Weight Loss Apps : Behavioral and Psychological Factors Related to Usage and Success

Mobile weight loss applications (‘apps’) such as MyFitnessPal® and Lose It!® have millions of downloads and allow users to track their intake on the go by accessing a massive digital nutrition database, and while the apps have been successful tools for participants in structured weight loss initiatives, little research has explored their efficacy for stand-alone users. The aim of this study was to examine the role of user adherence behavior, portion estimation and consumption norms, and the potential for the app to act as a behavior change tool. An online survey was administered to individuals 18 years or older who have used either MyFitnessPal or Loselt! in order to assess frequency of use, completeness of food records, portion estimation ability, portion consumption norms, and qualitative feedback on factors that impact user experience. Data was gathered using Qualtrics Survey Software and analyzed in IBM SPSS Statistics 22.0 using correlations, t-tests, ANOVA, ANCOYA, and linear regressions. Qualitative data was analyzed through coding and emergence of themes. Fully adherent groups lost significantly more weight than less adherent groups when controlling for duration of usage, and overall, adherence and duration predicted 40% of Average Total Completeness (p\u3c0.01). Participants displayed poor estimation skills, overestimating portion size by an average of 77.54%. Portion norms were not significantly related to weight loss but were positively related to Portion Estimation Error (p\u3c0.01). Qualitative analysis revealed four major themes that influence and explain user experience: App Features, App Qualities, Social Components, and the App as a Behavior Change Tool. Overall, these findings indicate that apps have the potential to be highly effective methods of behavior modification for those looking to lose weight, and strict adherence improves weight loss. Findings also suggest that there is a need for portion education and estimation assistance for users

Privacy-Aware Data Analysis: Recent Developments for Statistics and Machine Learning

Due to technological development, personal data has become more available to collect, store and analyze. Companies can collect detailed browsing behavior data, health-related data from smartphones and smartwatches, voice and movement recordings from smart home devices. Analysis of such data can bring numerous advantages to society and further development of science and technology. However, given an often sensitive nature of the collected data, people have become increasingly concerned about the data they share and how they interact with new technology. These concerns have motivated companies and public institutions to provide services and products with privacy guarantees. Therefore, many institutions and research communities have adopted the notion of differential privacy to address privacy concerns which has emerged as a powerful technique for enabling data analysis while preventing information leakage about individuals. In simple words, differential privacy allows us to use and analyze sensitive data while maintaining privacy guarantees for every individual data point. As a result, numerous algorithmic private tools have been developed for various applications. However, multiple open questions and research areas remain to be explored around differential privacy in machine learning, statistics, and data analysis, which the existing literature has not covered. In Chapter 1, we provide a brief discussion of the problems and the main contributions that are presented in this thesis. Additionally, we briefly recap the notion of differential privacy with some useful results and algorithms. In Chapter 2, we study the problem of differentially private change-point detection for unknown distributions. The change-point detection problem seeks to identify distributional changes in streams of data. Non-private tools for change-point detection have been widely applied in several settings. However, in certain applications, such as identifying disease outbreaks based on hospital records or IoT devices detecting home activity, the collected data is highly sensitive, which motivates the study of privacy-preserving tools. Much of the prior work on change-point detection---including the only private algorithms for this problem---requires complete knowledge of the pre-change and post-change distributions. However, this assumption is not realistic for many practical applications of interest. In this chapter, we present differentially private algorithms for solving the change-point problem when the data distributions are unknown to the analyst. Additionally, we study the case when data may be sampled from distributions that change smoothly over time rather than fixed pre-change and post-change distributions. Furthermore, our algorithms can be applied to detect changes in linear trends of such data streams. Finally, we also provide a computational study to empirically validate the performance of our algorithms. In Chapter 3, we study the problem of learning from imbalanced datasets, in which the classes are not equally represented, through the lens of differential privacy. A widely used method to address imbalanced data is resampling from the minority class instances. However, when confidential or sensitive attributes are present, data replication can lead to privacy leakage, disproportionally affecting the minority class. This challenge motivates the study of privacy-preserving pre-processing techniques for imbalanced learning. In this work, we present a differentially private synthetic minority oversampling technique (DP-SMOTE) which is based on a widely used non-private oversampling method known as SMOTE. Our algorithm generates differentially private synthetic data from the minority class. We demonstrate the impact of our pre-processing technique on the performance and privacy leakage of various classification methods in a detailed computational study. In Chapter 4, we focus on the analysis of sensitive data that is generated from online internet activity. Accurately analyzing and modeling online browsing behavior play a key role in understanding users and technology interactions. Towards this goal, in this chapter, we present an up-to-date measurement study of online browsing behavior. We study both self-reported and observational browsing data and analyze what underlying features can be learned from statistical analysis of this potentially sensitive data. For this, we empirically address the following questions: (1) Do structural patterns of browsing differ across demographic groups and types of web use?, (2) Do people have correct perceptions of their behavior online?, and (3) Do people change their browsing behavior if they are aware of being observed? In response to these questions, we found little difference across most demographic groups and website categories, suggesting that these features cannot be implied solely from clickstream data. We find that users significantly overestimate the time they spend online but have relatively accurate perceptions of how they spend their time online. We find no significant changes in behavior throughout the study, which may indicate that observation had no effect on behavior or that users were consciously aware of being observed throughout the study

Reconciling long-term cultural diversity and short-term collective social behavior

An outstanding open problem is whether collective social phenomena occurring over short timescales can systematically reduce cultural heterogeneity in the long run, and whether offline and online human interactions contribute differently to the process. Theoretical models suggest that short-term collective behavior and long-term cultural diversity are mutually excluding, since they require very different levels of social influence. The latter jointly depends on two factors: the topology of the underlying social network and the overlap between individuals in multidimensional cultural space. However, while the empirical properties of social networks are well understood, little is known about the large-scale organization of real societies in cultural space, so that random input specifications are necessarily used in models. Here we use a large dataset to perform a high-dimensional analysis of the scientific beliefs of thousands of Europeans. We find that inter-opinion correlations determine a nontrivial ultrametric hierarchy of individuals in cultural space, a result unaccessible to one-dimensional analyses and in striking contrast with random assumptions. When empirical data are used as inputs in models, we find that ultrametricity has strong and counterintuitive effects, especially in the extreme case of long-range online-like interactions bypassing social ties. On short time-scales, it strongly facilitates a symmetry-breaking phase transition triggering coordinated social behavior. On long time-scales, it severely suppresses cultural convergence by restricting it within disjoint groups. We therefore find that, remarkably, the empirical distribution of individuals in cultural space appears to optimize the coexistence of short-term collective behavior and long-term cultural diversity, which can be realized simultaneously for the same moderate level of mutual influence

ANCOVA Study of Psychotherapy Treatment of Internet Pornography Addiction in Heterosexual Men

Internet pornography has grown to become a problem that exists within the United States and across the globe. For those who suffer from this problematic behavior experience individual and familial problems as well as cause damage to the psyche, professionally and sexually. Those who suffer from addiction do not possess the ability to be able to stop the behavior on their own. Treatment is needed to help internet pornography users and addicts to recover while minimizing relapse from its consumption. This research proposal is a randomized, controlled, clinical ANCOVA study that will determine the effectiveness of Cognitive Behavioral Therapy and Acceptance Commitment Therapy for reducing Internet pornography viewing and behaviors among male heterosexual Internet pornography addicts. Treatment will take place for eight week period and consist of 75 male participants who are randomly assigned to either the CBT, ACT or wait-list control groups. Participants will be assessed at pretest (week 1), posttest (week 9) and three month follow-up (week 13) on the SIS/SES SAST-R, CPUI and AAQ-II measures. The study will define Internet pornography addictive behaviors, negative consequences and explain internal, external, construct and statistical construct validity with regard to the study’s design. Success of the study would provide effective treatment protocol and lessen the destruction of Internet pornography addiction on addicts who seek treatment as well as provide a format for therapists to follow as this is a new type of addiction, which has yet to be universally defined

The Effects of an Online Sleep Hygiene Intervention on Students\u27 Sleep Quality

Students in college or in their first year of medical school undergo increased educational and social pressure. To cope, students may sacrifice sleep to meet demands. Poor sleep affects learning, performance, and health. Studies have been successful at improving sleep quality through the use of in-person recruitment or cognitive-behavioral therapy delivered over the internet (Trockel, Manber, Chang, Thurston, & Tailor, 2011). The purpose of the current study was to investigate whether an online sleep hygiene intervention could improve sleep quality. One hundred thirty-eight students from one undergraduate institution in Southeast Virginia completed this study. Students were divided into groups; one of them received information regarding good and bad sleep hygiene and the other received information about dreaming. Both groups filled out the Pittsburgh Sleep Quality Index (PSQI), The Sleep Hygiene Index (SHI), the Epworth Sleepiness Scale (ESS), the Sleep Hygiene Pretests/Post-test and the Positive and Negative Affect Schedule (PANAS). Two weeks later, participants filled out the same measures they filled out at the beginning of the study. A mixed analysis of variance was used to evaluate the two different groups. Results indicated significant differences between the two groups in sleep hygiene knowledge. Individuals who received information on this topic had higher levels of knowledge from baseline to post-intervention. No other significant findings were detected. On average, this sample of college students had similar total hours of sleep as other researchers have identified (Lund, Reider, Whiting, & Prichard, 2010). One hundred seven students (77%) were considered poor sleepers by the PSQI Global scores. The SHI also identified poor sleep hygiene practices within this sample. Lastly, participants had relatively average positive mood and below average negative mood as measured by the PANAS during baseline and post-intervention. The brief online sleep hygiene intervention did not improve students\u27 sleep quality. It is believed the intervention did not succeed because students\u27 motivation to alter their sleep practices was not assessed and this may have influenced the likelihood of behavior change. Future research should focus on participants\u27 needs and motivation and use this information to tailor the intervention

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Sustainable consumption: towards action and impact. : International scientific conference November 6th-8th 2011, Hamburg - European Green Capital 2011, Germany: abstract volume

This volume contains the abstracts of all oral and poster presentations of the international scientific conference „Sustainable Consumption – Towards Action and Impact“ held in Hamburg (Germany) on November 6th-8th 2011. This unique conference aims to promote a comprehensive academic discourse on issues concerning sustainable consumption and brings together scholars from a wide range of academic disciplines. In modern societies, private consumption is a multifaceted and ambivalent phenomenon: it is a ubiquitous social practice and an economic driving force, yet at the same time, its consequences are in conflict with important social and environmental sustainability goals. Finding paths towards “sustainable consumption” has therefore become a major political issue. In order to properly understand the challenge of “sustainable consumption”, identify unsustainable patterns of consumption and bring forward the necessary innovations, a collaborative effort of researchers from different disciplines is needed