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Almond consumption and weight loss in obese and overweight adults

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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

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.

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Date Created
2011

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Analyzing the dynamics of communication in online social networks

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This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication,

This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

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Date Created
2011

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Almond consumption and dietary compensation in overweight and obese adults

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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

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.

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Date Created
2011

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RAProp: ranking tweets by exploiting the tweet/user/web ecosystem

Description

The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone.

The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.

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Date Created
2013

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Eating in the absence of hunger in college students

Description

The body is capable of regulating hunger in several ways. Some of these hunger regulation methods are innate, such as genetics, and some, such as the responses to stress and to the smell of food, are innate but can be

The body is capable of regulating hunger in several ways. Some of these hunger regulation methods are innate, such as genetics, and some, such as the responses to stress and to the smell of food, are innate but can be affected by body conditions such as BMI and physical activity. Further, some hunger regulation methods stem from learned behaviors originating from cultural pressures or parenting styles. These latter regulation methods for hunger can be grouped into the categories: emotion, environment, and physical. The factors that regulate hunger can also influence the incidence of disordered eating, such as eating in the absence of hunger (EAH). Eating in the absence of hunger can occur in one of two scenarios, continuous EAH or beginning EAH. College students are at a particularly high risk for EAH and weight gain due to stress, social pressures, and the constant availability of energy dense and nutrient poor food options. The purpose of this study is to validate a modified EAH-C survey in college students and to discover which of the three latent factors (emotion, environment, physical) best predicts continual and beginning EAH. To do so, a modified EAH-C survey, with additional demographic components, was administered to students at a major southwest university. This survey contained two questions, one each for continuing and beginning EAH, regarding 14 factors related to emotional, physical, or environmental reasons that may trigger EAH. The results from this study revealed that the continual and beginning EAH surveys displayed good internal consistency reliability. We found that for beginning and continuing EAH, although emotion is the strongest predictor of EAH, all three latent factors are significant predictors of EAH. In addition, we found that environmental factors had the greatest influence on an individual's likelihood to continue to eat in the absence of hunger. Due to statistical abnormalities and differing numbers of factors in each category, we were unable to determine which of the three factors exerted the greatest influence on an individual's likelihood to begin eating in the absence of hunger. These results can be utilized to develop educational tools aimed at reducing EAH in college students, and ultimately reducing the likelihood for unhealthy weight gain and health complications related to obesity.

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Date Created
2013

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Individual differences in taste perception and bitterness masking

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The unpleasant bitter taste found in many nutritious vegetables may deter people from consuming a healthy diet. We investigated individual differences in taste perception and whether these differences influence the effectiveness of bitterness masking. To test whether phenylthiocarbamide (PTC) `supertasters'

The unpleasant bitter taste found in many nutritious vegetables may deter people from consuming a healthy diet. We investigated individual differences in taste perception and whether these differences influence the effectiveness of bitterness masking. To test whether phenylthiocarbamide (PTC) `supertasters' also taste salt and sugar with greater intensity, as suggested by Bartoshuk and colleagues (2004), we infused strips of paper with salt water or sugar water. The bitterness rating of the PTC strip had a significant positive linear relationship with ratings of both the intensity of sweet and salt, but the effect sizes were very low, suggesting that the PTC strip does not give a complete picture of tasting ability. Next we investigated whether various seasonings could mask the bitter taste of vegetables and whether this varied with tasting ability. We found that sugar decreased bitterness and lemon decreased liking for vegetables of varying degrees of bitterness. The results did not differ by ability to taste any of the flavors. Therefore, even though there are remarkable individual differences in taste perception, sugar can be used to improve the initial palatability of vegetables and increase their acceptance and consumption.

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Date Created
2012

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Vitamin C is not related to resting fat oxidation in healthy, non-obese adults

Description

ABSTRACT Vitamin C plays an important role in fatty acid metabolism because it is required for carnitine synthesis. Vitamin C has been shown to have an inverse relationship with weight and body fat percent in a number of studies. However,

ABSTRACT Vitamin C plays an important role in fatty acid metabolism because it is required for carnitine synthesis. Vitamin C has been shown to have an inverse relationship with weight and body fat percent in a number of studies. However, there has been limited research exploring the relationship between vitamin C status and fat oxidation. This cross-sectional study investigates the relationship between plasma vitamin C and fat oxidation in 69 participants and between plasma vitamin C and body fatness in 82 participants. Participants were measured for substrate utilization via indirect calorimetry while at rest and measured for body fatness via DEXA scan. Participants provided a single fasting blood draw for analysis of plasma vitamin C. Results did not show a significant association between vitamin C and fat oxidation while at rest, therefore the data do not support the hypothesis that vitamin C status affects fat oxidation in a resting state. However, a significant inverse association was found between vitamin C and both total body fat percent and visceral fat.

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Date Created
2014

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iPhone applications and improvement in weight and health parameters: a randomized controlled trial

Description

Dietary counseling from a registered dietitian has been shown in previous studies to aid in weight loss for those receiving counseling. With the increasing use of smartphone diet/weight loss applications (app), this study sought to investigate if an iPhone diet

Dietary counseling from a registered dietitian has been shown in previous studies to aid in weight loss for those receiving counseling. With the increasing use of smartphone diet/weight loss applications (app), this study sought to investigate if an iPhone diet app providing feedback from a registered dietitian improved weight loss and bio-markers of health. Twenty-four healthy adults who owned iPhones (BMI > 24 kg/m2) completed this trial. Participants were randomly assigned to one of three app groups: the MyDietitian app with daily feedback from a registered dietitian (n=7), the MyDietitian app without feedback (n=7), and the MyPlate feedback control app (n=10). Participants used their respective diet apps daily for 8-weeks while their weight loss, adherence to self-monitoring, blood bio-markers of health, and physical activity were monitored. All of the groups had a significant reduction in waist and hip circumference (p<0.001), a reduction in A1c (p=0.002), an increase in HDL cholesterol levels (p=0.012), and a reduction in calories consumed (p=0.022) over the duration of the trial. Adherence to diet monitoring via the apps did not differ between groups during the study. Body weight did not change during the study for any groups. However, when the participants were divided into low (<50% of days) or high adherence (>50% of days) groups, irrespective of study group, the high adherence group had a significant reduction in weight when compared to the low adherence group (p=0.046). These data suggest that diet apps may be useful tools for self-monitoring and even weight loss, but that the value appears to be the self-monitoring process and not the app specifically.

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Date Created
2014

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IISS a framework to influence individuals through social signals on a social network

Description

Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with

Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.

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Date Created
2014

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Making thin data thick: user behavior analysis with minimum information

Description

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.

This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick.

In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?

In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.

The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.

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Agent

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Date Created
2015