Browsing by Author "Swartz, Michael D."
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Item Impact of a Mobile Phone Intervention to Reduce Sedentary Behavior in a Community Sample of Adults: A Quasi-Experimental Evaluation(JMIR Publications, 2016) Kendzor, Darla E.; Shuval, Kerem; Gabriel, Kelley Pettee; Businelle, Michael S.; Ma, Ping; High, Robin R.; Cuate, Erica L.; Poonawalla, Insiya B.; Rios, Debra M.; Demark-Wahnefried, Wendy; Swartz, Michael D.; Wetter, David W.Background: Greater time spent sedentary is linked with increased risk of breast, colorectal, ovarian, endometrial, and prostate cancers. Given steadily increasing rates of mobile phone ownership, mobile phone interventions may have the potential to broadly influence sedentary behavior across settings. Objective: The purpose of this study was to examine the short-term impact of a mobile phone intervention that targeted sedentary time in a diverse community sample. Methods: Adults participated in a quasi-experimental evaluation of a mobile phone intervention designed to reduce sedentary time through prompts to interrupt periods of sitting. Participants carried mobile phones and wore accelerometers for 7 consecutive days. Intervention participants additionally received mobile phone prompts during self-reported sitting and information about the negative health impact of prolonged sedentariness. The study was conducted from December 2012 to November 2013 in Dallas, Texas. Linear mixed model regression analyses were conducted to evaluate the influence of the intervention on daily accelerometer-determined estimates of sedentary and active time. Results: Participants (N=215) were predominantly female (67.9%, 146/215) and nonwhite (black: 50.7%, 109/215; Latino: 12.1%, 26/215; other: 5.6%, 12/215). Analyses revealed that participants who received the mobile phone intervention had significantly fewer daily minutes of sedentary time (B=–22.09, P=.045) and more daily active minutes (B=23.01, P=.04) than control participants. Conclusions: A simple mobile phone intervention was associated with engaging in less sedentary time and more physical activity. Findings underscore the potential impact of mobile phone interventions to positively influence sedentary behavior and physical activity.Item Investigating Multiple Candidate Genes and Nutrients in the Folate Metabolism Pathway to Detect Genetic and Nutritional Risk Factors for Lung Cancer(Public Library of Science, 2013) Swartz, Michael D.; Peterson, Christine B.; Lupo, Philip J.; Wu, Xifeng; Forman, Michele R.; Spitz, Margaret R.; Hernandez, Ladia M.; Vannucci, Marina; Shete, SanjayPurpose: Folate metabolism, with its importance to DNA repair, provides a promising region for genetic investigation of lung cancer risk. This project investigates genes (MTHFR, MTR, MTRR, CBS, SHMT1, TYMS), folate metabolism related nutrients (B vitamins, methionine, choline, and betaine) and their gene-nutrient interactions. Methods: We analyzed 115 tag single nucleotide polymorphisms (SNPs) and 15 nutrients from 1239 and 1692 non-Hispanic white, histologically-confirmed lung cancer cases and controls, respectively, using stochastic search variable selection (a Bayesian model averaging approach). Analyses were stratified by current, former, and never smoking status. Results: Rs6893114 in MTRR (odds ratio [OR] = 2.10; 95% credible interval [CI]: 1.20–3.48) and alcohol (drinkers vs. non-drinkers, OR = 0.48; 95% CI: 0.26–0.84) were associated with lung cancer risk in current smokers. Rs13170530 in MTRR (OR = 1.70; 95% CI: 1.10–2.87) and two SNP*nutrient interactions [betaine*rs2658161 (OR = 0.42; 95% CI: 0.19–0.88) and betaine*rs16948305 (OR = 0.54; 95% CI: 0.30–0.91)] were associated with lung cancer risk in former smokers. SNPs in MTRR (rs13162612; OR = 0.25; 95% CI: 0.11–0.58; rs10512948; OR = 0.61; 95% CI: 0.41–0.90; rs2924471; OR = 3.31; 95% CI: 1.66–6.59), and MTHFR (rs9651118; OR = 0.63; 95% CI: 0.43–0.95) and three SNP*nutrient interactions (choline*rs10475407; OR = 1.62; 95% CI: 1.11–2.42; choline*rs11134290; OR = 0.51; 95% CI: 0.27–0.92; and riboflavin*rs8767412; OR = 0.40; 95% CI: 0.15–0.95) were associated with lung cancer risk in never smokers. Conclusions: This study identified possible nutrient and genetic factors related to folate metabolism associated with lung cancer risk, which could potentially lead to nutritional interventions tailored by smoking status to reduce lung cancer risk.Item Stochastic search gene suggestion: Hierarchical Bayesian model selection meets gene mapping(2004) Swartz, Michael D.; Kimmel, Marek; Amos, ChrisThis dissertation introduces a novel approach for addressing the complexities of mapping a complex disease by adjusting a Bayesian Model Selection method. Mapping the genes for a complex disease, such as Rheumatoid Arthritis, involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. To avoid multiple tests, one can look at haplotypes, or linear sets of loci, but this results in a contingency table with sparse counts, especially when using marker loci with multiple alleles. In order to jointly consider all loci in the problem, we applied a Hierarchical Bayesian Model Selection method to a conditional logistic regression model used in gene mapping. We chose Stochastic Search Variable Selection for its use of latent indicator variables to indicate those covariates, in this case genes, important to the model. We extended the latent variable structure to mirror genetics through a latent allele indicator conditional on a latent locus indicator. We also examined using a prior correlation structure on the allele coefficients that mirrors linkage disequilibrium, a between-locus genetic correlation structure. Ultimately, we ruled out the usefulness of a dependent covariance structure on the prior for allele main effects, but we developed a preliminary method of fitting a positive definite matrix to data based on adjusting the kriging covariance functions commonly used in geostatistics or spatial statistics. We developed a Metropolis-within-Gibbs algorithm to sample our gene suggestion posterior, and evaluated the algorithm's performance on simulated data and completed the research with application to real data, searching for genes associated with Rheumatoid Arthritis. On simulated data, we found that our method successfully recognized disease loci and nondisease loci. Despite complications when analyzing the real data, our method did indicate the genes more strongly associated with Rheumatoid Arthritis.