Matching Items (307)
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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

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
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It is commonly accepted that undergraduate degree attainment rates must improve if postsecondary educational institutions are to meet macroeconomic demands. Involvement in co-curricular activities, such as student clubs and organizations, has been shown to increase students' satisfaction with their college experience and the rates by which they might persist. Yet,

It is commonly accepted that undergraduate degree attainment rates must improve if postsecondary educational institutions are to meet macroeconomic demands. Involvement in co-curricular activities, such as student clubs and organizations, has been shown to increase students' satisfaction with their college experience and the rates by which they might persist. Yet, strategies that college administrators, faculties, and peer leaders may employ to effectively promote co-curricular engagement opportunities to students are not well developed. In turn, I created the Sky Leaders program, a retention-focused intervention designed to promote commuter student involvement in academically-purposeful activities via faculty- and peer-lead mentoring experiences. Working from an interpretivist research paradigm, this quasi-experimental mixed methods action research study was intended to measure the intervention's impact on participants' re-enrollment and reported engagement rates, as well as the effectiveness of its conceptual and logistical aspects. I used enrollment, survey, interview, observation, and focus group data collection instruments to accommodate an integrated data procurement process, which allowed for the consideration of several perspectives related to the same research questions. I analyzed all of the quantitative data captured from the enrollment and survey instruments using descriptive and inferential statistics to explore statistically and practically significant differences between participant groups. As a result, I identified one significant finding that had a perceived positive effect. Expressly, I found the difference between treatment and control participants' reported levels of engagement within co-curricular activities to be statistically and practically significant. Additionally, consistent with Glaser and Strauss' grounded theory approach, I employed open, axial, and selective coding procedures to analyze all of the qualitative data obtained via open-ended survey items, as well as interview, observation, and focus group instruments. After I reviewed and examined the qualitative data corpus, I constructed six themes reflective of the participants' programmatic experiences as well as conceptual and logistical features of the intervention. In doing so, I found that faculty, staff, and peer leaders may efficaciously serve in specific mentoring roles to promote co-curricular engagement opportunities and advance students' institutional academic and social integration, thereby effectively curbing their potential college departure decisions, which often arise out of mal-integrative experiences.
ContributorsSebold, Brent (Author) / Beardsley, Audrey (Thesis advisor) / Serafini, Frank (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Arizona State University (Publisher)
Created2011
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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

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
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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

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
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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 grou

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
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Description
Community Supported Agriculture programs (CSAs) have become a viable local source of fresh agricultural goods and represent a potentially new way to improve fruit and vegetable consumption among individuals and families. Studies concerning CSAs have focused mainly on characteristics of the typical CSA member and motivations and barriers to join

Community Supported Agriculture programs (CSAs) have become a viable local source of fresh agricultural goods and represent a potentially new way to improve fruit and vegetable consumption among individuals and families. Studies concerning CSAs have focused mainly on characteristics of the typical CSA member and motivations and barriers to join a CSA program. The purpose of this study was to examine whether behavior and attitudinal differences existed between current CSA members and a nonmember control group. Specifically, ecological attitudes, eating out behaviors, composting frequency, and family participation in food preparation were assessed. This study utilized an online survey comprising items from previous survey research as well as newly created items. A total of 115 CSA member and 233 control survey responses were collected. CSA members were more likely to be older, have more education, and have a higher income than the control group. The majority of CSA members surveyed were female, identified as non-Hispanic and Caucasian, earned a higher income, and reported being the primary food shopper and preparer. The majority of members also noted that the amount and variety of fruits and vegetables they ate and served their family increased as a result of joining a CSA. CSA members were more ecologically minded compared to the control group. Frequency of eating out was not significantly different between groups. However, eating out behaviors were different between income categories. CSA members spent significantly more money at each meal eaten away from home and spent significantly more money on eating out each week. In both cases, controlling for income attenuated differences between groups. CSA members composted at a significantly higher rate and took part in other eco-friendly behaviors more often than the control group. Finally, no significant difference was evident between the two groups when analyzing family involvement in food preparation and meal decision-making. Overall, some significant attitudinal and behavioral differences existed between CSA members and non-CSA members. Further research is necessary to examine other distinctions between the two groups and whether these differences occur as a result of CSA membership.
ContributorsMacMillan Uribe, Alexandra L (Author) / Wharton, Christopher (Christopher Mack), 1977- (Thesis advisor) / Winham, Donna (Committee member) / Eakin, Hallie (Committee member) / Arizona State University (Publisher)
Created2011
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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

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
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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 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
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Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of

Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
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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

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