On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial.

BACKGROUND: Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode. OBJECTIVES: We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states. METHOD: In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG. RESULTS: DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67. CONCLUSIONS: DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.

Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.

BACKGROUND: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS: Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS: For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION: A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.

Split-Belt Treadmill Training to Improve Gait Adaptation in Parkinson's Disease.

BACKGROUND: Gait deficits in people with Parkinson's disease (PD) are triggered by circumstances requiring gait adaptation. The effects of gait adaptation training on a split-belt treadmill (SBT) are unknown in PD. OBJECTIVE: We investigated the effects of repeated SBT versus tied-belt treadmill (TBT) training on retention and automaticity of gait adaptation and its transfer to over-ground walking and turning. METHODS: We recruited 52 individuals with PD, of whom 22 were freezers, in a multi-center randomized single-blind controlled study. Training consisted of 4 weeks of supervised treadmill training delivered three times per week. Tests were conducted pre- and post-training and at 4-weeks follow-up. Turning (primary outcome) and gait were assessed over-ground and during a gait adaptation protocol on the treadmill. All tasks were performed with and without a cognitive task. RESULTS: We found that SBT-training improved gait adaptation with moderate to large effects sizes (P < 0.02) compared to TBT, effects that were sustained at follow-up and during dual tasking. However, better gait adaptation did not transfer to over-ground turning speed. In both SBT- and TBT-arms, over-ground walking and Movement Disorder Society-Unified Parkinson's Disease Rating Scale III (MDS-UPDRS-III scores were improved, the latter of which reached clinically meaningful effects in the SBT-group only. No impact was found on freezing of gait. CONCLUSION: People with PD are able to learn and retain the ability to overcome asymmetric gait-speed perturbations on a treadmill remarkably well, but seem unable to generalize these skills to asymmetric gait off-treadmill. Future study is warranted into gait adaptation training to boost the transfer of complex walking skills. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Accuracy of consumer-based activity trackers as measuring tool and coaching device in breast and colorectal cancer survivors.

PURPOSE: Consumer-based activity trackers are used to measure and promote PA. We studied the accuracy of a wrist- and waist-worn activity tracker in cancer survivors and compared these results to a healthy age-matched control group. METHODS: Twenty-two cancer survivors and 35 healthy subjects wore an activity tracker at the waist and at the wrist combined with a reference activity monitor at the waist (Dynaport Movemonitor). The devices were worn for 14 consecutive days. The mean daily step count from both activity trackers was compared with the reference activity monitor to investigate accuracy and agreement (paired t-test, intraclass correlation, Bland-Altman plots). To evaluate the accuracy as a coaching tool, day-by-day differences within patients were calculated. The Kendall correlation coefficient was used to test the consistency of ranking daily steps between the activity trackers and the reference activity monitor. RESULTS: The wrist-worn wearable significantly overestimated the daily step count in the cancer group (mean ± SDΔ: + 1305 (2685) steps per day; p = 0.033) and in the healthy control group (mean ± SDΔ: + 1598 (2927) steps per day; p = 0.003). The waist-worn wearable underestimated the step count in both groups, although this was not statistically significant. As a coaching device, moderate (r = 0.642-0.670) and strong (r = 0.733-0.738) accuracy was found for the wrist- and waist-worn tracker, respectively, for detecting day-by-day variability in both populations. CONCLUSION: Our results show that wrist-worn activity trackers significantly overestimate daily step count in both cancer survivors and healthy control subjects. Based on the accuracy, in particular, the waist-worn activity tracker could possibly be used as a coaching tool.

Validation of Commercial Activity Trackers in Everyday Life of People with Parkinson's Disease.

Maintaining physical activity is an important clinical goal for people with Parkinson's disease (PwPD). We investigated the validity of two commercial activity trackers (ATs) to measure daily step counts. We compared a wrist- and a hip-worn commercial AT against the research-grade Dynaport Movemonitor (DAM) during 14 days of daily use. Criterion validity was assessed in 28 PwPD and 30 healthy controls (HCs) by a 2 × 3 ANOVA and intraclass correlation coefficients (ICC2,1). The ability to measure daily step fluctuations compared to the DAM was studied by a 2 × 3 ANOVA and Kendall correlations. We also explored compliance and user-friendliness. Both the ATs and the DAM measured significantly fewer steps/day in PwPD compared to HCs (p < 0.01). Step counts derived from the ATs showed good to excellent agreement with the DAM in both groups (ICC2,1 > 0.83). Daily fluctuations were detected adequately by the ATs, showing moderate associations with DAM-rankings. While compliance was high overall, 22% of PwPD were disinclined to use the ATs after the study. Overall, we conclude that the ATs had sufficient agreement with the DAM for the purpose of promoting physical activity in mildly affected PwPD. However, further validation is needed before clinical use can be widely recommended.

Dual Task Turning in Place: A Reliable, Valid, and Responsive Outcome Measure of Freezing of Gait.

BACKGROUND: Freezing of gait (FOG) is a complex symptom in Parkinson's disease (PD) that is both elusive to elicit and varied in its presentation. These complexities present a challenge to measuring FOG in a sensitive and reliable way, precluding therapeutic advancement. OBJECTIVE: We investigated the reliability, validity, and responsiveness of manual video annotations of the turning-in-place task and compared it to the sensor-based FOG ratio. METHODS: Forty-five optimally medicated people with PD and FOG performed rapid alternating 360° turns without and with an auditory stroop dual task, thrice over two consecutive days. The tasks were video recorded, and inertial sensors were placed on the lower back and shins. Interrater reliability between three raters, criterion validity with self-reported FOG, and responsiveness to single-session split-belt treadmill (SBT) training were investigated and contrasted with the sensor-based FOG ratio. RESULTS: Visual ratings showed excellent agreement between raters for the percentage time frozen (%TF) (ICC [intra-class correlation coefficient] = 0.99), the median duration of a FOG episode (ICC = 0.90), and the number of FOG episodes (ICC = 0.86). Dual tasking improved the sensitivity and validity of visual FOG ratings resulting in increased FOG detection, criterion validity with self-reported FOG ratings, and responsiveness to a short SBT intervention. The sensor-based FOG ratio, on the contrary, showed complex FOG presentation-contingent relationships with visual and self-reported FOG ratings and limited responsiveness to SBT training. CONCLUSIONS: Manual video annotations of FOG during dual task turning in place generate reliable, valid, and sensitive outcomes for investigating therapeutic effects on FOG. © 2021 International Parkinson and Movement Disorder Society.

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks.

BACKGROUND: Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. METHODS: Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. RESULTS: The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. CONCLUSIONS: The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.

Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation.

BACKGROUND: Although deep neural networks (DNNs) are showing state of the art performance in clinical gait analysis, they are considered to be black-box algorithms. In other words, there is a lack of direct understanding of a DNN's ability to identify relevant features, hindering clinical acceptance. Interpretability methods have been developed to ameliorate this concern by providing a way to explain DNN predictions. METHODS: This paper proposes the use of an interpretability method to explain DNN decisions for classifying the movement that precedes freezing of gait (FOG), one of the most debilitating symptoms of Parkinson's disease (PD). The proposed two-stage pipeline consists of (1) a convolutional neural network (CNN) to model the reduction of movement present before a FOG episode, and (2) layer-wise relevance propagation (LRP) to visualize the underlying features that the CNN perceives as important to model the pathology. The CNN was trained with the sagittal plane kinematics from a motion capture dataset of fourteen PD patients with FOG. The robustness of the model predictions and learned features was further assessed on fourteen PD patients without FOG and fourteen age-matched healthy controls. RESULTS: The CNN proved highly accurate in modelling the movement that precedes FOG, with 86.8% of the strides being correctly identified. However, the CNN model was unable to model the movement for one of the seven patients that froze during the protocol. The LRP interpretability case study shows that (1) the kinematic features perceived as most relevant by the CNN are the reduced peak knee flexion and the fixed ankle dorsiflexion during the swing phase, (2) very little relevance for FOG is observed in the PD patients without FOG and the healthy control subjects, and (3) the poor predictive performance of one subject is attributed to the patient's unique and severely flexed gait signature. CONCLUSIONS: The proposed pipeline can aid clinicians in explaining DNN decisions in clinical gait analysis and aid machine learning practitioners in assessing the generalization of their models by ensuring that the predictions are based on meaningful kinematic features.

Determinants of Dual-Task Training Effect Size in Parkinson Disease: Who Will Benefit Most?

BACKGROUND AND PURPOSE: Dual-task interventions show positive effects in people with Parkinson disease (PD), but it remains unclear which factors determine the size of these benefits. As a secondary analysis of the DUALITY trial, the aim of this study was to assess the determinants of the effect size after 2 types of dual-task practice. METHODS: We randomly allocated 121 participants with PD to receive either integrated or consecutive dual-task training. Dual-task walking performance was assessed during (i) a backward digit span task (digit), (ii) an auditory Stroop task (Stroop), and (iii) a functional mobile phone task. Baseline descriptive, motor, and cognitive variables were correlated with the change in dual-task gait velocity after the intervention. Factors correlated with the change in dual-task gait velocity postintervention (P < 0.20) were entered into a stepwise forward multiple linear regression model. RESULTS: Lower dual-task gait velocity and higher cognitive capacity (Scales for Outcomes in Parkinson's Disease-Cognition [ScopaCog]) at baseline were related to larger improvements in dual-task gait velocity after both integrated and consecutive dual-task training for all 3 tasks (ß[gait] = -0.45, ß[ScopaCog] = 0.34, R = 0.23, P < 0.001, for digit; ß[gait] = -0.52, ß[ScopaCog] = 0.29, R = 0.26, P < 0.001, for Stroop; and ß[gait] = -0.40, ß[ScopaCog] = 0.30, R = 0.18, P < 0.001, for mobile phone task). DISCUSSION AND CONCLUSIONS: Participants with PD who showed a slow dual-task gait velocity and good cognitive functioning at baseline benefited most from the dual-task training, irrespective of the type of training and type of dual-task outcome.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A242).

Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults.

BACKGROUND: The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat. RESULTS: Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were - 27 to 27 ms for Stride Time, - 68 to 44 ms for Stance Time, - 31 to 31 ms for Step Time and - 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (≈ 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds. CONCLUSIONS: The TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the "SIAMOC Best Methodological Paper 2017".

Fall-Prone Older People's Attitudes towards the Use of Virtual Reality Technology for Fall Prevention.

BACKGROUND: Virtual reality (VR) technology is a relatively new rehabilitation tool that can deliver a combination of cognitive and motor training for fall prevention. The attitudes of older people to such training are currently unclear. OBJECTIVE: This study aimed to investigate: (1) the attitudes of fall-prone older people towards fall prevention exercise with and without VR; (2) attitudinal changes after intervention with and without VR; and (3) user satisfaction following fall prevention exercise with and without VR. METHODS: A total of 281 fall-prone older people were randomly assigned to an experimental group receiving treadmill training augmented by VR (TT+VR, n = 144) or a control group receiving treadmill training alone (TT, n = 137). Two questionnaires were used to measure (1) attitudes towards fall prevention exercise with and without VR (AQ); and (2) user satisfaction (USQ). AQ was evaluated at baseline and after intervention. USQ was measured after intervention only. RESULTS: The AQ revealed that most participants had positive attitudes towards fall prevention exercise at baseline (82.2%) and after intervention (80.6%; p = 0.144). In contrast, only 53.6% were enthusiastic about fall prevention exercise with VR at baseline. These attitudes positively changed after intervention (83.1%; p < 0.001), and 99.2% indicated that they enjoyed TT+VR. Correlation analyses showed that postintervention attitudes were strongly related to user satisfaction (USQ: r = 0.503; p < 0.001). CONCLUSIONS: Older people's attitudes towards fall prevention exercise with VR were positively influenced by their experience. From the perspective of the user, VR is an attractive training mode, and thus improving service provision for older people is important.

Virtual reality for rehabilitation in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder that is best managed by a combination of medication and regular physiotherapy. In this context, virtual reality (VR) technology is proposed as a new rehabilitation tool with a possible added value over traditional physiotherapy approaches. It potentially optimises motor learning in a safe environment, and by replicating real-life scenarios could help improve functional activities of daily living. OBJECTIVES: The objective of this review was to summarise the current best evidence for the effectiveness of VR interventions for the rehabilitation of people with PD in comparison with 1) active interventions, and 2) passive interventions. Our primary goal was to determine the effect of VR training on gait and balance. Secondary goals included examining the effects of VR on global motor function, activities of daily living, quality of life, cognitive function, exercise adherence, and the occurrence of adverse events. SEARCH METHODS: We identified relevant articles through electronic searches of the Cochrane Movement Disorders Group Trials Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (the Cochrane Library), MEDLINE, Embase, CINAHL, the Physiotherapy Evidence Database (PEDro), online trials registers, and by handsearching reference lists. We carried out all searches up until 26 November 2016. SELECTION CRITERIA: We searched for randomised and quasi-randomised controlled trials of VR exercise interventions in people with PD. We included only trials where motor rehabilitation was the primary goal. DATA COLLECTION AND ANALYSIS: Two review authors independently searched for trials that corresponded to the predefined inclusion criteria. We independently extracted and assessed all data for methodological quality. A third review author was responsible for conflict resolution when required. MAIN RESULTS: We included 8 trials involving 263 people with PD in the review. Risk of bias was unclear or high for all but one of the included studies. Study sample sizes were small, and there was a large amount of heterogeneity between trials with regard to study design and the outcome measures used. As a result, we graded the quality of the evidence as low or very low. Most of the studies intended to improve motor function using commercially available devices, which were compared with physiotherapy. The interventions lasted for between 4 and 12 weeks.In comparison to physiotherapy, VR may lead to a moderate improvement in step and stride length (standardised mean difference (SMD) 0.69, 95% confidence interval (CI) 0.30 to 1.08; 3 studies; 106 participants; low-quality evidence). VR and physiotherapy interventions may have similar effects on gait (SMD 0.20, 95% CI -0.14 to 0.55; 4 studies; 129 participants; low-quality evidence), balance (SMD 0.34, 95% CI -0.04 to 0.71; 5 studies; 155 participants; low-quality evidence), and quality of life (mean difference 3.73 units, 95% CI -2.16 to 9.61; 4 studies; 106 participants). VR interventions did not lead to any reported adverse events, and exercise adherence did not differ between VR and other intervention arms.The evidence available comparing VR exercise with a passive control was more limited. The evidence for the main outcomes of interest was of very low quality due to the very small sample sizes of the two studies available for this comparison. AUTHORS' CONCLUSIONS: We found low-quality evidence of a positive effect of short-term VR exercise on step and stride length. VR and physiotherapy may have similar effects on gait, balance, and quality of life. The evidence available comparing VR with passive control interventions was more limited. Additional high-quality, large-scale studies are needed to confirm these findings.

A wearable system for gait training in subjects with Parkinson's disease.

In this paper, a system for gait training and rehabilitation for Parkinson's disease (PD) patients in a daily life setting is presented. It is based on a wearable architecture aimed at the provision of real-time auditory feedback. Recent studies have, in fact, shown that PD patients can receive benefit from a motor therapy based on auditory cueing and feedback, as happens in traditional rehabilitation contexts with verbal instructions given by clinical operators. To this extent, a system based on a wireless body sensor network and a smartphone has been developed. The system enables real-time extraction of gait spatio-temporal features and their comparison with a patient's reference walking parameters captured in the lab under clinical operator supervision. Feedback is returned to the user in form of vocal messages, encouraging the user to keep her/his walking behavior or to correct it. This paper describes the overall concept, the proposed usage scenario and the parameters estimated for the gait analysis. It also presents, in detail, the hardware-software architecture of the system and the evaluation of system reliability by testing it on a few subjects.

Gait asymmetry and symptom laterality in Parkinson's disease: two of a kind?

BACKGROUND: The laterality of motor symptoms is considered a key feature of Parkinson's disease (PD). Here, we investigated whether gait and turning asymmetry coincided with symptom laterality as determined by the MDS-UPRDS part III and whether it was increased compared to healthy controls (HC). METHODS: We analyzed the asymmetry of gait and turning with and without a cognitive dual task (DT) using motion capture systems and wearable sensors in 97 PD patients mostly from Hoehn & Yahr stage II and III and 36 age-matched HC. We also assessed motor symptom asymmetry using the bilateral sub-items of the MDS-UPDRS-III. Finally, we examined the strength of the association between gait asymmetry and symptom laterality. RESULTS: Participants with PD had increased gait but not more turning asymmetry compared to HC (p < 0.05). Only 53.7% of patients had a shorter step length on the more affected body side as determined by the MDS-UPDRS-III. Also, 54% took more time and 29% more steps during turns toward the more affected side. The degree of asymmetry in the different domains did not correlate with each other and was not influenced by DT-load. CONCLUSIONS: We found a striking mismatch between the side and the degree of asymmetry in different motor domains, i.e., in gait, turning, and distal symptom severity in individuals with PD. We speculate that motor execution in different body parts relies on different neural control mechanisms. Our findings warrant further investigation to understand the complexity of gait asymmetry in PD.

Towards a better understanding of anticipatory postural adjustments in people with Parkinson's disease.

INTRODUCTION: Previous studies have shown that anticipatory postural adjustments (APAs) are altered in people with Parkinson's disease but its meaning for locomotion is less understood. This study aims to investigate the association between APAs and gait initiation, gait and freezing of gait and how a dynamic postural control challenging training may induce changes in these features. METHODS: Gait initiation was quantified using wearable sensors and subsequent straight walking was assessed via marker-based motion capture. Additionally, turning and FOG-related outcomes were measured with wearable sensors. Assessments were conducted one week before (Pre), one week after (Post) and 4 weeks after (Follow-up) completion of a training intervention (split-belt treadmill training or regular treadmill training), under single task and dual task (DT) conditions. Statistical analysis included a linear mixed model for training effects and correlation analysis between APAs and the other outcomes for Pre and Post-Pre delta. RESULTS: 52 participants with Parkinson's disease (22 freezers) were assessed. We found that APA size in the medio-lateral direction during DT was positively associated with gait speed (p<0.001) and stride length (p<0.001) under DT conditions at Pre. The training effect was largest for first step range of motion and was similar for both training modes. For the associations between changes after the training (pooled sample) medio-lateral APA size showed a significant positive correlation with first step range of motion (p = 0.033) only in the DT condition and for the non-freezers only. CONCLUSIONS: The findings of this work revealed new insights into how APAs were not associated with first step characteristics and freezing and only baseline APAs during DT were related with DT gait characteristics. Training-induced changes in the size of APAs were related to training benefits in the first step ROM only in non-freezers. Based on the presented results increasing APA size through interventions might not be the ideal target for overall improvement of locomotion.

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects.

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

Daily-Living Freezing of Gait as Quantified Using Wearables in People With Parkinson Disease: Comparison With Self-Report and Provocation Tests.

OBJECTIVE: Freezing of gait (FOG) is an episodic, debilitating phenomenon that is common among people with Parkinson disease. Multiple approaches have been used to quantify FOG, but the relationships among them have not been well studied. In this cross-sectional study, we evaluated the associations among FOG measured during unsupervised daily-living monitoring, structured in-home FOG-provoking tests, and self-report. METHODS: Twenty-eight people with Parkinson disease and FOG were assessed using self-report questionnaires, percentage of time spent frozen (%TF) during supervised FOG-provoking tasks in the home while off and on dopaminergic medication, and %TF evaluated using wearable sensors during 1 week of unsupervised daily-living monitoring. Correlations between those 3 assessment approaches were analyzed to quantify associations. Further, based on the %TF difference between in-home off-medication testing and in-home on-medication testing, the participants were divided into those responding to Parkinson disease medication (responders) and those not responding to Parkinson disease medication (nonresponders) in order to evaluate the differences in the other FOG measures. RESULTS: The %TF during unsupervised daily living was mild to moderately correlated with the %TF during a subset of the tasks of the in-home off-medication testing but not the on-medication testing or self-report. Responders and nonresponders differed in the %TF during the personal "hot spot" task of the provoking protocol while off medication (but not while on medication) but not in the total scores of the self-report questionnaires or the measures of FOG evaluated during unsupervised daily living. CONCLUSION: The %TF during daily living was moderately related to FOG during certain in-home FOG-provoking tests in the off-medication state. However, this measure of FOG was not associated with self-report or FOG provoked in the on-medication state. These findings suggest that to fully capture FOG severity, it is best to assess FOG using a combination of all 3 approaches. IMPACT: These findings suggest that several complementary approaches are needed to provide a complete assessment of FOG severity.

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.

Repeated Gait Perturbation Training in Parkinson's Disease and Healthy Older Adults: A Systematic Review and Meta-Analysis.

Background: Gait impairments are common in healthy older adults (HOA) and people with Parkinson's disease (PwPD), especially when adaptations to the environment are required. Traditional rehabilitation programs do not typically address these adaptive gait demands in contrast to repeated gait perturbation training (RGPT). RGPT is a novel reactive form of gait training with potential for both short and long-term consolidation in HOA and PwPD. The aim of this systematic review with meta-analysis is to determine whether RGPT is more effective than non-RGPT gait training in improving gait and balance in HOA and PwPD in the short and longer term. Methods: This review was conducted according to the PRISMA-guidelines and pre-registered in the PROSPERO database (CRD42020183273). Included studies tested the effects of any form of repeated perturbations during gait in HOA and PwPD on gait speed, step or stride length. Studies using balance scales or sway measures as outcomes were included in a secondary analysis. Effects of randomized controlled trials (RCT) on RGPT were pooled using a meta-analysis of final measures. Results: Of the 4421 studies, eight studies were deemed eligible for review, of which six could be included in the meta-analysis, totaling 209 participants (159 PwPD and 50 HOA). The studies were all of moderate quality. The meta-analysis revealed no significant effects of RGPT over non-RGPT training on gait performance (SMD = 0.16; 95% CI = -0.18, 0.49; Z = 0.92; P = 0.36). Yet, in some individual studies, favorable effects on gait speed, step length and stride length were observed immediately after the intervention as well as after a retention period. Gait variability and asymmetry, signifying more direct outcomes of gait adaptation, also indicated favorable RGPT effects in some individual studies. Conclusion: Despite some promising results, the pooled effects of RGPT on gait and balance were not significantly greater as compared to non-RGPT gait training in PwPD and HOA. However, these findings could have been driven by low statistical power. Therefore, the present review points to the imperative to conduct sufficiently powered RCT's to verify the true effects of RGPT on gait and balance in HOA and PwPD. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php? Identifier: CRD42020183273.

A systematic review on exercise and training-based interventions for freezing of gait in Parkinson's disease.

Freezing of gait (FOG) in Parkinson's disease (PD) causes severe patient burden despite pharmacological management. Exercise and training are therefore advocated as important adjunct therapies. In this meta-analysis, we assess the existing evidence for such interventions to reduce FOG, and further examine which type of training helps the restoration of gait function in particular. The primary meta-analysis across 41 studies and 1838 patients revealed a favorable moderate effect size (ES = -0.37) of various training modalities for reducing subjective FOG-severity (p < 0.00001), though several interventions were not directly aimed at FOG and some included non-freezers. However, exercise and training also proved beneficial in a secondary analysis on freezers only (ES = -0.32, p = 0.007). We further revealed that dedicated training aimed at reducing FOG episodes (ES = -0.24) or ameliorating the underlying correlates of FOG (ES = -0.40) was moderately effective (p < 0.01), while generic exercises were not (ES = -0.14, p = 0.12). Relevantly, no retention effects were seen after cessation of training (ES = -0.08, p = 0.36). This review thereby supports the implementation of targeted training as a treatment for FOG with the need for long-term engagement.