and user experience. Therefore, we developed a generic open-source Python package focusing on adaptability, usability, and user experience. The developed package, Machine Learning and Data Analytics (MaD) GUI, enables developers to rapidly create a GUI for their specific use case. Furthermore, MaD GUI enables domain experts without programming knowledge to annotate time-series data and apply algorithms to it. We conducted a small-scale study with participants from three international universities to test the adaptability of MaD GUI by developers and to test the user interface by clinicians as representatives of domain experts. MaD GUI saves up to 75% of time in contrast to using a state-of-the-art package. In line with this, subjective ratings regarding usability and user experience show that MaD GUI is preferred over a state-of-the-art package by developers and clinicians. MaD GUI reduces the effort of developers in creating GUIs for time-series analysis and offers similar usability and user experience for clinicians as a state-of-the-art package.

Treadmill training and physiotherapy similarly improve dual task gait performance: a randomized-controlled trial in Parkinson's disease.

Motor-cognitive dual tasks are used to investigate the interplay between gait and cognition. Dual task walking in patients with Parkinson's disease (PD) results in decreased gait speed and more importantly in an increased fall risk. There is evidence that physical training may improve gait during dual task challenge. Physiotherapy and treadmill walking are known to improve single task gait. The aim of this study was to investigate the impact of individualized physiotherapy or treadmill training on gait during dual task performance. 105 PD patients were randomly assigned to an intervention group (physiotherapy or treadmill). Both groups received 10 individual interventional sessions of 25 min each and additional group therapy sessions for 14 days. Primary outcome measure was the dual task gait speed. Secondary outcomes were additional gait parameters during dual task walking, UPDRS-III, BBS and walking capacity. All gait parameters were recorded using sensor-based gait analysis. Gait speed improved significantly by 4.2% (treadmill) and 8.3% (physiotherapy). Almost all secondary gait parameters, UPDRS-III, BBS, and walking capacity improved significantly and similarly in both groups. However, interaction effects were not observed. Both interventions significantly improved gait in patients with mild to moderate PD. However, treadmill walking did not show significant benefits compared to individualized physiotherapy. Our data suggest that both interventions improve dual task walking and therefore support safe and independent walking. This result may lead to more tailored therapeutic preferences.

Inertial Gait Sensors to Measure Mobility and Functioning in Hereditary Spastic Paraplegia: A Cross-Sectional Multicenter Clinical Study.

BACKGROUND AND OBJECTIVES: Hereditary spastic paraplegia (HSP) causes progressive spasticity and weakness of the lower limbs. As neurological examination and the clinical Spastic Paraplegia Rating Scale (SPRS) are subject to potential patient- and clinician-dependent bias, instrumented gait analysis bears the potential to objectively quantify impaired gait. The aim of the present study was to investigate gait cyclicity parameters by application of a mobile gait analysis system in a cross sectional cohort of HSP patients and a longitudinal fast progressing subcohort. METHODS: Using wearable sensors attached to the shoes, HSP patients and controls performed a 4x10 meters walking test during regular visits in three outpatient centers. Patients were also rated according to the SPRS and in a subset, questionnaires on quality of life and fear of falling were obtained. An unsupervised segmentation algorithm was employed to extract stride parameters and respective coefficients of variation. RESULTS: Mobile gait analysis was performed in a total of 112 ambulatory HSP patients and 112 age and gender matched controls. While swing time was unchanged compared to controls, there were significant increases in the duration of the total stride phase and the duration of the stance phase, both regarding absolute values and coefficients of variation values. While stride parameters did not correlate to age, weight or height of the patients, there were significant associations of absolute stride parameters to single SPRS items reflecting impaired mobility (|r| > 0.50), to patients' quality of life (|r| > 0.44), and notably to disease duration (|r| > 0.27). Sensor-derived coefficients of variation, on the other hand, were associated with patient-reported fear of falling (|r| > 0.41) and cognitive impairment (|r| > 0.40). In a small 1-year follow-up analysis of patients with complicated HSP and fast progression, absolute values of mobile gait parameters had significantly worsened compared to baseline. DISCUSSION: The presented wearable sensor system provides parameters of stride characteristics which appear clinically valid to reflect gait impairment in HSP. Due to the feasibility with regard to time, space and costs, the present study forms the basis for larger scale longitudinal and interventional studies in HSP.

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 the elderly, and the prevalence is especially high for patients suffering from neurological diseases like Parkinson's disease (PD). Today, 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 or reduced lower-limb muscle strength. Although stair ambulation is a fundamental part of everyday life 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 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 descending. In combination with prospective fall records, 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. This suggests that real-world stair ambulation provides sensitive parameters for mobility and fall risk due to the 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.

Short inertial sensor-based gait tests reflect perceived state fatigue in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system, affecting more than 2.3 million people worldwide. Fatigue is among the most common symptoms in MS, resulting in reduced mobility and quality of life. The six-minute walking test (6MWT) is commonly used as a measure of fatigability for the assessment of state fatigue throughout treatment or rehabilitation programs. This 'gold standard' test is time-consuming and can be difficult and exhausting for some patients with high levels of disability or high rates of fatigue. RESEARCH QUESTION: Can short inertial sensor-based gait tests assess perceived state fatigue in MS patients? METHODS: Sixty-five MS patients equipped with one sensor on each foot performed the 6 min walk test (6MWT) and the 25-foot walk (25FW, at both preferred and fastest speed). Perceived state fatigue was measured after each minute of the 6MWT, using the Borg rating. The highest of these ratings served as a measure of overall perceived state fatigue. Stride-wise spatio-temporal gait parameters were extracted from the 25FW and from the first minute, first 2 min, and first 4 min of the 6MWT. Principal component analysis was performed. Perceived state fatigue was predicted in a regression analysis, using the principal components of gait parameters as predictors. Statistical tests evaluated differences in performance between the full 6MWT, the shortened 6MWT, and the 25FW. RESULTS: A mean absolute error of less than 2 points on the Borg rating was obtained using the shortened 6MWT and the 25FW. There were no significant differences between the prediction accuracy of the full 6MWT and that of the shortened gait tests. SIGNIFICANCE: It is possible to use shortened gait tests when evaluating perceived state fatigue in MS patients using inertial sensors. Substituting them for long gait tests may reduce the burden of the testing on both patients and clinicians. Further, the approach taken here may prompt future work to explore the use of short bouts of real-world walking with unobtrusive inertial sensors for state fatigue assessment.

Validation of a Sensor-Based Gait Analysis System with a Gold-Standard Motion Capture System in Patients with Parkinson's Disease.

Digital technologies provide the opportunity to analyze gait patterns in patients with Parkinson's Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to compare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland-Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

Walking on common ground: a cross-disciplinary scoping review on the clinical utility of digital mobility outcomes.

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.

An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters.

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson's disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient's fall risk and disease state or progression.

Functional gait measures correlate to fear of falling, and quality of life in patients with Hereditary Spastic Paraplegia: A cross-sectional study.

OBJECTIVE: Gait impairment is the cardinal motor symptom in hereditary spastic paraplegias (HSPs) possibly linked to increased fear of falling and reduced quality of life (QoL). Disease specific symptoms in HSP are rated using the Spastic Paraplegia Rating Scale (SPRS). However, limited studies evaluated more objectively easy-to-apply gait measures by comparing these standardized assessments with patients' self-perceived impairment and clinically established scores. Therefore, the aim of this study was to correlate functional gait measures with self-rating questionnaires for fear of falling and QoL, and with the SPRS as clinical gold standard. METHODS: HSP patients ("pure" phenotype, n = 22) fulfilling the clinical diagnostic criteria for HSP and age-and gender-matched healthy subjects (n = 22) were included in this study. Motor impairment was evaluated using the SPRS, fear of falling by the Falls Efficacy Scale-International (FES-I), and QoL by SF-12. Functional gait measures included gait speed and step length (10-meter-walk-test), the Timed up and go test (TUG), and maximum walking distance (2-min-walking-test). RESULTS: Functional gait measures correlated to fear of falling (gait speed: r = -0.726; step length: r = -0.689; TUG: r = 0.721; 2-min: r = -0.709) and the physical component of QoL (gait speed: r = 0.541; step length: r = 0.531; TUG: r = -0.512; 2-min: r = 0.548). Furthermore, FES-I (r = 0.767) and QoL (r = -0.728) correlated with the clinical gold standard (SPRS). Gait measures strongly correlated with SPRS (gait speed: r = -0.787; step length: r = -0.821; TUG: r = 0.756; 2-min: r = -0.791). CONCLUSION: Functional gait measures reflect fear of falling, QoL, and mobility in HSP. The metric, semi-quantitative gait measures complement the clinician's evaluation and support the clinical workup by more objective parameters.

Consensus based framework for digital mobility monitoring.

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.

Human alpha-synuclein overexpressing MBP29 mice mimic functional and structural hallmarks of the cerebellar subtype of multiple system atrophy.

Multiple system atrophy (MSA) is a rare, but fatal atypical parkinsonian disorder. The prototypical pathological hallmark are oligodendroglial cytoplasmic inclusions (GCIs) containing alpha-synuclein (α-syn). Currently, two MSA phenotypes are classified: the parkinsonian (MSA-P) and the cerebellar subtype (MSA-C), clinically characterized by predominant parkinsonism or cerebellar ataxia, respectively. Previous studies have shown that the transgenic MSA mouse model overexpressing human α-syn controlled by the oligodendroglial myelin basic protein (MBP) promoter (MBP29-hα-syn mice) mirrors crucial characteristics of the MSA-P subtype. However, it remains elusive, whether this model recapitulates important features of the MSA-C-related phenotype. First, we examined MSA-C-associated cerebellar pathology using human post-mortem tissue of MSA-C patients and controls. We observed the prototypical GCI pathology and a preserved number of oligodendrocytes in the cerebellar white matter (cbw) accompanied by severe myelin deficit, microgliosis, and a profound loss of Purkinje cells. Secondly, we phenotypically characterized MBP29-hα-syn mice using a dual approach: structural analysis of the hindbrain and functional assessment of gait. Matching the neuropathological features of MSA-C, GCI pathology within the cbw of MBP29-hα-syn mice was accompanied by a severe myelin deficit despite an increased number of oligodendrocytes and a high number of myeloid cells even at an early disease stage. Intriguingly, MBP29-hα-syn mice developed a significant loss of Purkinje cells at a more advanced disease stage. Catwalk XT gait analysis revealed decreased walking speed, increased stride length and width between hind paws. In addition, less dual diagonal support was observed toward more dual lateral and three paw support. Taken together, this wide-based and unsteady gait reflects cerebellar ataxia presumably linked to the cerebellar pathology in MBP29-hα-syn mice. In conclusion, the present study strongly supports the notion that the MBP29-hα-syn mouse model mimics important characteristics of the MSA-C subtype providing a powerful preclinical tool for evaluating future interventional strategies.