Multimodal Mobility Assessment Predicts Fall Frequency and Severity in Cerebellar Ataxia

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

Remote assessment of cognition in Parkinson's disease and Cerebellar Ataxia: the MoCA test in English and Hebrew

There is a critical need for accessible neuropsychological testing for basic research and translational studies worldwide. Traditional in-person neuropsychological studies are inherently difficult to conduct because testing requires the recruitment and participation of individuals with neurological conditions. Consequently, studies are often based on small sample sizes, are highly time-consuming, and lack diversity. To address these challenges, in the last decade, the utilization of remote testing platforms has demonstrated promising results regarding the feasibility and efficiency of collecting patient data online. Herein, we tested the validity and generalizability of remote administration of the Montreal Cognitive Assessment (MoCA) test. We administered the MoCA to English and Hebrew speakers from three different populations: Parkinson's disease, Cerebellar Ataxia, and healthy controls via video conferencing. First, we found that the online MoCA scores do not differ from traditional in-person studies, demonstrating convergent validity. Second, the MoCA scores of both our online patient groups were lower than controls, demonstrating construct validity. Third, we did not find differences between the two language versions of the remote MoCA, supporting its generalizability to different languages and the efficiency of collecting binational data (USA and Israel). Given these results, future studies can utilize the remote MoCA, and potentially other remote neuropsychological tests to collect data more efficiently across multiple different patient populations, language versions, and nations.