Matching Items (143)
Description
Recent research has confirmed and revealed many physical and mental benefits of yoga. The practice of yoga has spread throughout the western world, where it is widely used for the purpose of exercise and fitness. Due to its rise in popularity, there is a need for research regarding the energy expenditure required for different types of yoga. The majority of the literature cites yoga as being an effective exercise for light intensity activity, but there are not as many studies attempting to determine if there are postures and sequences that can meet the requirements for moderate physical activity. In addition, there is a need to validate mobile devices with which to measure energy expenditure (EE) that are compatible with the dynamic movements that occur during yoga. The purpose of this study was to measure energy expenditure of twenty-two yoga practitioners of varying experience during a 30-minute Vinyasa flow yoga practice and from this data collection determine: if an ashtanga-based vinyasa yoga class meets the criteria for moderate intensity physical activity, the reliability between the Actigraph and Oxycon Mobile and the validity of an Actigraph GT3X device worn on the hip in estimating energy expenditure for ashtanga-based vinyasa flow yoga. The Actigraph GT3X and the Oxycon mobile were used to measure energy expenditure. Mean values for energy expenditure recorded by the Oxycon and Actigraph were 3.19 ± 0.42 METs and 1.16 ± 0.23 METs respectively, exhibiting a significant difference in data collection. There was no correlation between the values recorded by the two devices, indicating that the Actigraph was not consistent with the Oxycon Mobile (previously validated for measurement of EE). Results of this study indicate that this Vinyasa flow yoga sequence does satisfy the criteria for moderate intensity physical activity as defined by ACSM with an average EE of 3.19 ± 0.42 METs, and that the Actigraph GT3X is not an accurate device for measurement of EE for yoga.
ContributorsHand, Lindsay Gabrielle (Author) / Huberty, Jennifer (Thesis director) / Buman, Matthew (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.
ContributorsSullivan, Mariah (Author) / Stecher, Chad (Thesis advisor) / Huberty, Jennifer (Committee member) / Buman, Matthew (Committee member) / Larkey, Linda (Committee member) / Chung, Yunro (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation research project developed as an urgent response to physical inactivity, which has resulted in increased rates of obesity, diabetes, and metabolic disease worldwide. Incorporating enough daily physical activity (PA) is challenging for most people. This research aims to modulate the brain's reward systems to increase motivation for PA and, thus, slow the rapid increase in sedentary lifestyles. Transcranial direct current stimulation (tDCS) involves brain neuromodulation by facilitating or inhibiting spontaneous neural activity. tDCS applied to the dorsolateral prefrontal cortex (DLPFC) increases dopamine release in the striatum, an area of the brain involved in the reward–motivation pathways. I propose that a repeated intervention, consisting of tDCS applied to the DLPFC followed by a short walking exercise stimulus, enhances motivation for PA and daily PA levels in healthy adults. Results showed that using tDCS followed by short-duration walking exercise may enhance daily PA levels in low-physically active participants but may not have similar effects on those with higher levels of daily PA. Moreover, there was a significant effect on increasing intrinsic motivation for PA in males, but there were no sex-related differences in PA. These effects were not observed during a 2-week follow-up period of the study after the intervention was discontinued. Further research is needed to confirm and continue exploring the effects of tDCS on motivation for PA in larger cohorts of sedentary populations. This novel research will lead to a cascade of new evidence-based technological applications that increase PA by employing approaches rooted in biology.
ContributorsRuiz Tejada, Anaissa (Author) / Katsanos, Christos (Thesis advisor) / Neisewander, Janet (Committee member) / Sadleir, Rosalind (Committee member) / Buman, Matthew (Committee member) / Arizona State University (Publisher)
Created2023
Description
Climate change is one of the most pressing issues affecting the world today. One of the impacts of climate change is on the transmission of mosquito-borne diseases (MBDs), such as West Nile Virus (WNV). Climate is known to influence vector and host demography as well as MBD transmission. This dissertation addresses the questions of how vector and host demography impact WNV dynamics, and how expected and likely climate change scenarios will affect demographic and epidemiological processes of WNV transmission. First, a data fusion method is developed that connects non-autonomous logistic model parameters to mosquito time series data. This method captures the inter-annual and intra-seasonal variation of mosquito populations within a geographical location. Next, a three-population WNV model between mosquito vectors, bird hosts, and human hosts with infection-age structure for the vector and bird host populations is introduced. A sensitivity analysis uncovers which parameters have the most influence on WNV outbreaks. Finally, the WNV model is extended to include the non-autonomous population model and temperature-dependent processes. Model parameterization using historical temperature and human WNV case data from the Greater Toronto Area (GTA) is conducted. Parameter fitting results are then used to analyze possible future WNV dynamics under two climate change scenarios. These results suggest that WNV risk for the GTA will substantially increase as temperature increases from climate change, even under the most conservative assumptions. This demonstrates the importance of ensuring that the warming of the planet is limited as much as possible.
ContributorsMancuso, Marina (Author) / Milner, Fabio A (Thesis advisor) / Kuang, Yang (Committee member) / Kostelich, Eric (Committee member) / Eikenberry, Steffen (Committee member) / Manore, Carrie (Committee member) / Arizona State University (Publisher)
Created2023
Description
The relationship between sleep and physical activity is an area of growing scientific interest, particularly in the context of older adults. The importance of examining long sleep duration and its influence on physical activity in this demographic becomes increasingly relevant given rising healthcare costs. This dissertation aims to investigate this intricate relationship via secondary analysis by examining the effects of moderate time-in-bed (TIB) restriction (60 minutes per night)) on various intensities of physical activity (sedentary, light, moderate, vigorous, moderate-vigorous physical activity) in older adults classified as long sleepers and average duration sleepers. It was hypothesized that moderate TIB restriction would result in differential changes in physical activity levels across various intensities, with long sleepers exhibiting increased physical activity and average sleepers displaying decreased activity, potentially influenced by alterations in TST (total sleep time) and SE (sleep efficiency). Utilizing a randomized controlled trial design, this study examined the effect of treatment changes in objectively measures activity (waist actigraphy) and subjects physical activity levels as measured by the Godin Leisure-Time Exercise Questionnaire . Eligible participants were long sleepers (sleeping > 9 hours per night) and average sleepers (sleeping 7-9 hours per night). Both types of sleepers were either randomized to TIB restriction or asked to maintain their average sleep patterns. Mean TIB restriction compared with baseline was 39.5 minutes in average sleepers and 52.9 minutes in long sleepers randomized to TIB restriction . Contrary to the original hypothesis, no significant effect of TIB restriction was observed across all physical activity levels in either long sleepers or average sleepers. However, a notable association was found between increased sleep efficiency (+0.09% [SD = ± 4.64%]) and light physical activity (±31 minutes [SD = ± 104.81, R=0.445, P < 0.007]) in long sleepers undergoing TIB restriction. While this study presents several methodological limitations, including its nature as a secondary analysis and the less-than-intended achievement of TIB restriction, it adds a valuable layer to the existing body of research on sleep and physical activity in older adults. The findings suggest that moderate TIB restriction may not be sufficiently impactful to change behavior in physical activity levels, thus highlighting the need for more nuanced, targeted research in this domain.
ContributorsPerry, Christopher (Author) / Youngstedt, Shawn D (Thesis advisor) / Petrov, Megan (Committee member) / Swan, Pamela (Committee member) / Buman, Matthew (Committee member) / Ringenbach, Shannon (Committee member) / Arizona State University (Publisher)
Created2023
Description
Over the past 20 years, the fields of synthetic biology and synthetic biosystems engineering have grown into mature disciplines, leading to significant breakthroughs in cancer research, diagnostics, cell-based medicines, biochemical production, etc. Application of mathematical modelling to biological and biochemical systems have not only given great insight into how these systems function, but also have lent enough predictive power to aid in the forward-engineering of synthetic constructs. However, progress has been impeded by several modes of context-dependence unique to biological and biochemical systems that are not seen in traditional engineering disciplines, resulting in the need for lengthy design-build-test cycles before functional prototypes are generated.In this work, two of these universal modes of context dependence – resource competition and growth feedback –their effects on synthetic gene circuits and potential control mechanisms, are studied and characterized. Results demonstrate that a novel competitive control architecture can be utilized to mitigate the effects of winner-take-all resource competition (a form of context dependence where distinct gene modules influence each other by competing over a shared pool of transcriptional/translational resources) in synthetic gene circuits and restore circuits to their intended function. Application of the fluctuation-dissipation theorem and rigorous stochastic simulations demonstrate that realistic resource constraints present in cells at the transcriptional and translational levels influence noise in gene circuits in a nonmonotonic fashion, either increasing or decreasing noise depending on the transcriptional/translational capacity.
Growth feedback on the other hand links circuit function to cellular growth rate via increased protein dilution rate during exponential growth phase. This in turn can result in the collapse of bistable gene circuits as the accelerated dilution rate forces switches in a high stable state to fall to a low stable state. Mathematical modelling and experimental data demonstrate that application of repressive links can insulate sensitive parts of gene circuits against growth-fluctuations and can in turn increase the robustness of multistable circuits in growth contexts.
The results presented in this work aid in the accumulation of understanding of biological and biochemical context dependence, and corresponding control strategies and design principles engineers can utilize to mitigate these effects.
ContributorsStone, Austin (Author) / Tian, Xiao-jun (Thesis advisor) / Wang, Xiao (Committee member) / Smith, Barbara (Committee member) / Kuang, Yang (Committee member) / Cheng, Albert (Committee member) / Arizona State University (Publisher)
Created2023
Description
The purpose of this investigation was to evaluate the influence of tap water safety perceptions on plain water intake (PWI) and hydration status in US Latinx adults. Participants (n=492; age, 28±7 y; 37.4% female) completed an Adapted Survey of Water Issues in Arizona and household watersecurity experience-based scales. A sub-sample (n=55; age, 33±14 y; body mass index, 27.77±6.60 kg·m2) completed dietary recalls on two weekdays and one weekend day via Automated Self-Administered 24-hour Dietary Assessment Tool to determine average PWI and total water intake (TWI). A 24-h urine sample was collected on one recall day and analyzed for urine osmolality (UOsm). Binary logistic regression determined odds ratios (OR) for the odds of perceiving tap water to be unsafe. Hierarchical linear regression was employed with 24-h UOsm and PWI as primary outcomes for the sub-sample. Overall, 51.2% of all participants and 52.7% of the sub-sample mistrust their tap water safety. The odds of mistrusting tap water were significantly greater (P<0.05) for each additional favorable perception of bottled over tap water (OR=1.94, 95% CI=1.50, 2.50), each additional negative home tap water experience (OR=1.32, 95% CI=1.12, 1.56), each additional use of alternatives and/or modifications to home tap water (OR=1.25, 95% CI=1.04, 1.51), and decreased water quality and acceptability (OR=1.21, 95% CI=1.01, 1.45). The odds of mistrusting tap water were significantly lower (P<0.05) for those whose primary source of drinking water is the public supply (municipal) (OR=0.07, 95% CI=0.01, 0.63) and for those with decreased water access (OR=0.56, 95% CI=0.48, 0.66). There were no differences (n=55, P>0.05) in TWI (2,678±1,139 mL), PWI (1,357±971), or 24-h UOsm (460±234 mosm·kg-1). Tap water safety perceptions did not significantly explain variance in PWI or 24-h UOsm (P > 0.05). In conclusion, Latinx mistrust in tap water safety is prevalent. Mistrust appears to be influenced by organoleptic perceptions and to lead to reliance on alternatives to the home drinking water system. Perceptions of tap water safety do not appear to be related to PWI, TWI, or hydration status in Latinx adults.
ContributorsColburn, Abigail (Author) / Kavouras, Stavros (Thesis advisor) / Buman, Matthew (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Vega-Lopez, Sonia (Committee member) / Wutich, Amber (Committee member) / Arizona State University (Publisher)
Created2022
Description
There is a need in the ecology literature to have a discussion about the fundamental theories from which population dynamics arises. Ad hoc model development is not uncommon in the field often as a result of a need to publish rapidly and frequently. Ecologists and statisticians like Robert J. Steidl and Kenneth P Burnham have called for a more deliberative approach they call "hard thinking". For example, the phenomena of population growth can be captured by almost any sigmoid function. The question of which sigmoid function best explains a data set cannot be answered meaningfully by statistical regression since that can only speak to the validity of the shape. There is a need to revisit enzyme kinetics and ecological stoichiometry to properly justify basal model selection in ecology. This dissertation derives several common population growth models from a generalized equation. The mechanistic validity of these models in different contexts is explored through a kinetic lens. The behavioral kinetic framework is then put to the test by examining a set of biologically plausible growth models against the 1968-1995 elk population count data for northern Yellowstone. Using only this count data, the novel Monod-Holling growth model was able to accurately predict minimum viable population and life expectancy despite both being exogenous to the model and data set. Lastly, the elk/wolf data from Yellowstone was used to compare the validity of the Rosenzweig-MacArthur and Arditi-Ginzburg models. They both were derived from a more general model which included both predator and prey mediated steps. The Arditi-Ginzburg model was able to fit the training data better, but only the Rosenzweig-MacArthur model matched the validation data. Accounting for animal sexual behavior allowed for the creation of the Monod-Holling model which is just as simple as the logistic differential equation but provides greater insights for conservation purposes. Explicitly acknowledging the ethology of wolf predation helps explain the differences in predictive performances by the best fit Rosenzweig-MacArthur and Arditi-Ginzburg models. The behavioral kinetic framework has proven to be a useful tool, and it has the ability to provide even further insights going forward.
ContributorsPringle, Jack Andrew McCracken (Author) / Anderies, John M (Thesis advisor) / Kuang, Yang (Committee member) / Milner, Fabio (Committee member) / Arizona State University (Publisher)
Created2022
Description
Ecology has been an actively studied topic recently, along with the rapid development of human microbiota-based technology. Scientists have made remarkable progress using bioinformatics tools to identify species and analyze composition. However, a thorough understanding of interspecies interactions of microbial ecosystems is still lacking, which has been a significant obstacle in the further development of related technologies. In this work, a genetic circuit design principle with synthetic biology approaches is developed to form two-strain microbial consortia with different inter-strain interactions. The microbial systems are well-defined and inducible. Co-culture experiment results show that our microbial consortia behave consistently with previous ecological knowledge and thus serves as excellent model systems to simulate ecosystems with similar interactions. Colony patterns also emerge when co-culturing multiple species on solid media. With the engineered microbial consortia, image-processing based methods were developed to quantify the shape of co-culture colonies and distinguish microbial consortia with different interactions. Factors that affect the population ratios were identified through induction and variations in the inoculation process. Further time-lapse experiments revealed the basic rules of colony growth, composition variation, patterning, and how spatial factors impact the co-culture colony.
ContributorsChen, Xingwen (Author) / Wang, Xiao (Thesis advisor) / Kuang, Yang (Committee member) / Tian, Xiaojun (Committee member) / Brafman, David (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2022