How to Select Balance Measures Sensitive to Parkinson's Disease from Body-Worn Inertial Sensors-Separating the Trees from the Forest

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review

BACKGROUND

The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate is still significant. Low in-vivo diagnostic accuracy of clinical evaluation mainly relies on the lack of quantitative biomarkers for an objective motor performance assessment. Non-invasive technologies, such as wearable sensors, coupled with machine learning algorithms, assess quantitatively and objectively the motor performances, with possible benefits either for in-clinic and at-home settings. We conducted a systematic review of the literature on machine learning algorithms embedded in smart devices in Parkinson's disease diagnosis.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched PubMed for articles published between December, 2007 and July, 2023, using a search string combining "Parkinson's disease" AND ("healthy" or "control") AND "diagnosis", within the Groups and Outcome domains. Additional search terms included "Algorithm", "Technology" and "Performance".

RESULTS

From 89 identified studies, 47 met the inclusion criteria based on the search string and four additional studies were included based on the Authors' expertise. Gait emerged as the most common parameter analysed by machine learning models, with Support Vector Machines as the prevalent algorithm. The results suggest promising accuracy with complex algorithms like Random Forest, Support Vector Machines, and K-Nearest Neighbours.

DISCUSSION

Despite the promise shown by machine learning algorithms, real-world applications may still face limitations. This review suggests that integrating machine learning with wearable sensors has the potential to improve Parkinson's disease diagnosis. These tools could provide clinicians with objective data, potentially aiding in earlier detection.

The Effect of taVNS at 25 Hz and 100 Hz on Parkinson's Disease Gait-A Randomized Motion Sensor Study

BACKGROUND

Transcutaneous electrostimulation of the auricular branch of the vagal nerve (taVNS) has the propensity to reach diffuse neuromodulatory networks, which are dysfunctional in Parkinson's disease (PD). Previous studies support the use of taVNS as an add-on treatment for gait in PD.

OBJECTIVES

We assessed the effect of taVNS at 25 Hz (taVNS25), taVNS at 100 Hz (taVNS100), and sham earlobe stimulation (sVNS) on levodopa responsive (arm swing velocity, arm range of motion, stride length, gait speed) and non-responsive gait characteristics (arm range of motion asymmetry, anticipatory postural adjustment [APA] duration, APA first step duration, APA first step range of motion), and turns (first turn duration, double 360° turn duration, steps per turn) in advanced PD.

METHODS

In our double blind sham controlled within-subject randomized trial, we included 30 PD patients (modified Hoehn and Yahr stage, 2.5-4) to assess the effect of taVNS25, taVNS100, and sVNS on gait characteristics measured with inertial motion sensors during the instrumented stand and walk test and a double 360° turn. Separate generalized mixed models were built for each gait characteristic.

RESULTS

During taVNS100 compared to sVNS arm swing velocity (P = 0.030) and stride length increased (P = 0.027), and APA duration decreased (P = 0.050). During taVNS25 compared to sVNS stride length (P = 0.024) and gait speed (P = 0.021) increased and double 360° turn duration decreased (P = 0.039).

CONCLUSIONS

We have found that taVNS has a frequency specific propensity to improve stride length, arm swing velocity, and gait speed and double 360° turn duration in PD patients. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study over 12 months

Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.

Telerehabilitation by Videoconferencing for Balance and Gait in People with Parkinson's Disease: A Scoping Review

Although supervised and real-time telerehabilitation by videoconferencing is now becoming common for people with Parkinson's disease (PD), its efficacy for balance and gait is still unclear. This paper uses a scoping approach to review the current evidence on the effects of telerehabilitation by videoconferencing on balance and gait for patients with PD. We also explored whether studies have used wearable technology during telerehabilitation to assess and treat balance and gait via videoconferencing. Literature searches were conducted using PubMed, ISI's Web of Knowledge, Cochrane's Library, and Embase. The data were extracted for study design, treatment, and outcomes. Fourteen studies were included in this review. Of these, seven studies investigated the effects of telerehabilitation (e.g., tele-yoga and adapted physiotherapy exercises) on balance and gait measures (e.g., self-reported balance, balance scale, walking speed, mobility, and motor symptoms) using videoconferencing in both assessment and treatment. The telerehabilitation programs by videoconferencing were feasible and safe for people with PD; however, the efficacy still needs to be determined, as only four studies had a parallel group. In addition, no study used wearable technology. Robust evidence of the effects of telerehabilitation by videoconferencing on balance and gait for patients with PD was not found, suggesting that future powered, prospective, and robust clinical trials are needed.

Exploring inertial sensor-based balance biomarkers for early detection of mild cognitive impairment

Dementia is characterized by a progressive loss of cognitive abilities, and diagnosing its early stages Mild Cognitive Impairment (MCI), is difficult since it is a transitory state that is different from total cognitive collapse. Recent clinical research studies have identified that balance impairments can be a significant indicator for predicting dementia in older adults. Accordingly, the current research focuses on finding innovative postural balance-based digital biomarkers by using wearable inertial sensors and pre-screening of MCI in home settings using machine learning techniques. For this research, sixty subjects (30 cognitively normal and 30 MCI) with waist-mounted inertial sensor performed balance tasks in four different standing postures: eyes-open, eyes-closed, right-leg-lift, and left-leg-lift. The significant balance biomarkers for MCI identification are discovered by our research, demonstrating specific characteristics in each of these four states. A robust feature selection approach is ensured by the multi-step methodology that combines the strengths of Filter techniques, Wrapper methods, and SHAP (Shapley Additive exPlanations) technique. The proposed balance biomarkers have the potential to detect MCI (with 75.8% accuracy), as evidenced by the results of machine learning algorithms for classification. This work adds to the growing body of literature targeted at enhancing understanding and proactive management of cognitive loss in older populations and lays the groundwork for future research efforts aimed at refining digital biomarkers, validating findings, and exploring longitudinal perspectives.

Potential Prodromal Digital Postural Sway Markers for Fragile X-Associated Tremor/Ataxia Syndrome (FXTAS) Detected via Dual-Tasking and Sensory Manipulation

FXTAS is a neurodegenerative disorder occurring in some Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene premutation carriers (PMCs) and is characterized by cerebellar ataxia, tremor, and cognitive deficits that negatively impact balance and gait and increase fall risk. Dual-tasking (DT) cognitive-motor paradigms and challenging balance conditions may have the capacity to reveal markers of FXTAS onset. Our objectives were to determine the impact of dual-tasking and sensory and stance manipulation on balance in FXTAS and potentially detect subtle postural sway deficits in FMR1 PMCs who are asymptomatic for signs of FXTAS on clinical exam. Participants with FXTAS, PMCs without FXTAS, and controls underwent balance testing using an inertial sensor system. Stance, vision, surface stability, and cognitive demand were manipulated in 30 s trials. FXTAS participants had significantly greater total sway area, jerk, and RMS sway than controls under almost all balance conditions but were most impaired in those requiring vestibular control. PMCs without FXTAS had significantly greater RMS sway compared with controls in the feet apart, firm, single task conditions both with eyes open and closed (EC) and the feet together, firm, EC, DT condition. Postural sway deficits in the RMS postural sway variability domain in asymptomatic PMCs might represent prodromal signs of FXTAS. This information may be useful in providing sensitive biomarkers of FXTAS onset and as quantitative balance measures in future interventional trials and longitudinal natural history studies.

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.

Identification of motor progression in Parkinson's disease using wearable sensors and machine learning

Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.

Digital measures of freezing of gait across the spectrum of normal, non-freezers, possible freezers and definite freezers

Over the course of the disease, freezing of gait (FoG) will gradually impact over 80% of people with Parkinson's disease (PD). Clinical decision-making and research design are often based on classification of patients as 'freezers' or 'non-freezers'. We derived an objective measure of FoG severity from inertial sensors on the legs to examine the continuum of FoG from absent to possible and severe in people with PD and in healthy controls. One hundred and forty-seven people with PD (Off-medication) and 83 healthy control subjects turned 360° in-place for 1 minute while wearing three wearable sensors used to calculate a novel Freezing Index. People with PD were classified as: 'definite freezers', new FoG questionnaire (NFOGQ) score > 0 and clinically observed FoG; 'non-freezers', NFOGQ = 0 and no clinically observed FoG; and 'possible freezers', either NFOGQ > 0 but no FoG observed or NFOGQ = 0 but FoG observed. Linear mixed models were used to investigate differences in participant characteristics among groups. The Freezing Index significantly increased from healthy controls to non-freezers to possible freezers and to definite freezers and showed, in average, excellent test-retest reliability (ICC = 0.89). Unlike the Freezing Index, sway, gait and turning impairments were similar across non-freezers, possible and definite freezers. The Freezing Index was significantly related to NFOG-Q, disease duration, severity, balance confidence, and the SCOPA-Cog (p < 0.01). An increase in the Freezing Index, objectively assessed with wearable sensors during a turning- in-place test, may help identify prodromal FoG in people with PD prior to clinically-observable or patient-perceived freezing. Future work should follow objective measures of FoG longitudinally.

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease

Although the multifactorial nature of falls in Parkinson's disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores.

METHODS

Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months' falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner).

RESULTS

Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85).

CONCLUSION

Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Outcome Measures for Disease-Modifying Trials in Parkinson's Disease: Consensus Paper by the EJS ACT-PD Multi-Arm Multi-Stage Trial Initiative

BACKGROUND

Multi-arm, multi-stage (MAMS) platform trials can accelerate the identification of disease-modifying treatments for Parkinson's disease (PD) but there is no current consensus on the optimal outcome measures (OM) for this approach.

OBJECTIVE

To provide an up-to-date inventory of OM for disease-modifying PD trials, and a framework for future selection of OM for such trials.

METHODS

As part of the Edmond J Safra Accelerating Clinical Trials in Parkinson Disease (EJS ACT-PD) initiative, an expert group with Patient and Public Involvement and Engagement (PPIE) representatives' input reviewed and evaluated available evidence on OM for potential use in trials to delay progression of PD. Each OM was ranked based on aspects such as validity, sensitivity to change, participant burden and practicality for a multi-site trial. Review of evidence and expert opinion led to the present inventory.

RESULTS

An extensive inventory of OM was created, divided into: general, motor and non-motor scales, diaries and fluctuation questionnaires, cognitive, disability and health-related quality of life, capability, quantitative motor, wearable and digital, combined, resource use, imaging and wet biomarkers, and milestone-based. A framework for evaluation of OM is presented to update the inventory in the future. PPIE input highlighted the need for OM which reflect their experience of disease progression and are applicable to diverse populations and disease stages.

CONCLUSION

We present a range of OM, classified according to a transparent framework, to aid selection of OM for disease-modifying PD trials, whilst allowing for inclusion or re-classification of relevant OM as new evidence emerges.

Freezing of Gait in Parkinson's Disease: Implications for Dual-Task Walking

BACKGROUND

The simultaneous completion of multiple tasks (dual-tasking, DT) often leads to poorer task performance (DT cost, DTC). People with Parkinson's disease (PwPD) exhibit difficulty with DT, and DTC may be particularly pronounced in PwPD with freezing of gait (FOG).

OBJECTIVE

This study assessed the relationship between FOG status and DTC during gait.

METHODS

Gait parameters were collected using inertial sensors in 106 PwPD (off-medication), including definite-freezers (dFOG; n = 25), possible-freezers (pFOG; n = 16), and non-freezers (nFOG; n = 65) during single (ST)-and DT walking.

RESULTS

PwPD with dFOG had larger (worse) DTC than nFOG for foot-strike angle, stride length, toe-off angle, variability of foot-strike angle, and arm range of motion (ROM). After accounting for covariates, DTC for toe-off angle and stride length remained worse in PwPD who freeze. Worse cognition predicted larger DTC for stride length, gait cycle duration, gait speed, and step duration across groups. Men had larger DTC compared to women for gait speed, variability in foot-strike angle, stride length, and arm ROM. Increased variability in gait speed DTC was associated with increased disease severity.

CONCLUSION

These findings provide additional support that PwPD who freeze may rely on greater cortical control for the execution of specific gait metrics. The results also underscore the importance of considering cognition when assessing DT ability in PwPD.

Postural control of Parkinson's disease: A visualized analysis based on Citespace knowledge graph

Postural control impairment is one of the primary motor symptoms in patients with Parkinson's disease, leading to an increased risk of falling. Several studies have been conducted on postural control disorders in Parkinson's disease patients, but no relevant bibliometric analysis has been found. In this paper, the Web of Science Core Collection database was searched for 1,295 relevant papers on postural control in Parkinson's disease patients from December 2011 to December 2021. Based on the Citespace knowledge graph, these relevant papers over the last decade were analyzed from the perspectives of annual publication volume, countries and institutes cooperation, authors cooperation, dual-map overlay of journals, co-citation literature, and keywords. The purpose of this study was to explore the current research status, research hotspots, and frontiers in this field, and to provide a reference for further promoting the research on postural control in Parkinson's disease patients.

Feasibility of a Novel Therapist-Assisted Feedback System for Gait Training in Parkinson's Disease

We tested the feasibility of one session of treadmill training using a novel physical therapist assisted system (Mobility Rehab) using wearable sensors on the upper and lower limbs of 10 people with Parkinson's disease (PD). Participants performed a 2-min walk overground before and after 15 min of treadmill training with Mobility Rehab, which included an electronic tablet (to visualize gait metrics) and five Opal sensors placed on both the wrists and feet and on the sternum area to measure gait and provide feedback on six gait metrics (foot-strike angle, trunk coronal range-of-motion (ROM), arm swing ROM, double-support duration, gait-cycle duration, and step asymmetry). The physical therapist used Mobility Rehab to select one or two gait metrics (from the six) to focus on during the treadmill training. Foot-strike angle (effect size (ES) = 0.56, 95% Confidence Interval (CI) = 0.14 to 0.97), trunk coronal RoM (ES = 1.39, 95% CI = 0.73 to 2.06), and arm swing RoM (ES = 1.64, 95% CI = 0.71 to 2.58) during overground walking showed significant and moderate-to-large ES following treadmill training with Mobility Rehab. Participants perceived moderate (60%) and excellent (30%) effects of Mobility Rehab on their gait. No adverse events were reported. One session of treadmill training with Mobility Rehab is feasible for people with mild-to-moderate PD.

Resting state functional networks predict different aspects of postural control in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disorder causing postural control impairments. Postural control involves multiple domains, such as control of postural sway in stance, automatic postural responses (APRs) and anticipatory postural adjustments (APAs). We hypothesize that impairments in each postural domain is associated with resting-state functional connectivity (rsFC), accounted by predictive modeling and that cortical and cerebellar networks would predict postural control in people with PD (PwPD).

OBJECTIVE

To determine whether rsFC can predict three domains of postural control independently in PwPD and older adults (OA) based on predictive accuracy of models.

METHODS

The cohort consisted of 65 PwPD (67.7 +8.1 age) tested in their OFF-state and 42 OA (69.7 +8.2 age). Six body-worn, inertial sensors measured postural sway area while standing on foam, step length of APRs to a backward push-and-release perturbation, and magnitude of lateral APAs prior to voluntary gait initiation. Resting state-fMRI data was reported on 384 regions of interest that were grouped into 13 functional brain networks. Associations between rsFC and postural metrics were characterized using predictive modeling, with an independent training (n = 67) and validation (n = 40) dataset. Models were trained in the training sample and performance of the best model was validated in the independent test dataset.

RESULTS

rsFC of different brain networks predicted each domain of postural control in PD: Frontoparietal and Ventral Attention rsFC for APAs; Cerebellar-Subcortical and Visual rsFC and Auditory and Cerebellar-Subcortical rsFC for APRs; Ventral Attention and Ventral Multimodal rsFC for postural sway. In OA, CinguloOpercular and Somatomotor rsFC predicted APAs.

CONCLUSIONS

Our findings suggest that cortical networks predict postural control in PD and there is little overlap in brain network connectivities that predict different domains of postural control, given the rsFC methodology used. PwPD use different cortical networks for APAs compared to OA.

Validity and reliability of a smartphone-based assessment for anticipatory and compensatory postural adjustments during predictable perturbations

BACKGROUND

Postural adjustments involve displacements of the center of mass (COM), controlled by the central nervous system (CNS), to maintain equilibrium whilst standing. Postural adjustments can be anticipatory (APAs) or compensatory (CPAs), and are triggered to counteract predictable perturbations.

RESEARCH QUESTION

Is the new smartphone application, Momentum, a valid and reliable tool for the assessment of body balance, by measuring APAs and CPAs using accelerometer readings?

METHODS

20 young adults were exposed to external predictable perturbations induced at the shoulder level, whilst standing. COM linear acceleration was recorded by Momentum (extracting data from a smartphone's accelerometer) and a 3D motion capture system.

RESULTS

The key results demonstrated a very high, significant correlation (r ≥ 0.7, p < 0.05) between the two device settings in the APA parameters_, which obtained r = 0.65, denoting a high correlation. Considering the reliability, variables that are compensatory in nature are presented on a scale of good to excellent in measurement methods, kinematics, and Momentum. However, the anticipatory variables presented excellent reliability only for the kinematics.

SIGNIFICANCE

These experiments show that Momentum is a valid method for measuring COM acceleration under predictable perturbations and is reliable for compensatory events.

Does visual cueing improve gait initiation in people with Parkinson's disease?

Anticipatory postural adjustments (APAs) prior to gait initiation are impaired in people with Parkinson's disease (PD), particularly in those who report Freezing of Gait (FOG). External cues can improve gait parameters in people with PD, but the effects of visual cues on gait initiation are poorly known. The study aimed to (i) assess differences, during gait initiation, between people with PD with (FOG+) and without FOG (FOG-) and healthy controls (HC), (ii) explore the effect of disease severity on gait initiation and (iii) investigate the acute effect of visual cueing on gait initiation and straight-ahead gait. Twenty FOG- and twenty FOG+, and eighteen HC participated in this study. Participants were asked to perform self-initiated gait with and without visual cues presented as transverse taped lines on the floor. Gait initiation and gait were characterized with wireless inertial measurement units. Results showed that FOG+ had smaller APAs than HC and FOG-; although no differences were detected between FOG+ and FOG- when taking into account disease severity. Significant correlations between MDS-UPDRS III scores and gait initiation/straight-ahead gait variables confirmed that differences between FOG+ and FOG- were driven by disease severity. In gait initiation, visual cues elicited different behaviors in people with and without PD. Particularly, people with PD showed smaller and longer APAs, whereas HC showed longer first step durations, compared to baseline. However, the adopted visual cues improved gait speed and stride length in all individuals. These results suggest that people with PD, despite the presence of FOG, utilize different motor strategies, compared to HC, to adapt to the new biomechanical requirements of gait initiation dictated by the visual cues.

Wearable-Based Assessment of Frailty Trajectories During Cardiac Rehabilitation After Open-Heart Surgery

Frailty in patients after open-heart surgery influences the type and intensity of a cardiac rehabilitation program. The response to tailored exercise training can be different, requiring convenient tools to assess the effectiveness of a training program routinely. The study aims to investigate whether kinematic measures extracted from the acceleration signals can provide information about frailty trajectories during rehabilitation. One hundred patients after open-heart surgery, assigned to the equal-sized intervention and control groups, participated in exercise training during inpatient rehabilitation. After rehabilitation, the intervention group continued exercise training at home, whereas the control group was asked to maintain the usual physical activity regimen. Stride time, cadence, movement vigor, gait asymmetry, Lissajous index, and postural sway were estimated during the clinical walk and stair-climbing tests before and after inpatient rehabilitation as well as after home-based exercise training. Frailty was assessed using the Edmonton frail scale. Most kinematic measures estimated during walking improved after rehabilitation along with the improvement in frailty status, i.e., stride time, cadence, postural sway, and movement vigor improved in 71%, 77%, 81%, and 83% of patients, respectively. Meanwhile, kinematic measures during stair-climbing improved to a lesser extent compared to walking. Home-based exercise training did not result in a notable change in kinematic measures which agrees well with only a negligible deterioration in frailty status. The study demonstrates the feasibility to follow frailty trajectories during inpatient rehabilitation after open-heart surgery based on kinematic measures extracted using a single wearable sensor.

Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

With the rapid development and widespread application of wearable inertial sensors in the field of human motion capture, the low-cost and non-invasive accelerometer (ACC) based measures have been widely used for working postural stability assessment. This study systematically investigated the abilities of ACC-based measures to assess the stability of working postures in terms of the ability to detect the effects of work-related factors and the ability to classify stable and unstable working postures. Thirty young males participated in this study and performed twenty-four load-holding tasks (six working postures × two standing surfaces × two holding loads), and forty-three ACC-based measures were derived from the ACC data obtained by using a 17 inertial sensors-based motion capture system. ANOVAs, t-tests and machine learning (ML) methods were adopted to study the factors’ effects detection ability and the postural stability classification ability. The results show that almost all forty-three ACC-based measures could (p < 0.05) detect the main effects of Working Posture and Load Carriage, and their interaction effects. However, most of them failed in (p ≥ 0.05) detecting Standing Surface’s main or interaction effects. Five measures could detect both main and interaction effects of all the three factors, which are recommended for working postural stability assessment. The performance in postural stability classification based on ML was also good, and the feature set exerted a greater influence on the classification accuracy than sensor configuration (i.e., sensor placement locations). The results show that the pelvis and lower legs are recommended locations overall, in which the pelvis is the first choice. The findings of this study have proved that wearable ACC-based measures could assess the stability of working postures, including the work-related factors’ effects detection ability and stable-unstable working postures classification ability. However, researchers should pay more attention to the measure selection, sensors placement, feature selection and extraction in practical applications.

Perspective: Balance Assessments in Progressive Supranuclear Palsy: Lessons Learned

Many studies have examined aspects of balance in progressive supranuclear palsy (PSP), but guidance on the feasibility of standardized objective balance assessments and balance scales in PSP is lacking. Balance tests commonly used in Parkinson's disease often cannot be easily administered or translated to PSP. Here we briefly review methodology in prior studies of balance in PSP; then we focus on feasibility by presenting our experience with objective balance assessment in PSP-Richardson syndrome and PSP-parkinsonism during a crossover rTMS intervention trial. We highlight lessons learned, safety considerations, and future approaches for objective balance assessment in PSP.

Relationship Between Brain Volumes and Objective Balance and Gait Measures in Parkinson's Disease

BACKGROUND

Instrumented measures of balance and gait measure more specific balance and gait impairments than clinical rating scales. No prior studies have used objective balance/gait measures to examine associations with ventricular and brain volumes in people with Parkinson's disease (PD).

OBJECTIVE

To test the hypothesis that larger ventricular and smaller cortical and subcortical volumes are associated with impaired balance and gait in people with PD.

METHODS

Regional volumes from structural brain images were included from 96 PD and 50 control subjects. Wearable inertial sensors quantified gait, anticipatory postural adjustments prior to step initiation (APAs), postural responses to a manual push, and standing postural sway on a foam surface. Multiple linear regression models assessed the relationship between brain volumes and balance/gait and their interactions in PD and controls, controlling for sex, age and corrected for multiple comparisons.

RESULTS

Smaller brainstem and subcortical gray matter volumes were associated with larger sway area in people with PD, but not healthy controls. In contrast, larger ventricle volume was associated with smaller APAs in healthy controls, but not in people with PD. A sub-analysis in PD showed significant interactions between freezers and non-freezers, in several subcortical areas with stride time variability, gait speed and step initiation.

CONCLUSION

Our models indicate that smaller subcortical and brainstem volumes may be indicators of standing balance dysfunction in people with PD whereas enlarged ventricles may be related to step initiation difficulties in healthy aging. Also, multiple subcortical region atrophy may be associated with freezing of gait in PD.

JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease

Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.

Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study

Background

Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD).

Objective

Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC).

Methods

Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects.

Results

Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.

Conclusions

These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications.

Inertial Sensor Reliability and Validity for Static and Dynamic Balance in Healthy Adults: A Systematic Review

Compared to laboratory equipment inertial sensors are inexpensive and portable, permitting the measurement of postural sway and balance to be conducted in any setting. This systematic review investigated the inter-sensor and test-retest reliability, and concurrent and discriminant validity to measure static and dynamic balance in healthy adults. Medline, PubMed, Embase, Scopus, CINAHL, and Web of Science were searched to January 2021. Nineteen studies met the inclusion criteria. Meta-analysis was possible for reliability studies only and it was found that inertial sensors are reliable to measure static standing eyes open. A synthesis of the included studies shows moderate to good reliability for dynamic balance. Concurrent validity is moderate for both static and dynamic balance. Sensors discriminate old from young adults by amplitude of mediolateral sway, gait velocity, step length, and turn speed. Fallers are discriminated from non-fallers by sensor measures during walking, stepping, and sit to stand. The accuracy of discrimination is unable to be determined conclusively. Using inertial sensors to measure postural sway in healthy adults provides real-time data collected in the natural environment and enables discrimination between fallers and non-fallers. The ability of inertial sensors to identify differences in postural sway components related to altered performance in clinical tests can inform targeted interventions for the prevention of falls and near falls.

How many gait initiation trials are necessary to reliably detect anticipatory postural adjustments and first step characteristics in healthy elderly and people with Parkinson's disease?

BACKGROUND

The gait initiation (GI) process can be characterized by anticipatory postural adjustments (APAs) and first step characteristics. However, even within a constrained environment, it is unclear how many trials are necessary to obtain a reliable measurement of the GI process within one assessment.

RESEARCH QUESTION

How many gait initiation trials are necessary to reliably detect APAs and first step characteristics in healthy elderly (HC) and people with Parkinson's disease with Freezing of Gait (PD + FOG) under single (ST) and dual task (DT) conditions and are there any potential systematic errors?

METHODS

Thirty-eight PD + FOG (ON-medication) and 30 HC performed 5 trials of GI under ST and DT (auditory stroop test). APAs and first-step-outcomes were captured with IMUs placed on the lower back and on each foot. Intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM) were computed to investigate reliability and mixed model analysis to find potential systematic errors. Additionally, we computed an estimation for the number of necessary trials to reach acceptable reliability (ICC = 0.75) for each outcome.

RESULTS

ICCs varied from low reliability to excellent reliability across outcomes in PD + FOG and HC. ICCs were comparable under ST and DT for most outcomes. SEM results confirmed the ICC results. A systematic error was found for the first trial in first step ROM. Number of necessary trials varied largely across outcomes.

SIGNIFICANCE

Within-session reliability varied across outcomes but was similar for PD + FOG and HC, and ST and DT. ML size of APA and first step ROM were most reliable, whereas APA duration and latency were least reliable. Depending on the outcome of interest, future studies should conduct multiple trials of GI to increase reliability.

Physical activity thresholds for predicting longitudinal gait decline in adults with knee osteoarthritis

OBJECTIVE

To investigate the longitudinal relationship between physical performance (via real-life accelerometry) and physical capacity (laboratory measurement of gait speed) in patients with knee osteoarthritis (KOA), and to derive accelerometry measured thresholds associated with gait speed decline in KOA that may provide targets for disease-specific physical activity guidelines.

DESIGN

Longitudinal data from the Osteoarthritis Initiative (OAI) accelerometer sub-study was extracted from 1,229 participants assessed 2 years apart. Extracted data include functional capacity, demographic and anthropometric characteristics, patient-reported outcome measures, and accelerometry-based physical activity measures. A "poor capacity" group was defined based on the gait speed quintile decline between baseline and the 2-yr follow-up. A Random Forest classifier was trained to classify individuals' capacity status, and the impact of each extracted factor on the prediction outcome was analyzed using a novel machine learning interpretation algorithm.

RESULTS

The most impactful predicting feature for gait decline is low minutes in the performance of moderate-vigorous activity (count per min 2,500+). Slower sit-to-stand performance, higher age and self-reported knee pain, and lower minutes in performance light activities (count per min 350-2499) also contributed to the model prediction. The overall classification accuracy is 76.3% (75.4% sensitivity, 76.5% specificity).

CONCLUSIONS

We investigated the impact magnitude and direction of each predicting feature on the longitudinal capacity status among KOA patients. Using novel data interpretation method, we established feature thresholds that may increase the probability of gait decline. These identified thresholds may provide meaningful information for establishing specific physical activity guidelines for KOA.

Dual-Task Costs of Quantitative Gait Parameters While Walking and Turning in People with Parkinson's Disease: Beyond Gait Speed

BACKGROUND

There is a lack of recommendations for selecting the most appropriate gait measures of Parkinson's disease (PD)-specific dual-task costs to use in clinical practice and research.

OBJECTIVE

We aimed to identify measures of dual-task costs of gait and turning that best discriminate performance in people with PD from healthy individuals. We also investigated the relationship between the most discriminative measures of dual-task costs of gait and turning with disease severity and disease duration.

METHODS

People with mild-to-moderate PD (n = 144) and age-matched healthy individuals (n = 79) wore 8 inertial sensors while walking under single and dual-task (reciting every other letter of the alphabet) conditions. Outcome measures included 26 objective measures within four gait domains (upper/lower body, turning and variability). The area under the curve (AUC) from the receiver-operator characteristic plot was calculated to compare discriminative ability of dual-task costs on gait across outcome measures.

RESULTS

PD-specific, dual-task interference was identified for arm range of motion, foot strike angle, turn velocity and turn duration. Arm range of motion (AUC = 0.73) and foot strike angle (AUC = 0.68) had the largest AUCs across dual-task costs measures and they were associated with disease severity and/or disease duration. In contrast, the most commonly used dual-task gait measure, gait speed, showed an AUC of only 0.54.

CONCLUSION

Findings suggest that people with PD rely more than healthy individuals on executive-attentional resources to control arm swing, foot strike, and turning, but not gait speed. The dual-task costs of arm range of motion best discriminated people with PD from healthy individuals.

Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review

Objective

The gold standard for objective body posture examination is posturography. Body movements are detected through the use of force platforms that assess static and dynamic balance (conventional posturography). In recent years, new technologies like wearable sensors (mobile posturography) have been applied during complex dynamic activities to diagnose and rehabilitate balance disorders. They are used in healthy people, especially in the aging population, for detecting falls in the older adults, in the rehabilitation of different neurological, osteoarticular, and muscular system diseases, and in vestibular disorders. Mobile devices are portable, lightweight, and less expensive than conventional posturography. The vibrotactile system can consist of an accelerometer (linear acceleration measurement), gyroscopes (angular acceleration measurement), and magnetometers (heading measurement, relative to the Earth’s magnetic field). The sensors may be mounted to the trunk (most often in the lumbar region of the spine, and the pelvis), wrists, arms, sternum, feet, or shins. Some static and dynamic clinical tests have been performed with the use of wearable sensors. Smartphones are widely used as a mobile computing platform and to evaluate the results or monitor the patient during the movement and rehabilitation. There are various mobile applications for smartphone-based balance systems. Future research should focus on validating the sensitivity and reliability of mobile device measurements compared to conventional posturography.

Conclusion

Smartphone based mobile devices are limited to one sensor lumbar level posturography and offer basic clinical evaluation. Single or multi sensor mobile posturography is available from different manufacturers and offers single to multi-level measurements, providing more data and in some instances even performing sophisticated clinical balance tests.

Characterization of gait variability in multiple system atrophy and Parkinson's disease

BACKGROUND

Gait impairment is a pivotal feature of parkinsonian syndromes and increased gait variability is associated with postural instability and a higher risk of falls.

OBJECTIVES

We compared gait variability at different walking velocities between and within groups of patients with Parkinson-variant multiple system atrophy, idiopathic Parkinson's disease, and a control group of older adults.

METHODS

Gait metrics were recorded in 11 multiple system atrophy, 12 Parkinson's disease patients, and 18 controls using sensor-based gait analysis. Gait variability was analyzed for stride, swing and stance time, stride length and gait velocity. Values were compared between and within the groups at self-paced comfortable, fast and slow walking speed.

RESULTS

Multiple system atrophy patients displayed higher gait variability except for stride time at all velocities compared with controls, while Parkinson's patients did not. Compared with Parkinson's disease, multiple system atrophy patients displayed higher variability of swing time, stride length and gait velocity at comfortable speed and at slow speed for swing and stance time, stride length and gait velocity (all P < 0.05). Stride time variability was significantly higher in slow compared to comfortable walking in patients with multiple system atrophy (P = 0.014). Variability parameters significantly correlated with the postural instability/gait difficulty subscore in both disease groups. Conversely, significant correlations between variability parameters and MDS-UPDRS III score was observed only for multiple system atrophy patients.

CONCLUSION

This analysis suggests that gait variability parameters reflect the major axial impairment and postural instability displayed by multiple system atrophy patients compared with Parkinson's disease patients and controls.

Aging, Vestibular Function, and Balance: Proceedings of a National Institute on Aging/National Institute on Deafness and Other Communication Disorders Workshop

Balance impairment and falls are among the most prevalent and morbid conditions affecting older adults. A critical contributor to balance and gait function is the vestibular system; however, there remain substantial knowledge gaps regarding age-related vestibular loss and its contribution to balance impairment and falls in older adults. Given these knowledge gaps, the National Institute on Aging and the National Institute on Deafness and Other Communication Disorders convened a multidisciplinary workshop in April 2019 that brought together experts from a wide array of disciplines, such as vestibular physiology, neuroscience, movement science, rehabilitation, and geriatrics. The goal of the workshop was to identify key knowledge gaps on vestibular function and balance control in older adults and develop a research agenda to make substantial advancements in the field. This article provides a report of the proceedings of this workshop. Three key questions emerged from the workshop, specifically: (i) How does aging impact vestibular function?; (ii) How do we know what is the contribution of age-related vestibular impairment to an older adult's balance problem?; and more broadly, (iii) Can we develop a nosology of balance impairments in older adults that can guide clinical practice? For each of these key questions, the current knowledge is reviewed, and the critical knowledge gaps and research strategies to address them are discussed. This document outlines an ambitious 5- to 10-year research agenda for increasing knowledge related to vestibular impairment and balance control in older adults, with the ultimate goal of linking this knowledge to more effective treatment.

Responsiveness of Objective vs. Clinical Balance Domain Outcomes for Exercise Intervention in Parkinson's Disease

Background: Balance deficits in people with Parkinson's disease (PD) are often not helped by pharmacological or surgical treatment. Although balance exercise intervention has been shown to improve clinical measures of balance, the efficacy of exercise on different, objective balance domains is still unknown.

Objective: To compare the sensitivity to change in objective and clinical measures of several different domains of balance and gait following an Agility Boot Camp with Cognitive Challenges (ABC-C) intervention.

Methods: In this cross-over, randomized design, 86 individuals with PD participated in 6-week (3×/week) ABC-C exercise classes and 6-week education classes, consisting of 3–6 individuals. Blinded examiners tested people in their practical off state. Objective outcome measures from wearable sensors quantified four domains of balance: sway in standing balance, anticipatory postural adjustments (APAs) during step initiation, postural responses to the push-and-release test, and a 2-min natural speed walk with and without a cognitive task. Clinical outcome measures included the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III, the Mini Balance Evaluation Systems Test (Mini-BESTest), the Activities of Balance Confidence (ABC), and the Parkinson's Disease Questionnaire (PDQ-39). The standardized response means (SRM) of the differences between before and after each intervention compared responsiveness of outcomes to intervention. A linear mixed model compared effects of exercise with the active control—education intervention.

Results: The most responsive outcome measures to exercise intervention with an SRM > 0.5 were objective measures of gait and APAs, specifically arm range of motion, gait speed during a dual-task walk, trunk coronal range of motion, foot strike angle, and first-step length at step initiation. The most responsive clinical outcome measure was the patient-reported PDQ-39 activities daily living subscore, but all clinical measures had SRMs <0.5.

Conclusions: The objective measures were more sensitive to change after exercise intervention compared to the clinical measures. Spatiotemporal parameters of gait, including gait speed with a dual task, and APAs were the most sensitive objective measures, and perceived functional independence was the most sensitive clinical measure to change after the ABC-C exercise intervention. Future exercise intervention to improve gait and balance in PD should include objective outcome measures.

The Adapted Resistance Training with Instability Randomized Controlled Trial for Gait Automaticity

BACKGROUND

Deficits in the cerebellar locomotor region (CLR) have been associated with loss of gait automaticity in individuals with freezing of gait in Parkinson's disease (freezers); however, exercise interventions that restore gait automaticity in freezers are lacking. We evaluated the effects of the adapted resistance training with instability ([ARTI] complex exercises) compared with traditional motor rehabilitation (without complex exercises) on gait automaticity and attentional set-shifting. We also verified associations between gait automaticity change and CLR activation change previously published.

METHODS

Freezers were randomized either to the experimental group (ARTI, n = 17) or to the active control group (traditional motor rehabilitation, n = 15). Both training groups performed exercises 3 times a week for 12 weeks. Gait automaticity (dual-task and dual-task cost [DTC] on gait speed and stride length), single-task gait speed and stride length, attentional set-shifting (time between Trail Making Test parts B and A), and CLR activation during a functional magnetic resonance imaging protocol of simulated step initiation task were evaluated before and after interventions.

RESULTS

Both training groups improved gait parameters in single task (P < 0.05), but ARTI was more effective than traditional motor rehabilitation in improving DTC on gait speed, DTC on stride length, dual-task stride length, and CLR activation (P < 0.05). Changes in CLR activation were associated with changes in DTC on stride length (r = 0.68, P = 0.002) following ARTI. Only ARTI improved attentional set-shifting at posttraining (P < 0.05).

CONCLUSIONS

ARTI restores gait automaticity and improves attentional set-shifting in freezers attributed to the usage of exercises with high motor complexity. © 2020 International Parkinson and Movement Disorder Society.

Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.

Relating Parkinson freezing and balance domains: A structural equation modeling approach

BACKGROUND

People with PD who exhibit freezing of gait (FOG) also exhibit poor balance compared to those who do not freeze. However, balance is a broad construct that can be subdivided into subdomains that include dynamic balance (gait), anticipatory postural adjustments (APAs) & gait initiation, postural sway in stance, and automatic postural responses (e.g., reactive stepping). Few studies have provided a robust investigation on how each of these domains is impacted by FOG, and no studies have compared balance across groups while rigorously controlling for disease severity.

METHODS

Structural equation modeling was used to evaluate the relationships between FOG and balance domains constructed as latent variables and controlling for disease severity. Domains included: dynamic balance (gait), APAs, postural sway, and reactive stepping. Models were run relating domains to both the presence and severity of FOG.

RESULTS

Latent variables reflecting domains of Gait and APAs, but not postural sway or reactive stepping, were significantly related to the severity of FOG. Models for presence of FOG showed the same results, as Gait and APAs, but not postural sway or reactive stepping, were related to presence of FOG.

CONCLUSION

These results are consistent with hypotheses that balance deficits in people with PD who freeze are most pronounced in gait and anticipatory postural adjustments. Reactive stepping and postural sway domains are less effected in PD patients who freeze compared to those who do not. These findings suggest that rehabilitative strategies focused on gait and APAs may be most effective for people with PD who freeze.

Quantifying Postural Control in Premanifest and Manifest Huntington Disease Using Wearable Sensors

Background. Impairments in postural control in Huntington disease (HD) have important consequences for daily functioning. This observational study systematically examined baseline postural control and the effect of sensory attenuation and sensory enhancement on postural control across the spectrum of HD. Methods. Participants (n = 39) included healthy controls and individuals in premanifest (pHD) and manifest stages (mHD) of HD. Using wearable sensors, postural control was assessed according to (1) postural set (sit vs stand), (2) sensory attenuation using clinical test of sensory integration, and (3) sensory enhancement with gaze fixation. Outcomes included sway smoothness, amplitude, and frequency. Results. Based on postural set, pHD reduced postural sway in sitting relative to standing, whereas mHD had pronounced sway in standing and sitting, highlighting a baseline postural deficit. During sensory attenuation, postural control in pHD deteriorated relative to controls when proprioceptive demands were high (eyes closed on foam), whereas mHD had significant deterioration of postural control when proprioception was attenuated (eyes open and closed on foam). Finally, gaze fixation improved sway smoothness, amplitude, and frequency in pHD; however, no benefit was observed in mHD. Conclusions. Systematic examination of postural control revealed a fundamental postural deficit in mHD, which further deteriorates when proprioception is challenged. Meanwhile, postural deficits in pHD are detectable when proprioceptive challenge is high. Sensory enhancing strategies using gaze fixation to benefit posture may be useful when introduced well before motor diagnosis. These findings encourage further examination of wearable sensors as part of routine clinical assessments in HD.

Deep Phenotyping of Parkinson's Disease

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.