Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back

Enhancing spatial navigation skills in mild cognitive impairment patients: a usability study of a new version of ANTaging software

Introduction

Mild Cognitive Impairment (MCI) often presents challenges related to spatial navigation and retention of spatial information. Navigating space involves intricate integration of bodily and environmental cues. Spatial memory is dependent on two distinct frame of reference systems for organizing this information: egocentric and allocentric frames of reference. Virtual Reality (VR) has emerged as a promising technology for enhancing spatial navigation skills and spatial memory by facilitating the manipulation of bodily, environmental, and cognitive cues.

Methods

This usability study was based on a fully within-subjects design in which seven MCI patients underwent two kinds of VR conditions: participants were required to complete the ANTaging demo both in Oculus Rift S (immersive condition) and in Samsung UHD 4K monitor (semi-immersive condition). Participants were seated and they had to use a foot-motion pad to navigate and explore the environment to collect and relocate some objects in the virtual environment. Post-interaction, users provided feedback on their experiences. Additionally, usability, potential side effects, data analysis feasibility, and user preferences with immersive and semi-immersive technologies were assessed through questionnaires.

Results

Results indicated higher usability ratings for the semi-immersive setup, with fewer negative effects reported compared to the immersive counterpart. According to qualitative analyses of the interviews, patients do seem to like both VR apparatuses even though the semi-immersive condition was perceived as the most suitable choice because of the size of the screen. Patients generally found it difficult to remember object locations. Participants expressed the need for more practice with the foot-motion pad, despite an overall positive experience. They generally would like to use this system to improve their memory.

Discussion

Identifying these key aspects was crucial for refining the system before the upcoming clinical trial. This study sheds light on the potential of semi-immersive VR in aiding individuals with MCI, paving the way for enhanced spatial navigation interventions.

The Role of Muscle Strength in the Sit-to-Stand Task in Parkinson's Disease

Background

Rising from a chair or the sit-to-stand (STS) task is frequently impaired in individuals with Parkinson's disease (PD). These patients commonly attribute such difficulties to weakness in the lower extremities. However, the role of muscle strength in the STS transfer task has not been fully elucidated.

Objective

We aim at determining the role of muscle strength in the STS task.

Methods

We studied 90 consecutive patients with PD and 52 sex- and age-matched controls. Lower limb strength was determined in both legs by clinical examination using the Medical Research Council Scale, dynamometric (leg flexion) and weighting machine (leg pressure) measures. Patients were interrogated regarding the presence of subjective lower limb weakness or allied sensations.

Results

There were 20 patients (22.2%) with abnormal STS task (item 3.9 of the MDS-UPDRS-III ≥2 points). These patients had higher modified Hoehn and Yahr stage (P  <  0.001) and higher total motor scores of the MDS-UPDRS(P  <  0.001), compared with 70 PD patients with normal STS task. Patients with abnormal STS task endorsed lower limb weakness more frequently and had lower muscle strength in the proximal lower extremities, compared to PD patients with normal STS task and normal controls. The presence of perceived lower limb weakness increased the risk of an abnormal STS task, OR: 11.93 (95% C.I. 1.51-94.32), whereas a hip extension strength ≤9 kg/pressure also increased the risk of abnormal STS task, OR: 4.45 (95% C.I. 1.49-13.23). In the multivariate regression analysis, bradykinesia and decreased hip strength were related to abnormal STS task.

Conclusions

Patients with PD and abnormal STS task complain more commonly of lower limb weakness and have decreased proximal lower limb strength compared to patients with PD and normal STS task, likely contributing to abnormalities in performing the STS task.

Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis

Impairment in persons with multiple sclerosis (PwMS) can often be attributed to symptoms of motor instability and fatigue. Symptom monitoring and queued interventions often target these symptoms. Clinical metrics are currently limited to objective physician assessments or subjective patient reported measures. Recent research has turned to wearables for improving the objectivity and temporal resolution of assessment. Our group has previously observed wearable assessment of supervised and unsupervised standing transitions to be predictive of fall-risk in PwMS. Here we extend the application of standing transition quantification to longitudinal home monitoring of symptoms. Subjects (N=23) with varying degrees of MS impairment were recruited and monitored with accelerometry for a total of  ∼  6 weeks each. These data were processed using a preexisting framework, applying a deep learning activity classifier to isolate periods of standing transition from which descriptive features were extracted for analysis. Participants completed daily and biweekly assessments describing their symptoms. From these data, Canonical Correlation Analysis was used to derive digital phenotypes of MS instability and fatigue. We find these phenotypes capable of distinguishing fallers from non-fallers, and further that they demonstrate a capacity to characterize symptoms at both daily and sub-daily resolutions. These results represent promising support for future applications of wearables, which may soon augment or replace current metrics in longitudinal monitoring of PwMS.

How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway

Wearable sensors facilitate the evaluation of gait and balance impairment in the free-living environment, often with observation periods spanning weeks, months, and even years. Data supporting the minimal duration of sensor wear, which is necessary to capture representative variability in impairment measures, are needed to balance patient burden, data quality, and study cost. Prior investigations have examined the duration required for resolving a variety of movement variables (e.g., gait speed, sit-to-stand tests), but these studies use differing methodologies and have only examined a small subset of potential measures of gait and balance impairment. Notably, postural sway measures have not yet been considered in these analyses. Here, we propose a three-level framework for examining this problem. Difference testing and intra-class correlations (ICC) are used to examine the agreement in features computed from potential wear durations (levels one and two). The association between features and established patient reported outcomes at each wear duration is also considered (level three) for determining the necessary wear duration. Utilizing wearable accelerometer data continuously collected from 22 persons with multiple sclerosis (PwMS) for 6 weeks, this framework suggests that 2 to 3 days of monitoring may be sufficient to capture most of the variability in gait and sway; however, longer periods (e.g., 3 to 6 days) may be needed to establish strong correlations to patient-reported clinical measures. Regression analysis indicates that the required wear duration depends on both the observation frequency and variability of the measure being considered. This approach provides a framework for evaluating wear duration as one aspect of the comprehensive assessment, which is necessary to ensure that wearable sensor-based methods for capturing gait and balance impairment in the free-living environment are fit for purpose.

Estimating balance, cognitive function, and falls risk using wearable sensors and the sit-to-stand test

The five times sit-to-stand test (FTSS) is an established functional test, used clinically as a measure of lower-limb strength, endurance and falls risk. We report a novel method to estimate and classify cognitive function, balance impairment and falls risk using the FTSS and body-worn inertial sensors. 168 community dwelling older adults received a Comprehensive Geriatric Assessment which included the Mini-Mental State Examination (MMSE) and the Berg Balance Scale (BBS). Each participant performed an FTSS, with inertial sensors on the thigh and torso, either at home or in the clinical environment. Adaptive peak detection was used to identify phases of each FTSS from torso or thigh-mounted inertial sensors. Features were then extracted from each sensor to quantify the timing, postural sway and variability of each FTSS. The relationship between each feature and MMSE and BBS was examined using Spearman's correlation. Intraclass correlation coefficients were used to examine the intra-session reliability of each feature. A Poisson regression model with an elastic net model selection procedure was used to estimate MMSE and BBS scores, while logistic regression and sequential forward feature selection was used to classify participants according to falls risk, cognitive decline and balance impairment. BBS and MMSE were estimated using cross-validation with low root mean squared errors of 2.91 and 1.50, respectively, while the cross-validated classification accuracies for balance impairment, cognitive decline, and falls risk were 81.96, 72.71, and 68.74%, respectively. The novel methods reported provide surrogate measures which may have utility in remote assessment of physical and cognitive function.

Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis

BACKGROUND

One in two people with multiple sclerosis (PwMS) will fall in a three-month period. Predicting which patients will fall remains a challenge for clinicians. Standardized functional assessments provide insight into balance deficits and fall risk but their use has been limited to supervised visits.

RESEARCH QUESTION

The study aim was to characterize unsupervised 30-second chair stand test (30CST) performance using accelerometer-derived metrics and assess its ability to classify fall status in PwMS compared to supervised 30CST.

METHODS

Thirty-seven PwMS (21 fallers) performed instrumented supervised and unsupervised 30CSTs with a single wearable sensor on the thigh. In unsupervised conditions, participants performed bi-hourly 30CSTs and rated their balance confidence and fatigue over 48-hours. ROC analysis was used to classify fall status for 30CST performance.

RESULTS

Non-fallers (p = 0.02) but not fallers (p = 0.23) differed in their average unsupervised 30CST performance (repetitions) compared to their supervised performance. The unsupervised maximum number of 30CST repetitions performed optimized ROC classification AUC (0.79), accuracy (78.4%) and specificity (90.0%) for fall status with an optimal cutoff of 17 repetitions.

SIGNIFICANCE

Brief durations of instrumented unsupervised monitoring as an adjunct to routine clinical assessments could improve the ability for predicting fall risk and fluctuations in functional mobility in PwMS.

SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing

Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda.

Assessment of Thigh Angular Velocity by an Activity Monitor to Describe Sit-to-Stand Performance

The assessment of sit-to-stand (STS) performance is highly relevant, especially in older persons, but testing STS performance in the laboratory does not necessarily reflect STS performance in daily life. Therefore, the aim was to validate a wearable sensor-based measure to be used under unsupervised daily life conditions. Since thigh orientation from horizontal to vertical is characteristic for STS movement, peak angular velocity (PAV) of the thigh was chosen as the outcome variable. A total of 20 younger and older healthy persons and geriatric patients (mean age: 55.5 ± 20.8 years; 55% women) with a wide range of STS performance were instructed to stand up from a chair at their usual pace. STS performance was measured by an activity monitor, force plates, and an opto-electronic system. The association between PAV measured by the thigh-worn activity monitor and PAV measured by the opto-electronic system (gold standard) was r = 0.74. The association between PAV measured by the thigh-worn activity monitor and peak power measured by force plate and opto-electronic system was r = 0.76. The Intra-Class Coefficient (ICC) of agreement between the 2 trials was ICC_(A,1) = 0.76. In this sample of persons with a wide range of physical performance, PAV as measured by a thigh-worn acceleration sensor was a valid and reliable measure of STS performance.