Effect of Lower Limb vs. Abdominal Compression on Mobility in Orthostatic Hypotension: A Single-Blinded, Randomized, Controlled, Cross-Over Pilot Study in Parkinson's Disease.

BACKGROUND: Orthostatic hypotension (OH) in Parkinson's disease (PD) is frequent and associated with impairments in quality of life and reduced activities of daily living. Abdominal binders (AB) and compression stockings (CS) have been shown to be effective non-pharmacological treatment options. OBJECTIVE: Here, we investigate the effect of AB versus CS on physical activity using a digital mobility outcome (sit to stand [STS] frequency) collected in the usual environment as a primary endpoint. METHODS: We enrolled 16 PD patients with at least moderate symptomatic OH. In a randomized, single-blinded, controlled, crossover design, participants were assessed without OH treatment over 1 week (baseline), then were given AB or CS for 1 week and subsequently switched to the other treatment arm. The primary outcome was the number of real-life STS movements per hour as assessed with a lower back sensor. Secondary outcomes included real-life STS duration, mean/systolic/diastolic blood pressure drop (BPD), orthostatic hypotension questionnaire (OHQ), PD quality of life (PDQ-39), autonomic symptoms (SCOPA-AUT), non-motor symptoms (NMSS), MDS-UPDRS, and activities of daily living (ADL/iADL). RESULTS: Real-life STS frequency on CS was 4.4±4.1 per hour compared with 3.6±2.2 on AB and 3.6±1.8 without treatment (p = 1.0). Concerning the secondary outcomes, NMSS showed significant improvement with CS and AB. OHQ and SCOPA-AUT improved significantly with AB but not CS, and mean BPD drop worsened with CS but not AB. Mean STS duration, PDQ-39, MDS-UPDRS, ADL, and iADL did not significantly change. CONCLUSION: Both AB and CS therapies do not lead to a significant change of physical activity in PD patients with at least moderate symptomatic OH. Secondary results speak for an effect of both therapies concerning non-motor symptoms, with superiority of AB therapy over CS therapy.

Instrumented 5-Time Sit-To-Stand Test: Parameters Predicting Serious Falls beyond the Duration of the Test.

BACKGROUND: Falls are a major cause of injuries in older adults. To evaluate the risk of falls in older adults, clinical assessments such as the 5-time sit-to-stand (5xSTS) test can be performed. The development of inertial measurement units (IMUs) has provided the possibility of a more in-depth analysis of the movements' biomechanical characteristics during this test. The goal of the present study was to investigate whether an instrumented 5xSTS test provides additional information to predict multiple or serious falls compared to the conventional stopwatch-based method. METHODS: Data from 458 community-dwelling older adults were analyzed. The participants were equipped with an IMU on the trunk to extract temporal, kinematic, kinetic, and smoothness movement parameters in addition to the total duration of the test by the stopwatch. RESULTS: The total duration of the test obtained by the IMU and the stopwatch was in excellent agreement (Pearson's correlation coefficient: 0.99), while the total duration obtained by the IMU was systematically 0.52 s longer than the stopwatch. In multivariable analyses that adjusted for potential confounders, fallers had slower vertical velocity, reduced vertical acceleration, lower vertical power, and lower vertical jerk than nonfallers. In contrast, the total duration of the test measured by either the IMU or the stopwatch did not differ between the 2 groups. CONCLUSIONS: An instrumented 5xSTS test provides additional information that better discriminates among older adults those at risk of multiple or serious falls than the conventional stopwatch-based assessment.

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease.

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

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease.

Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.

Toward a Remote Assessment of Walking Bout and Speed: Application in Patients With Multiple Sclerosis.

Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gait speed estimation algorithms in clinical settings which can be different than the home assessments in which the patients demonstrate their actual performance. Moreover, to provide comfort for the users, devising an algorithm based on a single sensor setup is essential. To this end, the goal of this study was to develop and validate a new gait speed estimation method based on a machine learning approach to predict gait speed in both clinical and home assessments by a sensor on the lower back. Moreover, two methods were introduced to detect walking bouts during daily activities at home. We have validated the algorithms in 35 patients with multiple sclerosis as it often presents with mobility difficulties. Therefore, the robustness of the algorithm can be shown in an impaired or slow gait. Against silver standard multi-sensor references, we achieved a bias close to zero and a precision of 0.15 m/s for gait speed estimation. Furthermore, the proposed machine learning-based locomotion detection method had a median of 96.8% specificity, 93.0% sensitivity, 96.4% accuracy, and 78.6% F1-score in detecting walking bouts at home. The high performance of the proposed algorithm showed the feasibility of the unsupervised mobility assessment introduced in this study.

Gait speed in clinical and daily living assessments in Parkinson's disease patients: performance versus capacity.

Gait speed often referred as the sixth vital sign is the most powerful biomarker of mobility. While a clinical setting allows the estimation of gait speed under controlled conditions that present functional capacity, gait speed in real-life conditions provides the actual performance of the patient. The goal of this study was to investigate objectively under what conditions during daily activities, patients perform as well as or better than in the clinic. To this end, we recruited 27 Parkinson's disease (PD) patients and measured their gait speed by inertial measurement units through several walking tests in the clinic as well as their daily activities at home. By fitting a bimodal Gaussian model to their gait speed distribution, we found that on average, patients had similar modes in the clinic and during daily activities. Furthermore, we observed that the number of medication doses taken throughout the day had a moderate correlation with the difference between clinic and home. Performing a cycle-by-cycle analysis on gait speed during the home assessment, overall only about 3% of the strides had equal or greater gait speeds than the patients' capacity in the clinic. These strides were during long walking bouts (>1 min) and happened before noon, around 26 min after medication intake, reaching their maximum occurrence probability 3 h after Levodopa intake. These results open the possibility of better control of medication intake in PD by considering both functional capacity and continuous monitoring of gait speed during real-life conditions.

Postural transitions detection and characterization in healthy and patient populations using a single waist sensor.

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

Long-term unsupervised mobility assessment in movement disorders.

Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them.

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter.

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.