Poor nutrition and heightened maternal stress are two commonly occurring factors during pregnancy that have the potential to negatively influence fetal development, pregnancy complications, and downstream health and wellbeing of the child. Many behavioral studies with pregnant women aim to either reduce stress or improve dietary intakes and nutritional status, considering these factors as isolated components. There is, however, a growing body of evidence to indicate that maternal nutrition and stress interact with and influence one another. To effectively improve pregnancy outcomes, an integrative health approach that considers these mind-body interactions may be required.
Mindfulness practices have been shown to improve psychological wellbeing of pregnant women; however, the potential for mindfulness to augment the effectiveness of nutrition interventions in pregnancy has yet to be determined. To address this gap, we propose a pilot study of 16 non-diabetic pregnant women with overweight and obesity. Participants will be assigned to receive either a nutrition intervention alone—consisting of four pre-recorded culinary nutrition classes and four individual dietitian consults—or the nutrition intervention, plus mindfulness coaching—daily guided mindfulness practice supported by the Headspace app and coaching on mindful eating—from approximately 12-34 weeks of their pregnancies.
Our aims are to: 1) determine the acceptability of and compliance to each intervention component; 2) compare changes in subjective measures of maternal diet quality, eating behavior, stress, pregnancy-related anxiety, and state mindfulness from early to late pregnancy between groups; and 3) compare changes between groups in objective stress measured using heart rate variability (HRV). We hypothesize that both the nutrition and mindfulness components will be acceptable as measured by retention and compliance rates and feedback surveys, but that the combined nutrition and mindfulness group will show greater improvement in nutrition and psychological measures, as well as a smaller decrease in HRV across pregnancy.
Despite the fact that registered nurses are the largest health workforce component, with identified potential to improve patient safety, currently no evidence-based frontline RN care model exists. The long-term goal of this research is to generate critical knowledge that enables wide-spread implementation and sustained utilization of evidence-based frontline RN care models that maximize the quality and safety of care received. One emerging model highlighted by policy makers and increasingly taken up by health systems across the nation is the Clinical Nurse Leader (CNL) care model. The purpose of this study is to estimate the CNL care model’s effectiveness in impacting better care and better health. It leverages a natural experiment in 66 clinical care units in 9 hospitals across 5 states (GA, TX, MI, NC, IL) that are integrating CNLs into their frontline nursing care delivery model. A hybrid type II implementation-effectiveness study will be used.
The proposed study will examine the efficacy of an innovative, web-based pain and symptom management intervention for children undergoing primarily outpatient cancer treatment. The focus of this application, pediatric cancer pain, is of extremely high public health significance given the large numbers of children diagnosed with cancer each year, the significant under treatment of pain in this population, and the effects of untreated pain on children’s physical, social, and emotional health. This intervention has the potential to impact not only the tens of thousands of children diagnosed with cancer each year, but has widespread applicability to manage pain associated with a wide variety of illnesses.
Cancer health disparities extend to caregivers with numerous studies demonstrating that the traumatic stress of having a child with a chronic illness is associated with poorer health. This includes more frequent illnesses, increased health care costs, and higher rates of comorbid diseases. (1) Moreover, cancer health disparities are a high priority given documented disparities in low socio-economic status and racial/ethnic minority groups. In terms of caregivers, these disparities contribute to increased stress and negative physical health. (2) Accordingly, the scientific premise of this proposal is built upon evidence demonstrating negative impacts on health, well-being, and quality of life in caregivers of children with cancer and socio-economic and ethnic disparities in cancer health outcomes that place low-income Latino caregivers at greater risk for negative effects compared to white caregivers.
Little research is reported for dementia caregiver interventions in underserved minorities and one given at home by community health workers (CHWs). The proposed intervention meets the needs of these family caregivers in developing a positive relationship with the PWD by educating caregivers to better understand the PWD’s behaviors. Another component of the intervention is stress reduction techniques, including mindful deep breathing and compassionate support/listening to reduce depression and improve family relationships making the caregiving less burdensome. By monitoring the physiological responses of stress (i.e. heart rate variability), sleep and activity, we can objectively measure changes as a result of the intervention. Using Wearable Internet of Things (WIoT) technology, a combination of Watch/ring-Smartphone-Cloud, has proven to be a significant method of monitoring behavioral and physiological measures providing evidence of change over time and uniquely associated with this intervention.
C-STRESS addresses the mental health needs of college students with an innovative mobile application to deliver Cognitively-Based Compassion Training (CBCT), a mind-training intervention that builds skills of attention, mindfulness, and traditional cognitive behavioral techniques, actualized through meditation, to cultivate compassion for self and others. The ultimate goal of C-STRESS is to revolutionize the delivery of CBCT and strengthen college students’ cognitive resilience to stress and improve mental health outcomes.
This Future of Work at the Human Technology Frontier planning grant lays the foundation for a new digital health enabled community-centered care (D-CCC) model that will transform the manual, restricted, and unstructured state of the current community healthcare landscape into a scalable, digital, and automated space. The D-CCC model will improve: the future of work – the nature of healthcare services delivered to home/community; the future of workers – training and empowering registered nurses and community healthcare workers (CHWs) to improve quality of serving capabilities; and the future of technology – using artificial intelligence-enabled cognitive Cyber-Physical System (CPS) models for novel and scalable healthcare services. The D-CCC model will achieve scalability by using technology to allow CHWs to serve an expanded patient population, including underserved, elderly, disabled and vulnerable groups.
This study will contribute to the current knowledge base about the reach and implementation fidelity of the new guidelines through the following aims: 1) to determine civil surgeons’ adherence to new Centers for Disease Control (CDC) guidelines, specifically the percentage of civil surgeons who screen using a blood test, report LTBI-positive GCAs to the health department, and inform GCAs of their LTBI diagnosis; 2) to determine the effect of the implementation of new CDC guidelines on LTBI treatment rates among GCAs; and 3) to explore facilitators and potentially modifiable barriers to guideline adherence among civil surgeons. We will generate critical preliminary data for developing public health outreach programs to maximize the uptake of the new guidelines and, ultimately, prevent TB among GCAs. As TB screening is already routinely done in this population, focusing on extending LTBI treatment services to GCAs may be a sustainable strategy that substantially contributes to TB elimination in the US. This fellowship training will take place at the University of California-Irvine and will enable the applicant to achieve the following goals: 1) mastery of skills for quantitative research, specifically in research design, statistical analysis, and survey building; 2) mastery of skills for qualitative research, specifically in qualitative study design, fieldwork analysis, data collection, interview techniques, content analysis, and write-up of qualitative data; 3) expand knowledge of theories, models, and frameworks of implementation science; and 4) engagement in health disparities, migrant health, and community-based research.
The proposed research will address the current gaps in knowledge by incorporating two novel methods for detecting within-host M. tuberculosis complex (Mtbc) heterogeneity: 1) we will conduct whole-genome sequencing (WGS) on early primary culture samples to detect heterogeneous Mtbc strains; and 2) we will perform targeted amplicon-based sequencing of 150 genetic loci important for phylogenetic and resistance prediction. We will use advanced bioinformatic methods to integrate these sources of data on Mtbc heterogeneity. The proposed research will also utilize community-based door-to-door active case finding to minimize sampling bias. These methods will be applied to achieve 2 specific aims: 1) to determine the impact of accounting for within-host heterogeneity of Mtbc strains on inference in a population-based TB transmission study; and 2) to determine more accurately the proportion of household TB cases that are attributable to transmission within the household by conducting a prospective household contact study. We will also determine pathogen and host factors that predict individual and population-level transmission. This project will generate important scientific knowledge of TB transmission and factors that affect transmission, and findings will inform and guide targeted interventions to combat TB epidemics by interrupting the transmission network in local settings.