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Predicting the impact of social influence on physical activity in adults with mental health conditions
No full textStatement of the Problem
Individuals with severe mental illness (SMI) perform less physical activity (PA) and live more sedentary lifestyles than the general population (USDHHS, 2018). The physical inactivity of people with SMI is associated with an increased rate of negative physical and mental health issues and shorter life expectancy compared to the general population (DeHert et al., 2011; Seabury et al., 2019; Stubbs et al., 2015). Engaging in regular PA is an underutilized intervention that can improve the physical and mental health of the SMI population. There is a lack of understanding of how to promote PA in the population, especially how types of social influence from relationships may promote or hinder PA behaviors.
Methods
This study utilized the Social-Ecological Model to examine how individual, relationship, and community variables predict PA in the SMI population. Participants (n=141) had a diagnosis of SMI and were over the age of 18. They completed a battery of questionnaires about their PA behaviors, types and sources of social influence, the conduciveness of the community to perform PA, functional ability, and demographic variables.
Results
This study used three hierarchal regressions to examine and compare results by different relationship sources around the participants (family, friends, and healthcare providers). It was found that the negative type of inhibiting influence from all three sources was a negative predictor for PA behaviors. Family and healthcare providers who provided positive esteem support positively predicted PA behaviors, and friends who provided the negative type of justifying influence positively predicted PA behaviors.
Conclusions
This study was the first known examination of sources and types of social influence for PA behaviors in the SMI population. The results suggest that negative types of influence may be more important to consider than positive types when working with a client with SMI. The results also offer each source a unique prescription for best supporting PA behaviors. Healthcare providers, family, and friends should focus on providing positive social influence and reducing negative social influence when discussing PA with someone with an SMI.Ph.D.Includes bibliographical reference
Multi-scale habitat use to inform conservation of bats in eastern temperate forests
No full textGlobally, bat conservation faces challenges from habitat loss, diseases, wind energy facilities, persecution, and lack of information. With the majority of bat species in need of management or research, bat conservation would greatly benefit from practices that increase the efficiency of monitoring. These conservation shortcuts take various forms, but they often involve the use of a surrogate. My research seeks to identify effective surrogates for North American bat conservation over three different spatial scales. My first chapter evaluates a potential indicator taxon over broad regional scales. My second chapter attempts to identify habitat characteristics that may serve as environmental indicators at a landscape scale. In my last chapter, I propose umbrella species for bat conservation by looking at small-scale habitat selection.
In chapter 1, I use measures of cross-taxa congruence to evaluate insectivorous birds as a possible indicator taxon for bats in eastern temperate forests. To acquire data over vast spatial scales, I utilize mass-aggregated online resources for bat and bird observations over six consecutive years. Despite substantial overlap in broad-scale habitat associations, my results indicated only weak congruence between bats and birds. Therefore, the monitoring of insectivorous birds as a shortcut for bat conservation may have limited applications. However, mass-aggregated online resources may become a useful tool for conservation going forward. Because my results align well with known trends in bat and bird biodiversity, this project highlights the utility of mass-aggregated online resources for answering broad-scale ecological questions.
Because habitat characteristics can also serve as indicators for biodiversity monitoring, I attempted to identify landscape-scale characteristics that are associated with greater bat activity in a habitat patch. Because golf courses provide a relatively consistent suite of patch-scale features, I monitored bat activity on golf courses for two consecutive years to explore the correlation between bat activity and the surrounding landscape. For most species, bat activity was greater on golf courses when the surrounding landscape contained fewer open spaces and more developed land. Courses with larger patches of forest in the surrounding landscape were used more often by rare bat species like Myotis spp., further suggesting that a golf course’s conservation value varies with its surrounding landscape. This research suggests that the surrounding landscape could be a useful indicator of bat community metrics in a habitat patch, thus providing a meaningful conservation shortcut.
In my final chapter, I aim to identify certain bat species as potential conservation umbrellas according to their roost tree preferences. Many North American bat species rely on tree roosts during the maternity season during which they raise their pups. Therefore, management aimed at promoting availability of high-quality roosting habitat is an important conservation goal. I conducted a systematic review of roost-site characteristics of all tree-roosting bats in eastern temperate forests. After synthesizing the existing knowledge of roost tree preferences, I sorted the species into three roosting guilds using multi-variate statistical analyses. I propose Myotis septentrionalis and Perimyotis subflavus be considered for use as umbrella species for snag-roosting and foliage-roosting bats respectively. Both species display substantial overlap in their roosting niches with their guildmates, and their existing protections may extend to benefit other bats in their roosting guilds.
This dissertation has provided important information on bat habitat associations across several spatial scales. These patterns may reveal potential shortcuts for bat conservation in the eastern temperate forests of North America.Ph.D.Includes bibliographical reference
A sheaf-theoretic approach to continuation in dynamics
No full textWe develop a sheaf-theoretic framework to study the continuation of algebraic structures from Conley theory, such as attractor lattices, repellers, and Morse representations. These algebraic structures are expressed as functors on a category of dynamical systems in order to study their continuation systematically and simultaneously. Sheaves are built from this abstract formulation, which track the algebraic data as the underlying system varies. This construction is shown to be functorial. Then the rich theory of sheaves is exploited to study continuation. Sheaf cohomology, relative and local cohomology, and the cup product are shown to encode features of dynamics. The general theory is applied to several classical bifurcations. Each produces unique algebraic data, demonstrating the ability for sheaf cohomology to classify bifurcations. We conclude by demonstrating the application of this theory to problems in data science. Computational analogues of sheaves, cellular sheaves, are shown to yield a converging numerical method to compute sheaf cohomology. Cellular sheaves produced from DSGRN computations provide an explicit example.Ph.D.Includes bibliographical reference
Plant microbial ecology and synthetic communities of duckweed-associated bacteria
No full textBacteria are abundant plant colonists with the potential to promote plant growth. However, efforts to utilize these bacteria for plant growth promotion in agriculture have failed due to their displacement by resident soil and plant microbiota. Ultimately, harnessing the beneficial effects of plant-growth-promoting bacteria (PGPB) requires an understanding of community-level processes and underlying ecological interactions. To understand this community ecology, community profiling and synthetic community approaches are applied to terrestrial plant microbiomes. However, these approaches are complicated in terrestrial plants, which undergo sophisticated development and require a soil environment that is chemically and physically heterogeneous. Alternatively, duckweeds are a family of floating aquatic monocots with a reduced morphology, uniform clonal development, and a relatively homogenous freshwater environment.
In this dissertation research, I hypothesized that synthetic communities of duckweed-associated bacteria (DAB) could elucidate mechanisms underlying community-level processes and functions present in the broader context of the plant microbiome. I addressed this by first asking whether bacterial communities associated with duckweed are similar to those found in terrestrial plants. Secondly, I investigated whether synthetic communities of DABs could elucidate molecular mechanisms underlying community assembly.
In Chapter 2, I took a culture-independent approach and generated community profiles of DAB communities to determine their structure and whether there were structural similarities between bacterial communities of duckweed and terrestrial plants. By sampling wild DAB communities, I show that DAB communities are predominantly composed of particular families of Proteobacteria and Bacteroidetes and demonstrate a taxonomic overlap between DAB communities and terrestrial phyllosphere communities. By inoculating axenic duckweed with a wastewater microcosm, I demonstrate that duckweed influences the bacterial community in the surrounding environment, similar to the “rhizosphere effect” observed in terrestrial plants.
In Chapter 3, I focused on the development of essential molecular techniques for culture-dependent investigations of DAB communities. Among the molecular tools developed was a versatile DNA fingerprinting assay. This assay serves multiple purposes, including the validation of the axenic status of duckweed, the detection of various bacterial genera associated with duckweed, and the assessment of bacterial contamination in duckweed samples. Additionally, I developed a computational pipeline designed for creating strain-specific PCR primers to detect specific bacteria within a community context and enable the quantification of their colonization levels. These molecular tools
were validated by performing confocal microscopy on duckweed inoculated with PGPB known to display epiphytic and endophytic colonization patterns with terrestrial plants. The microscopy results not only corroborated the PCR findings but also illustrated colonization patterns of PGPB on duckweed that mirrored those observed on terrestrial plants.
In Chapter 4, I established an initial culture collection of DABs and applied the insights gained in Chapter 1 regarding DAB community structure to construct a synthetic community (SynCom) of 30 members. I introduce an alternative approach using freezer stocks to inoculate complex synthetic communities and validate a 16S rDNA amplicon sequencing workflow to characterize SynCom colonization dynamics. I then leveraged genomic approaches available with SynComs to investigate competitive interactions observed in the DAB SynCom by integrating correlation networks, metabolic networks, and the strain-specific PCR approach introduced in Chapter 3. Together, these approaches pointed towards gluconolactone, a recently identified plant prebiotic, as a possible mediator of resource competition in DAB communities.
In conclusion, my dissertation research uncovered numerous similarities in bacterial community structure and assembly between duckweed and terrestrial plants, supporting the proposition of utilizing duckweed to understand plant microbiome processes. Addressing the challenges associated with SynComs and expanding the DAB culture collection holds the potential to bridge more molecular features with community-level processes, thereby advancing our understanding of plant microbial ecology and its utilization to meet future agricultural needs.Ph.D.Includes bibliographical reference
The dorsal column nuclei scales mechanical allodynia during neuropathic pain
No full textOur ability to sense touch is vital for the navigation of our sensory environment. However, pathological conditions such as neuropathic pain can cause increased sensitivity to innocuous stimuli, and even allows for light, innocuous stimuli to elicit painful responses, known as mechanical allodynia. In normal conditions, a key pathway for the transmission of innocuous tactile information is the dorsal column nuclei (DCN), which are responsible for conveying peripheral low-threshold stimuli to the brain. Although other key regions of the central nervous system have been implicated in mediating tactile hypersensitivity and allodynia during neuropathic pain, the DCN have been largely understudied in this context, despite having a role in normal tactile sensation. Here, we identify that the DCN undergoes molecular and functional changes during neuropathic pain which contribute to a hyperactive environment, particularly during tactile stimulation. Based on this, we hypothesized that DCN circuits are hyperactive during neuropathic pain. We performed slice electrophysiology on DCN neurons and found that DCN neurons are not hyperexcitable during neuropathic pain, but have altered inputs from the periphery and spinal cord which may enhance recruitment of DCN neurons during sensory stimulation. Based on these results, we next explored the functional role of the DCN output to the thalamus, which is a key component of tactile sensation, in the context of neuropathic pain. Using chemogenetics for neuronal manipulation, we found that DCN neurons projecting to the thalamus are important for maintaining touch sensitivity, but do not affect noxious mechanical or thermal sensitivity. Lastly, we propose that the presence of local inhibitory neurons in the DCN confer the ability to scale up or scale down tactile sensitivity. Our results suggest that chemogenetic or optogenetic manipulation of DCN inhibition is able to bidirectionally scale tactile sensitivity: reduction of DCN inhibition artificially generates phenotypes of neuropathic pain, while suppression of DCN circuits during neuropathic pain is able to reduce tactile hypersensitivity, suggesting an underexplored area of interest for studying mechanisms and treatments of mechanical allodynia. Together, this data suggests that in addition to established mechanisms of mechanical allodynia, the DCN likely contributes in part to altered tactile signaling during neuropathic pain, with local inhibition as a key modulator of tactile sensitivity.Ph.D.Includes bibliographical reference
Utilization of a multidisciplinary treatment and follow-up program for COVID-19 patients in the outpatient setting
No full textBackground
Coronavirus-19 (COVID-19) has resulted in physical, cognitive, and psychological impairments in millions of Americans since the start of the COVID-19 pandemic in 2020. The aim of this program was to implement an evidence-based protocol for clinical management of those diagnosed and recovering from COVID-19 in the outpatient setting. The use of a clinical pathway was implemented to improve provider comfort with managing patients with COVID-19 in the outpatient setting. Patients with COVID-19 may present with symptoms such as dyspnea, weakness, and palpitations in the post-acute phase. They are also at higher risk for complications such as thromboembolic disease, obstructive ventilatory defect, and stroke, among other ailments during this post-acute period as well. The clinical pathway provided support for decision making regarding complication surveillance using targeted follow ups, on-site testing, and specialty referral. The surveillance testing included pulmonary function testing, echocardiograms and venous dopplers of the lower extremities.
Purpose
The purpose of this DNP project was to evaluate if the development and implementation of an outpatient treatment and follow-up program can improve provider and clinical staff confidence with providing care to COVID-19 patients in the outpatient setting.
Methodology
This quality improvement project was an exploratory pilot program that utilized the implementation of an evidence based clinical pathway for the clinical management of patients with COVID-19. Clinician feedback was obtained using an anonymous web-based feedback tool collected using a QR code in Qualtrics. The anonymous web-based feedback tool included 5 questions answered using with a 5 point Likert scale. An analysis of clinician and support staff feedback was completed to evaluate whether the clinical pathway made a clinically significant difference in improving clinician and support staff comfort with managing patients with COVID19.
Results
The outcomes measured were provider comfort, provider perception of helpfulness, frequency, and ease of use of the clinical pathway. Testing data as a result of provider usage of the clinical pathway was also collected. The results revealed 75% of clinicians and clinical staff reported improvement in their comfort with managing patients with COVID-19 in the outpatient setting after utilizing the clinical pathway. In total, 36 patients were managed using the clinical pathway. The use of this program resulted in 38 follow up visits, 22 PFTs, 12 echocardiograms, 12 Electrocardiograms, 22 chest x-rays and 7 CT scans of the chest. The results indicate that a structured approach to clinical management of patients with COVID-19 is feasible for use throughout other locations within the organization.
Implications for Practice
The results of this quality improvement project indicated the use of a clinical pathway created a structured approach to clinical management of patients with COVID –19 in the outpatient setting. This structured approach helped to promote skill development in clinical staff and comfort in their daily practice when managing patients with COVID-19.D.N.P.Includes bibliographical reference
Evaluation of an early mobility protocol on venous thromboembolism (VTE) and length of stay (LOS) in a neuroscience unit
No full textPurpose of Project: This quality improvement project sought to improve the use of the Johns Hopkins Activity and Mobility Promotion (JH-AMP) protocol in a neuroscience unit with the goal of decreasing length of stay and reducing venous thromboembolism (VTE). Methods: Nursing staff were provided additional education on the importance of early mobility and how to use the JH-AMP protocol correctly. The importance of proper documentation in the electronic medical record was emphasized. Chart audits to measure patient mobilization and proper documentation were collected and analyzed for 4 weeks prior to the educational intervention and 4 weeks after the intervention to assess for differences in adherence with the JH-AMP protocol. Descriptive and inferential statistics were used to analyze data. Results: The number of charts with proper documentation and the number of patients meeting their calculated JH-AMP mobility level increased. Regarding mobilization by shift, there was no significant increase. Length of stay decreased from an average of 6 days to 3 days, showing a significant difference between the 2 groups. No new VTEs were seen in the pre-intervention period, but one new VTE event occurred in the post-intervention period. Implications: Implementing an educational program to reinforce a new clinical practice and reminding staff on how to properly document their care showed a positive impact on mobility practices and should be further explored as a way to improve patient outcomes and decrease healthcare costs.D.N.P.Includes bibliographical reference
Rutgers-Camden Graduate Research and Creative Works Symposium 2024. Agbo Eje, Ojobo_Gola, Thembile_Kandel, Prakash_Markellos, Nicholas_Deposit and release forms
No full textPredicting type 2 diabetes risk: a comprehensive analysis of socioeconomic, behavioral, and machine learning predictors
No full textIn the United States and globally, Type 2 Diabetes presents a public health challenge. Early and accurate prediction of T2D can facilitate timely interventions, thereby mitigating the risk of long-term complications. Traditional statistical methods, while valuable, often fall short of capturing the intricate relationships among various T2D risk factors. Machine learning (ML) models, with their ability to handle large, multidimensional datasets, offer promising alternatives. This study explores the application of ML in T2D prediction, leveraging a broad spectrum of predictors from lifestyle habits and socioeconomic backgrounds to environmental exposures, aiming to identify the model that best predicts T2D risk. Methods The study analyzed data comprising 129,024 individuals, including 21,303 with type 2 diabetes, from the Center for Disease Control, 2014 Behavioral Risk Factor Surveillance System, annotated with variables including BMI, age, exercise frequency, smoking status, sleep duration, socioeconomic status (SES), healthcare access, and geographical factors. The data was split into 80% training and 20% testing sets. Six Machine Learning models (Logistic Regression, Gaussian Naive Bayes, Random Forest, Gradient Boosting, Neural Network and Decision Trees) were trained, and their performances were compared based on accuracy, sensitivity, specificity, and the Area Under Curve (AUC). Feature importance analysis was conducted to identify the most predictive variables. Results The predictive models used achieved a high area under the curve (AUC ranging from 0.69 to 0.81). However, the Gradient Boosting Model (GBM) and Neural Network outperformed others with similar results of accuracy of 0.86 and 0.85 respectively, specificity of 0.99, and AUC of 0.81. The Gaussian Naive Bayes model presented a balanced sensitivity-specificity trade-off (Accuracy: 0.80, Sensitivity: 0.43, Specificity: 0.87, AUC: 0.75) but fell short in overall accuracy and AUC compared to Gradient Boosting and Neural Network. This study highlighted that people who are unable to work (Coef = 0.5649), or who had a Health Care Coverage with the Alaska Native/Indian Health Service (Coef = 0.6096), or who have not had a medical checkup in the last 5 years or more (Coef = -1.0077) have higher risk for type 2 diabetes. Discussion The gradient boosting model showed the best model performance with the highest AUC value; however, the naive Bayes model is preferred for initial screening for type 2 diabetes because it had the highest sensitivity and, therefore, detection rate. The superior performance of GBM and NN can be attributed to its ability to handle complex interactions among a range of risk factors, from biomedical to socioeconomic and environmental. Notably, the study confirms previously reported risk factors like BMI, Age, gender, etc. by Gary Collins et al, it also identifies employment status, healthcare coverage type, and frequency of medical checkups as 3 new potential risk factors related to T2D, highlighting the potential for machine learning to uncover nuanced insights into disease prediction. By integrating these broader determinants of health, machine learning models can offer a more comprehensive tool for early disease detection, thus highlighting the critical role of machine learning in advancing personalized medicine, health informatics and public health strategies.This work was accepted to the annual Graduate Research and Creative Works Symposium while the author was a graduate student at Rutgers University-Camden
Exploring the interrelationships among trauma, religious/spiritual struggles, and substance use among emerging and young adults
No full textReligious and spiritual (r/s) struggles are a prevalent experience among adults throughout their lives. However, little is known about the interrelationships between r/s struggles, posttraumatic cognitions, and their impact on emerging and young adults (EYA) who have been exposed to trauma. This research aimed to understand the mediating effects of r/s struggles and posttraumatic cognitions on trauma exposure, symptoms of PTSD, and substance use among trauma-exposed EYA aged 18-35 years old.
The study analyzed data from 347 EYA using a parallel mediation analysis. Results showed a significant mediation by both r/s struggles and posttraumatic cognitions between trauma exposure and symptoms of PTSD. Regarding the link between trauma exposure and substance use, r/s struggles were identified as a significant mediator, whereas posttraumatic cognitions were not.
These findings suggest that interventions for symptoms of PTSD and substance use in EYA should consider assessing r/s struggles alongside cognitive approaches. Mental health counselors and counselor educators are encouraged to consider the spiritual domains of EYA and how they affect a critical time of their development.Ph.D.Includes bibliographical reference