Prevalence and Factors Associated With Mental Health Symptoms in Adults Undergoing Covid-19 Testing.

BACKGROUND AND OBJECTIVE: Understanding the mental health impact of the COVID-19 pandemic on persons receiving COVID-19 testing will help guide mental health interventions. We aimed to determine the association between sociodemographic factors and mental health symptoms at 8 weeks (baseline) after a COVID-19 test, and compare prevalence of mental health symptoms at baseline to those at 16-week follow-up. MATERIALS AND METHODS: Prospective cohort study of adults who received outpatient COVID-19 testing at primary care clinics. Logistic regression analyses were used to assess the association between sociodemographic characteristics and COVID-19 test results with mental health symptoms. Mental health symptoms reported at baseline were compared to symptoms at 16 weeks follow-up using conditional logistic regression analyses. RESULTS: At baseline, a total of 124 (47.51%) participants reported at least mild depressive symptoms, 110 (42.15%) participants endorsed at least mild anxiety symptoms, and 94 participants (35.21%) endorsed hazardous use of alcohol. Females compared to males were at increased risk of at least mild depressive symptoms at baseline (Adjusted Odds Ratio (AOR): 2.08; 95% CI: 1.14-3.79). The odds of at least mild depressive symptoms was significantly lower among those residing in zip codes within the highest quartile compared to lowest quartile of household income (AOR: 0.37; 95% CI: 0.17-0.81). Also, non-Hispanic Whites had significantly higher odds of reporting hazardous alcohol use compared to non-Whites at baseline (AOR: 1.94; 95% CI: 1.05-3.57). The prevalence of mental health symptoms remained elevated after 16 weeks. CONCLUSION AND RELEVANCE: We found a high burden of symptoms of depression and anxiety as well as hazardous alcohol use in a diverse population who received testing for COVID-19 in the primary care setting. Primary care providers need to remain vigilant in screening for symptoms of mental health disorders in patients tested for COVID-19 well after initial testing.

NUCare: Advancing research on technological integration for self-management in the aging population.

BACKGROUND: The Center for Technology in Support of Self-Management and Health (NUCare) is an exploratory research center funded by the National Institute of Nursing Research's P20 mechanism positioned to conduct rigorous research on the integration of technology in the self-management of the older adult population. PURPOSE: The purpose of this paper is to describe the development and application of an evaluation plan and preliminary evaluation results from the first year of implementation. METHODS: This evaluation plan is derived from and is consistent with Dorsey et al.'s (2014) logic model. Dorsey's model provided guidelines for evaluating sustainability, leveraging of resources, and interdisciplinary collaboration within the center. DISCUSSION: Preliminary results and strategies for addressing findings from the first year of evaluation are discussed. A secondary aim of this paper is to showcase the relevance of this center to the advancement and maintenance of health in the aging population.

Can accelerometry data improve estimates of heart rate variability from wrist pulse PPG sensors?

A key prerequisite for precision medicine is the ability to assess metrics of human behavior objectively, unobtrusively and continuously. This capability serves as a framework for the optimization of tailored, just-in-time precision health interventions. Mobile unobtrusive physiological sensors, an important prerequisite for realizing this vision, show promise in implementing this quality of physiological data collection. However, first we must trust the collected data. In this paper, we present a novel approach to improving heart rate estimates from wrist pulse photoplethysmography (PPG) sensors. We also discuss the impact of sensor movement on the veracity of collected heart rate data.

Health Disparities in Veterans: A Map of the Evidence.

BACKGROUND: Goals for improving the quality of care for all Veterans and eliminating health disparities are outlined in the Veterans Health Administration Blueprint for Excellence, but the degree to which disparities in utilization, health outcomes, and quality of care affect Veterans is not well understood. OBJECTIVES: To characterize the research on health care disparities in the Veterans Health Administration by means of a map of the evidence. RESEARCH DESIGN: We conducted a systematic search for research studies published from 2006 to February 2016 in MEDLINE and other data sources. We included studies of Veteran populations that examined disparities in 3 outcome categories: utilization, quality of health care, and patient health. MEASURES: We abstracted data on study design, setting, population, clinical area, outcomes, mediators, and presence of disparity for each outcome category. We grouped the data by population characteristics including race, disability status, mental illness, demographics (age, era of service, rural location, and distance from care), sex identity, socioeconomic status, and homelessness, and created maps illustrating the evidence. RESULTS: We reviewed 4249 citations and abstracted data from 351 studies which met inclusion criteria. Studies examining disparities by race/ethnicity comprised by far the vast majority of the literature, followed by studies examining disparities by sex, and mental health condition. Very few studies examined disparities related to lesbian, gay, bisexual, or transgender identity or homelessness. Disparities findings vary widely by population and outcome. CONCLUSIONS: Our evidence maps provide a "lay of the land" and identify important gaps in knowledge about health disparities experienced by different Veteran populations.

Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care.

Health-related behaviors are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to enhance health outcomes and mitigate the escalating challenges arising from an increasingly aging population and the proliferation of chronic diseases. Although it has been difficult to obtain lasting improvements in health behaviors on a wide scale, advances at the intersection of technology and behavioral science may provide the tools to address this challenge. In this paper, we describe a vision and an approach to improve health behavior interventions using the tools of behavioral informatics, an emerging transdisciplinary research domain based on system-theoretic principles in combination with behavioral science and information technology. The field of behavioral informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely interventions. We describe the components of a closed-loop system for health interventions. These components range from fine grain sensor characterizations to individual-based models of behavior change. We provide an example of a research health coaching platform that incorporates a closed-loop intervention based on these multiscale models. Using this early prototype, we illustrate how the optimized and personalized methodology and technology can support self-management and remote care. We note that despite the existing examples of research projects and our platform, significant future research is required to convert this vision to full-scale implementations.

Folate polyglutamylation eliminates dependence of activity on enzyme concentration in mitochondrial serine hydroxymethyltransferases from Arabidopsis thaliana.

The reversible reaction catalyzed by serine hydroxymethyltransferase (SHMT) is the major one-carbon unit source for essential metabolic processes. The Arabidopsis thaliana genome encodes seven SHMT isozymes localized in mitochondria, plastids, nuclei, and the cytosol. Knowledge of the biochemical properties of each isozyme is central to understanding and manipulating one-carbon metabolism in plants. We heterologously expressed and purified three recombinant SHMTs from A. thaliana (AtSHMTs) putatively localized in mitochondria (two) and the cytosol (one). Their biochemical properties were characterized with respect to the impact of folate polyglutamylation on substrate saturation kinetics. The two mitochondrial AtSHMTs, but not the cytosolic one, had increased turnover rates at higher (>0.4ng/µL) enzyme concentrations in the presence of monoglutamylated folate substrates, but not in the presence of pentaglutamylated folate substrates. We found no experimental support for a change in oligomerization state over the range of enzyme concentration studied. Modeling of the enzyme structures presented features that may explain the activity differences between the mitochondrial and cytosolic isozymes.