Is the freezing index a valid outcome to assess freezing of gait during turning in Parkinson's disease?

Introduction: Freezing of gait (FOG) is a disabling symptom for people with Parkinson's disease (PwPD). Turning on the spot for one minute in alternating directions (360 turn) while performing a cognitive dual-task (DT) is a fast and sensitive way to provoke FOG. The FOG-index is a widely used wearable sensor-based algorithm to quantify FOG severity during turning.

Step Width Haptic Feedback for Gait Stability in Spinocerebellar Ataxia: Preliminary Results.

Background: Wider step width and lower step-to-step variability are linked to improved gait stability and reduced fall risk. It is unclear if patients with spinocerebellar ataxia (SCA) can learn to adjust these aspects of gait to reduce fall risk.

Objectives: The aims were to examine the possibility of using wearable step width haptic biofeedback to enhance gait stability and reduce fall risk in individuals with SCA.

Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.

Mobility Abilities Mediate the Association of a More Active Lifestyle With Mobility Disability in Older Adults.

Background: Few studies have analyzed sensor-derived metrics of mobility abilities and total daily physical activity (TDPA). We tested whether sensor-derived mobility metrics and TDPA indices are independently associated with mobility disabilities.

Methods: This cohort study derived mobility abilities from a belt-worn sensor that recorded annual supervised gait testing.

Step Width Estimation in Individuals With and Without Neurodegenerative Disease Via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors.

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

Motor and Nonmotor Measures and Declining Daily Physical Activity in Older Adults.

Importance: Difficulties in identifying modifiable risk factors associated with daily physical activity may impede public health efforts to mitigate the adverse health outcomes of a sedentary lifestyle in an aging population.

Objective: To test the hypothesis that adding baseline sensor-derived mobility metrics to diverse baseline motor and nonmotor variables accounts for the unexplained variance of declining daily physical activity among older adults.

Design, Setting, And Participants: This cohort study analyzed data from participants of the Rush Memory and Aging Project (MAP), an ongoing longitudinal clinical pathological study that began to enroll older adults (age range, 59.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope.

Which Gait Tasks Produce Reliable Outcome Measures of Freezing of Gait in Parkinson's Disease?

Background: Measurement of freezing of gait (FOG) relies on the sensitivity and reliability of tasks to provoke FOG. It is currently unclear which tasks provide the best outcomes and how medication state plays into this.

Objective: To establish the sensitivity and test-retest reliability of various FOG-provoking tasks for presence and severity of FOG, with (ON) and without (OFF) dopaminergic medication.

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

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment.

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.

Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g.

Associations of pain sensitivity and conditioned pain modulation with physical activity: findings from the Multicenter Osteoarthritis Study (MOST).

Objective: Individuals with chronic pain due to knee osteoarthritis (OA) are insufficiently physically active, and alterations of facilitatory and inhibitory nociceptive signaling are common in this population. Our objective was to examine the association of these alterations in nociceptive signaling with objective accelerometer-based measures of physical activity in a large observational cohort.

Design: We used data from the Multicenter Osteoarthritis Study.

Gait and heart rate: do they measure trait or state physical fatigue in people with multiple sclerosis?

Background: Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e.

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

Background: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing.

Automated Gait Detection in Older Adults during Daily-Living using Self-Supervised Learning of Wrist-Worn Accelerometer Data: Development and Validation of ElderNet.

Progressive gait impairment is common in aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data.

Gait Alterations and Association With Worsening Knee Pain and Physical Function: A Machine Learning Approach With Wearable Sensors in the Multicenter Osteoarthritis Study.

Objective: The objective of this study was to identify gait alterations related to worsening knee pain and worsening physical function, using machine learning approaches applied to wearable sensor-derived data from a large observational cohort.

Methods: Participants in the Multicenter Osteoarthritis Study (MOST) completed a 20-m walk test wearing inertial sensors on their lower back and ankles. Parameters describing spatiotemporal features of gait were extracted from these data.

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

Background: Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode.

Objectives: We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states.

Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease.

Background: Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression.

A meta-analysis identifies factors predicting the future development of freezing of gait in Parkinson's disease.

Freezing of gait (FOG) is a debilitating problem that is common among many, but not all, people with Parkinson's disease (PD). Numerous attempts have been made at treating FOG to reduce its negative impact on fall risk, functional independence, and health-related quality of life. However, optimal treatment remains elusive.

Long-term Tai Chi practice in older adults is associated with "younger" functional abilities.

Age-related alterations in physiology lead to declines in physical function that are associated with numerous adverse outcomes among older adults. Utilizing a hybrid design, we aimed to understand whether both long-term and short-term Tai Chi (TC) training are associated with age-related decline in physical function in healthy older adults. We first conducted cross-sectional comparisons among TC-naïve older adults (n = 60, 64.