Matching Items (292)
Description
Dietary self-monitoring has been shown to be a predictor of weight loss success and is a prevalent part of behavioral weight control programs. As more weight loss applications have become available on smartphones, this feasibility study investigated whether the use of a smartphone application, or a smartphone memo feature would improve dietary self-monitoring over the traditional paper-and-pencil method. The study also looked at whether the difference in methods would affect weight loss. Forty-seven adults (BMI 25 to 40 kg/m2) completed an 8-week study focused on tracking the difference in adherence to a self-monitoring protocol and subsequent weight loss. Participants owning iPhones (n=17) used the 'Lose It' application (AP) for diet and exercise tracking and were compared to smartphone participants who recorded dietary intake using a memo (ME) feature (n=15) on their phone and participants using the traditional paper-and-pencil (PA) method (n=15). There was no significant difference in completion rates between groups with an overall completion rate of 85.5%. The overall mean adherence to self-monitoring for the 8-week period was better in the AP group than the PA group (p = .024). No significant difference was found between the AP group and ME group (p = .148), or the ME group and the PA group (p = .457). Weight loss for the 8 week study was significant for all groups (p = .028). There was no significant difference in weight loss between groups. Number of days recorded regardless of group assignment showed a weak correlation to weight loss success (p = .068). Smartphone owners seeking to lose weight should be encouraged by the potential success associated with dietary tracking using a smartphone app as opposed to the traditional paper-and-pencil method.
ContributorsCunningham, Barbara (Author) / Wharton, Christopher (Christopher Mack), 1977- (Thesis advisor) / Johnston, Carol (Committee member) / Hall, Richard (Committee member) / Arizona State University (Publisher)
Created2012
Description
Nut consumption, specifically almonds, have been shown to help maintain weight and influence disease risk factors in adult populations. Limited studies have been conducted examining the effect of a small dose of almonds on energy intake and body weight. The objective of this study was to determine the influence of pre-meal almond consumption on energy intake and weight in overweight and obese adults. In this study included 21, overweight or obese, participants who were considered healthy or had a controlled disease state. This 8-week parallel arm study, participants were randomized to consume an isocaloric amount of almonds, (1 oz) serving, or two (2 oz) cheese stick serving, 30 minutes before the dinner meal, 5 times per week. Anthropometric measurements including weight, waist circumference, and body fat percentage were recorded at baseline, week 1, 4, and 8. Measurement of energy intake was self-reported for two consecutive days at week 1, 4 and 8 using the ASA24 automated dietary program. The energy intake after 8 weeks of almond consumption was not significantly different when compared to the control group (p=0.965). In addition, body weight was not significantly reduced after 8 weeks of the almond intervention (p=0.562). Other parameters measured in this 8-week trial did not differ between the intervention and the control group. These data presented are underpowered and therefore inconclusive on the effects that 1 oz of almonds, in the diet, 5 per week has on energy intake and bodyweight.
ContributorsMcBride, Lindsey (Author) / Johnston, Carol (Thesis advisor) / Swan, Pamela (Committee member) / Mayol-Kreiser, Sandra (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Epidemiological studies have suggested a link between nut consumption and weight. The possible effects of regular nut consumption as a method of weight loss has shown minimal results with 2-3 servings of nut products per day. This 8 week study sought to investigate the effect of more modest nut consumption (1 oz./day, 5 days/week) on dietary compensation in healthy overweight individuals. Overweight and obese participants (n = 28) were recruited from the local community and were randomly assigned to either almond (NUT) or control (CON) group in this randomized, parallel-arm study. Subjects were instructed to eat their respective foods 30 minutes before the dinner meal. 24 hour diet recalls were completed pre-trial and at study weeks 1, 4 and 8. Self-reported satiety data were completed at study weeks 1, 4, and 8. Attrition was unexpectedly high, with 13 participants completing 24 dietary recall data through study week 8. High attrition limited statistical analyses. Results suggested a lack of effect for time or interaction for satiety data (within groups p = 0.997, between groups p = 0.367). Homogeneity of of inter-correlations could not be tested for 24-hour recall data as there were fewer than 2 nonsingular cell covariance matrices. In conclusion, this study was unable to prove or disprove the effectiveness of almonds to induce dietary compensation.
ContributorsJahns, Marshall (Author) / Johnston, Carol (Thesis advisor) / Hall, Richard (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT This study evaluated the LoseIt Smart Phone app by Fit Now Inc. for nutritional quality among users during an 8 week behavioral modification weight loss protocol. All participants owned smart phones and were cluster randomized to either a control group using paper and pencil record keeping, a memo group using a memo function on their smart phones, or the LoseIt app group which was composed of the participants who owned iPhones. Thirty one participants completed the study protocol: 10 participants from the LoseIt app group, 10 participants from the memo group, and 11 participants from the paper and pencil group. Food records were analyzed using Food Processor by ESHA and the nutritional quality was scored using the Healthy Eating Index - 2005 (HEI-2005). Scores were compared using One-Way ANOVA with no significant changes in any category across all groups. Non-parametric statistics were then used to determine changes between combined memo and paper and pencil groups and the LoseIt app group as the memo and paper and pencil group received live counseling at biweekly intervals and the LoseIt group did not. No significant difference was found in HEI scores across all categories, however a trend was noted for total HEI score with higher scores among the memo and paper and pencil group participants p=0.091. Conclusion, no significant difference was detected between users of the smart phone app LoseIt and memo and paper and pencil groups. More research is needed to determine the impact of in-person counseling versus user feedback provided with the LoseIt smart phone app.
ContributorsCowan, David Kevin (Author) / Johnston, Carol (Thesis advisor) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Mayol-Kreiser, Sandra (Committee member) / Arizona State University (Publisher)
Created2011
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Overcrowding of Emergency Departments (EDs) put the safety of patients at risk. Decision makers implement Ambulance Diversion (AD) as a way to relieve congestion and ensure timely treatment delivery. However, ineffective design of AD policies reduces the accessibility to emergency care and adverse events may arise. The objective of this dissertation is to propose methods to design and analyze effective AD policies that consider performance measures that are related to patient safety. First, a simulation-based methodology is proposed to evaluate the mean performance and variability of single-factor AD policies in a single hospital environment considering the trade-off between average waiting time and percentage of time spent on diversion. Regression equations are proposed to obtain parameters of AD policies that yield desired performance level. The results suggest that policies based on the total number of patients waiting are more consistent and provide a high precision in predicting policy performance. Then, a Markov Decision Process model is proposed to obtain the optimal AD policy assuming that information to start treatment in a neighboring hospital is available. The model is designed to minimize the average tardiness per patient in the long run. Tardiness is defined as the time that patients have to wait beyond a safety time threshold to start receiving treatment. Theoretical and computational analyses show that there exists an optimal policy that is of threshold type, and diversion can be a good alternative to decrease tardiness when ambulance patients cause excessive congestion in the ED. Furthermore, implementation of AD policies in a simulation model that accounts for several relaxations of the assumptions suggests that the model provides consistent policies under multiple scenarios. Finally, a genetic algorithm is combined with simulation to design effective policies for multiple hospitals simultaneously. The model has the objective of minimizing the time that patients spend in non-value added activities, including transportation, waiting and boarding in the ED. Moreover, the AD policies are combined with simple ambulance destination policies to create ambulance flow control mechanisms. Results show that effective ambulance management can significantly reduce the time that patients have to wait to receive appropriate level of care.
ContributorsRamirez Nafarrate, Adrian (Author) / Fowler, John W. (Thesis advisor) / Wu, Teresa (Thesis advisor) / Gel, Esma S. (Committee member) / Limon, Jorge (Committee member) / Arizona State University (Publisher)
Created2011
Description
Vitamin C is a micronutrient with many important physiological roles. It can function as a reducing agent, a free radical scavenger, and an enzyme cofactor. Much research has examined the potential of vitamin C supplements to enhance exercise capacity in trained athletes; however, little is known regarding the effects of vitamin C supplements on the promotion of leisure-time physical activity in the general population. This area deserves attention since 1/3 of Americans have below adequate vitamin C status, and since aversion to exercise, fatigue, and altered mood states are the earliest signs of poor vitamin C status. This study analyzed the effect of supplementing 500 mg twice daily of vitamin C on self-reported leisure-time activity levels and mood states in young men. Twenty-nine healthy, young men, aged 18-35 years, were stratified by age, BMI, smoking status, and plasma vitamin C concentrations and assigned to either a control (CON) or experimental group (VTC) for the 8-week randomized, double-blinded, parallel arm trial. Subjects were instructed to keep track of their leisure-time physical activity by filling out the validated Godin Leisure-Time Exercise Questionnaire weekly for the entire study. In addition, subjects took the self-administered Profile of Mood States (POMS) at baseline, week 4, and week 8 to observe mood states. Plasma vitamin C concentrations were analyzed at the initial screening, week 4, and week 8 of the study. Plasma vitamin C concentrations significantly differed by group at week 4 and week 8. Furthermore, vitamin C supplementation significantly increased self-reported mild, moderate, and strenuous activity levels during the 8-week trial. Overall, total physical activity scores increased nearly 50% in the VTC group as compared to 18% in the CON group (p=0.001). However, mood states were not significantly impacted by vitamin C supplementation during the trial. This study provides the first experimental evidence that supplementing 500 mg of vitamin C twice daily can be effective in increasing leisure-time physical activity in healthy young men. This study, however, was unable to link improvements in physical activity rates to improved mood states. Since sedentary behaviors have been implicated in the rise of obesity in the U.S., further research should be conducted to substantiate the finding that vitamin C supplementation increases physical activity.
ContributorsSchumacher, Sarah (Author) / Johnston, Carol (Thesis advisor) / Appel, Christy (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2012
Description
The antioxidant, antihistamine, and chemotactic properties of vitamin C provide the theoretical basis linking vitamin C supplementation to combating the common cold; yet, the clinical evidence is mixed. To date, vitamin C intervention trials have not systematically recorded cold symptoms daily or looked at fluctuations in plasma histamine over an extended period. Also, trials have not been conducted in individuals with marginal vitamin C status. This study examined the impact of vitamin C supplementation during cold season on specific cold symptoms in a population with low plasma vitamin C concentrations. Healthy young males who were not regular smokers or training for competitive sports between the ages of 18 and 35 with below average plasma vitamin C concentrations were stratified by age, body mass index, and vitamin C status into two groups: VTC (500 mg vitamin C capsule ingested twice daily) or CON (placebo capsule ingested twice daily). Participants were instructed to fill out the validated Wisconsin Upper Respiratory Symptom Survey-21 daily for 8 weeks. Blood was sampled at trial weeks 0, 4, and 8. Plasma vitamin C concentrations were significantly different by groups at study week 4 and 8. Plasma histamine decreased 4.2% in the VTC group and increased 17.4% in the CON group between study weeks 0 and 8, but these differences were not statistically significant (p>0.05). Total cold symptom scores averaged 43±15 for the VTC group compared to 148±36 for the CON group, a 244% increase in symptoms for CON participants versus VTC participants (p=0.014). Additionally, recorded symptom severity and functional impairment scores were lower in the VCT group than the CON group (p=0.031 and 0.058, respectively). Global perception of sickness was 65% lower in the VTC group compared to the CON group (p=0.022). These results suggest that 1000 mg of vitamin C in a divided dose daily may lower common cold symptoms, cold symptom severity, and the perception of sickness. More research is needed to corroborate these findings.
ContributorsOsterday, Gillean (Author) / Johnston, Carol (Thesis advisor) / Beezhold, Bonnie (Committee member) / Vaughan, Linda (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cardiovascular disease is one of the most deadly outcomes of end stage renal disease. Bioelectrical impedance is a intriguing, yet unproven method of measuring fluid buildup in the heart, and is marketed as a early diagnostic tool for onset of cardiovascular disease. In this study, selenium supplements were given to a cohort of dialysis patients in the Phoenix metro area and their fluid tolerance was measured with thoracic biolectrical impedance. BNP was used as a correlate to see if bioelectrical impedance was correlated with heart disease. The study found no correlation between BNP and bioelectrical impedance and thus was not an accurate diagnostic tool in a medical setting.
ContributorsBrown, Patrick Michael (Author) / Johnston, Carol (Thesis director) / Orchinik, Miles (Committee member) / Tingey, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2013-05
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013