Frailty Syndrome, Cognition, and Dysphonia in the Elderly

Purpose. The purpose of the current study is to determine the relation of frailty syndrome to acoustic measures of voice quality and voice-related handicap. Methods. Seventy-three adults (52 community-dwelling participants and 21 assisted living residents) age 60 and older completed frailty screening, acoustic assessment, cognitive screening, and the Voice Handicap Index-10 (VHI-10). Factor analysis was used to consolidate acoustic measures. Statistical analysis included multiple regression, analysis of variance, and Tukey post-hoc tests with alfa of 0.05. Results. Montreal Cognitive Assessment (MoCA) and exhaustion explained 28% of the variance in VHI-10. MoCA and sex explained 27% of the variance in factor 1 (spectral ratio), age and MoCA explained 13% of the variance in factor 2 (cepstral peak prominence for speech), and slowness explained 10% of the variance in factor 3 (cepstral peak prominence for sustained /a/). There were statistically significant differences in two measures across frailty groups: VHI-10 and MoCA. Acoustic factor scores did not differ significantly among frailty groups (P > 0.05). Conclusions. Voice-related handicap and cognitive status differed among robust and frail older adults, yet vocal function measures did not. The components of frailty most related to VHI-10 were exhaustion and weight loss rather than slowness, weakness, or inactivity. Based on these findings, routine screening of physical frailty and cognition are recommended as part of a complete voice evaluation for older adults.12 month embargo; published online: 25 July 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The Resurgence of Home-Based Primary Care Models in the United States

This article describes the forces behind the resurgence of home-based primary care (HBPC) in the United States and then details different HBPC models. Factors leading to the resurgence include an aging society, improved technology, an increased emphasis on home and community services, higher fee-for-service payments, and health care reform that rewards value over volume. The cost savings come principally from reduced institutional care in hospitals and skilled nursing facilities. HBPC targets the most complex and costliest patients in society. An interdisciplinary team best serves this high-need population. This remarkable care model provides immense provider satisfaction. HBPC models differ based on their mission, target population, geography, and revenue structure. Different missions include improved care, reduced costs, reduced readmissions, and teaching. Various payment structures include fee-for-service and value-based contracts such as Medicare Shared Savings Programs, Medicare capitation programs, or at-risk contracts. Future directions include home-based services such as hospital at home and the expansion of the home-based workforce. HBPC is an area that will continue to expand. In conclusion, HBPC has been shown to improve the quality of life of home-limited patients and their caregivers while reducing health care costs

Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach

Previous research showed that frailty can influence autonomic nervous system and consequently heart rate response to physical activities, which can ultimately influence the homeostatic state among older adults. While most studies have focused on resting state heart rate characteristics or heart rate monitoring without controlling for physical activities, the objective of the current study was to classify pre-frail/frail vs non-frail older adults using heart rate response to physical activity (heart rate dynamics). Eighty-eight older adults (65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. Groups consisted of 27 non-frail (age=78.807.23) and 61 pre-frail/frail (age=80.638.07) individuals. Participants performed a normal speed walking as the physical task, while heart rate was measured using a wearable electrocardiogram recorder. After creating heart rate time series, a long-short term memory model was used to classify participants into frailty groups. In 5-fold cross validation evaluation, the long-short term memory model could classify the two above-mentioned frailty classes with a sensitivity, specificity, F1-score, and accuracy of 83.0%, 80.0%, 87.0%, and 82.0%, respectively. These findings showed that heart rate dynamics classification using long-short term memory without any feature engineering may provide an accurate and objective marker for frailty screening.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The impact of cochlear implantation on cognition in older adults: a systematic review of clinical evidence

BACKGROUND: Hearing loss is the third most prevalent chronic condition faced by older adults and has been linked to difficulties in speech perception, activities of daily living, and social interaction. Recent studies have suggested a correlation between severity of hearing loss and an individual's cognitive function; however, a causative link has yet to be established. One intervention option for management of the most severe to profound hearing loss in older adults is cochlear implantation. We performed a review to determine the status of the literature on the potential influence of cochlear implantation on cognition in the older adult population. METHODS: Over 3800 articles related to cochlear implants, cognition, and older adults were reviewed. Inclusion criteria were as follows: (1) study population including adults > 65 years, (2) intervention with cochlear implantation, and (3) cognition as the primary outcome measure of implantation. RESULTS: Out of 3,886 studies selected, 3 met inclusion criteria for the review. CONCLUSIONS: While many publications have shown that cochlear implants improve speech perception, social functioning, and overall quality of life, we found no studies in the English literature that have prospectively evaluated changes in cognitive function after implantation with modern cochlear implants in older adults. The state of the current literature reveals a need for further clinical research on the impact of cochlear implantation on cognition in older adults.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Radar-Based Fall Detection: A Survey

Fall detection, particularly critical for high-risk demographics like the elderly, is a key public health concern, where timely detection can greatly minimize harm. With the advancements in radio frequency (RF) technology, radar has emerged as a powerful tool for human fall detection. Traditional machine learning (ML) algorithms, such as support vector machines (SVM) and k-nearest neighbors (kNN), have shown promising outcomes. However, deep learning (DL) approaches, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have outperformed in learning intricate features and managing large, unstructured datasets. This survey offers an in-depth analysis of radar-based fall detection, with emphasis on micro-Doppler, range-Doppler, and range-Doppler-angles techniques. We discuss the intricacies and challenges in fall detection and emphasize the necessity for a clear definition of falls and appropriate detection criteria, informed by diverse influencing factors. We present an overview of radar signal-processing principles and the underlying technology of radar-based fall detection, providing an accessible insight into ML and DL algorithms. After examining 74 research articles on radar-based fall detection published since 2000, we aim to bridge current research gaps and underscore the potential future research strategies, emphasizing the real-world applications possibility and the unexplored potential of DL in improving radar-based fall detection.Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

mmPose-FK: A Forward Kinematics Approach to Dynamic Skeletal Pose Estimation Using mmWave Radars

In this paper, we propose mmPose-FK, a novel millimeter wave (mmWave) radar-based pose estimation method that employs a dynamic forward kinematics (FK) approach to address the challenges posed by low resolution, specularity, and noise artifacts commonly associated with mmWave radars. These issues often result in unstable joint poses that vibrate over time, reducing the effectiveness of traditional pose estimation techniques. To overcome these limitations, we integrate the FK mechanism into the deep learning model and develop an end-to-end solution driven by data. Our comprehensive experiments using various matrices and benchmarks highlight the superior performance of mmPose-FK, especially when compared to our previous research methods. The proposed method provides more accurate pose estimation and ensures increased stability and consistency, which underscores the continuous improvement of our methodology, showcasing superior capabilities over its antecedents. Moreover, the model can output joint rotations and human bone lengths, which could be further utilized for various applications such as gait parameter analysis and height estimation. This makes mmPose-FK a highly promising solution for a wide range of applications in the field of human pose estimation and beyond.National Institute of Biomedical Imaging and BioengineeringImmediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

State inequality, socioeconomic position and subjective cognitive decline in the United States

Background: Social gradients in health have been observed for many health conditions and are suggested to operate through the effects of status anxiety. However, the gradient between education and Alzheimer's disease is presumed to operate through cognitive stimulation. We examined the possible role of status anxiety through testing for state-level income inequality and social gradients in markers of socioeconomic position (SEP) for Alzheimer's disease risk. Methods: Using data from the cross-sectional 2015 and 2016 Behavioral Risk Factor Surveillance System (BRFSS) and the U.S. Census Bureau's American Community Survey, we tested for the association between U.S. state-level income inequality and individual SEP on subjective cognitive decline (SCD) - a marker of dementia risk - using a generalized estimating equation and clustering by state. Results: State income inequality was not significantly associated with SCD in our multivariable model (OR 1.2; 95% CI: 0.9, 1.6; p = 0.49). We observed a clear linear relationship between household income and SCD where those with an annual household income of 50k to 75k had 1.4 (95% CI: 1.3, 1.6) times the odds and those with household incomes of less than $10,000 had 4.7 (95$75,000. We also found that college graduates (ref.) and those who completed high school (OR: 1.1; 95% CI 1.04, 1.2) fared better than those with some college (OR: 1.3, 95% CI 1.2, 1.4) or less than a high school degree (OR: 1.5; 95% CI: 1.4, 1.7). Conclusions: Income inequality does not play a dominant role in SCD, though a social gradient in individual income for SCD suggests the relationship may operate in part via status anxiety.Centers for Disease Control and Prevention, Healthy Brain Research Network [U48 DP 005002]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]