Patterns of Oral Microbiota Diversity in Adults and Children: A Crowdsourced Population Study.

Oral microbiome dysbiosis has been associated with various local and systemic human diseases such as dental caries, periodontal disease, obesity, and cardiovascular disease. Bacterial composition may be affected by age, oral health, diet, and geography, although information about the natural variation found in the general public is still lacking. In this study, citizen-scientists used a crowdsourcing model to obtain oral bacterial composition data from guests at the Denver Museum of Nature & Science to determine if previously suspected oral microbiome associations with an individual's demographics, lifestyle, and/or genetics are robust and generalizable enough to be detected within a general population. Consistent with past research, we found bacterial composition to be more diverse in youth microbiomes when compared to adults. Adult oral microbiomes were predominantly impacted by oral health habits, while youth microbiomes were impacted by biological sex and weight status. The oral pathogen Treponema was detected more commonly in adults without recent dentist visits and in obese youth. Additionally, oral microbiomes from participants of the same family were more similar to each other than to oral microbiomes from non-related individuals. These results suggest that previously reported oral microbiome associations are observable in a human population containing the natural variation commonly found in the general public. Furthermore, these results support the use of crowdsourced data as a valid methodology to obtain community-based microbiome data

Male Weight Control: Crowdsourcing and an Intervention to Discover More

Men and women have similar rates of obesity but the combined prevalence of overweight and obesity is higher among men. Men who are overweight are a high-risk group for many obesity-related chronic diseases, as they are more likely to carry excess weight in the abdomen, which is generally more harmful than weight stored in the lower body. Men are also less likely than women to perceive themselves as overweight, and thus are less likely to initiate weight loss through organized weight loss programs. On average, less than 27% of weight loss trial participants have been men. Internet-based research is a low-cost, efficient way to produce novel hypotheses related to weight loss that may have previously escaped weight loss professionals. Additionally, incentives are an effective tool to motivate behavior change, and there is ample evidence to support the use of incentives to encourage many health-promoting behaviors, such as weight loss. The purpose our initial study was to facilitate intervention development by using crowdsourcing to detect unexpected beliefs and unpredicted barriers to male weight loss. The aim of our main study was to evaluate the impact of financial incentives to facilitate weight loss in men, delivered as part of a weight loss intervention. Two separate studies were conducted. In the first project, participants were recruited to a crowdsourcing survey website which was used to generate hypotheses for behaviors related to overweight and obesity in men. Participants provided 21,846 responses to 193 questions. While several common themes seen in prior research were revealed such as previous health diagnoses and physical activity participation, other potential weight determinants such as dietary habits, sexual behaviors and self-perception were reported. Crowdsourcing in this context provides a mechanism to further investigate perceptions of weight and weight loss interventions in the male population that have not previously been documented. These insights will help guide future intervention design. For the main project, a randomized trial compared the Gutbusters weight loss program (based on the REFIT program) alone with Gutbusters with escalating incentives for successful weight loss. The six-month intervention was conducted online with weekly in-person weight collections for the first 12 weeks. Gutbusters encouraged participants to make six 100-calorie changes to their daily diet, utilizing a variety of online lessons targeting specific eating behaviors. Measures included demographic information, height, weight, waist circumference, and body fat percentage. Participants (N=102, 47. 0± 12. 3 yrs old, 32. 5 kg/m2, 80. 4% with at least two years of college) were randomized in a 1:1 ratio to Gutbusters or Gutbusters+Incentive. Significantly more Gutbusters+Incentive participants lost at least 5% of their baseline weight compared to the Gutbusters group at both 12 and 24 weeks. Similar to the aforementioned REFIT program, Gutbusters participants were able to achieve clinically significant weight loss. The Gutbusters+Incentive achieved greater rates of weight loss than the Gutbusters alone group, further supporting the value of incentives in promoting health behaviors

Systems Biology of the human microbiome

© The Author(s), 2017. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Current Opinion in Biotechnology 51 (2018): 146-153, doi:10.1016/j.copbio.2018.01.018.Recent research has shown that the microbiome—a collection of microorganisms, including bacteria, fungi, and viruses, living on and in a host—are of extraordinary importance in human health, even from conception and development in the uterus. Therefore, to further our ability to diagnose disease, to predict treatment outcomes, and to identify novel therapeutics, it is essential to include microbiome and microbial metabolic biomarkers in Systems Biology investigations. In clinical studies or, more precisely, Systems Medicine approaches, we can use the diversity and individual characteristics of the personal microbiome to enhance our resolution for patient stratification. In this review, we explore several Systems Medicine approaches, including Microbiome Wide Association Studies to understand the role of the human microbiome in health and disease, with a focus on ‘preventive medicine’ or P4 (i.e., personalized, predictive, preventive, participatory) medicine.BPB is funded by the Arnold and Mabel Beckman Foundation (Arnold O. Beckman Postdoctoral Fellow)2019-02-1

The Rise of Citizen Science in Health and Biomedical Research

Citizen science models of public participation in scientific research represent a growing area of opportunity for health and biomedical research, as well as new impetus for more collaborative forms of engagement in large-scale research. However, this also surfaces a variety of ethical issues that both fall outside of and build upon the standard human subjects concerns in bioethics. This article provides background on citizen science, examples of current projects in the field, and discussion of established and emerging ethical issues for citizen science in health and biomedical research

Examining the Effects of Personalized Explanations in a Multi-list Food Recommender System

In the past decade, food recipe websites have become a popular approach to find a recipe. Due to the vast amount of options, food recommender systems have been devel- oped and used to suggest appetizing recipes. However, recommending appealing meals does not necessarily imply that they are healthy. Recent studies on recommender sys- tems have demonstrated a growing interest in altering the interface, where the usage of multi-list interfaces with explanations has been explored earlier in an unsuccessful at- tempt to encourage healthier food choices. Building upon other research that highlights the ability of personalized explanations to provide a better understanding of presented recommendations, this thesis explores whether a multi-list interface with personalized explanations, which takes into account user preferences, health, and nutritional aspects, can affect users’ evaluation and perception of a food recommender system, as well as steer them towards healthier choices. A food recommender system was develop, with which single- and multi-lists, as well as non-personalized and personalized explana- tions, were compared in an online experiment (N = 163) in which participants were requested to choose recipes they liked and to answer questionnaires. The analysis re- vealed that personalized explanations in a multi-list interface were not able to increase choice satisfaction, choice difficulty, understanding or support healthier choices. Sur- prisingly, users selected healthier recipes if non-personalized rather than personalized explanations were presented alongside them. In addition, users perceived multi-lists to be more diverse and found single-list to be more satisfying.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Understanding Fitness App Users’ Loyalty and Word of Mouth through Gameful Experience and Flow Theory

In this study, we examine the effect that a gameful experience and personalization have on the flow experience of fitness app users. We also test the association between flow experience and satisfaction in using fitness apps and whether satisfied users remain loyal and spread word of mouth regarding fitness apps. We use the belief-attitude- behavior framework as a theoretical lens and flow theory to explore the proposed relationships. Four hundred thirty- one fitness app users from India participated in the study. The results indicate that gameful experience and personalization lead to flow experience. We found a positive association between flow and satisfaction wherein satisfied fitness app users spread word of mouth and remained loyal to using fitness apps. Our findings will help fitness app developers identify factors to retain fitness app users and attract new ones

Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review

Reproduced by permission of Oxford University Press https://academic.oup.com Copyright © 2019 American Society for NutritionSmartphone applications are increasingly being used to support nutrition improvement in community settings. However, there is a scarcity of practical literature to support researchers and practitioners in choosing or developing health applications. This work maps the features, key content, theoretical approaches, and methods of consumer testing of applications intended for nutrition improvement in community settings. A systematic, scoping review methodology was used to map published, peer-reviewed literature reporting on applications with a specific nutrition-improvement focus intended for use in the community setting. After screening, articles were grouped into 4 categories: dietary self-monitoring trials, nutrition improvement trials, application description articles, and qualitative application development studies. For mapping, studies were also grouped into categories based on the target population and aim of the application or program. Of the 4818 titles identified from the database search, 64 articles were included. The broad categories of features found to be included in applications generally corresponded to different behavior change support strategies common to many classic behavioral change models. Key content of applications generally focused on food composition, with tailored feedback most commonly used to deliver educational content. Consumer testing before application deployment was reported in just over half of the studies. Collaboration between practitioners and application developers promotes an appropriate balance of evidence-based content and functionality. This work provides a unique resource for program development teams and practitioners seeking to use an application for nutrition improvement in community settings

Unsupervised linking of scientific articles to food systems taxonomies

In this thesis, a novel method for linking scientific articles to taxonomy terms in the domain of food systems research is presented. With food systems being in the center of 12 of the 17 United Nations Sustainable Development goals, there has been an ever-growing amount of scientific articles in this field. These articles are vital in understanding the complex nature of food systems and their inter-dependencies. However, finding relevant literature in this field is difficult for decision makers given the interdisciplinary nature of the field and that annotation and expert feedback is expensive. In the thesis, BERT-based models (SBERT, SPECTER and SciBERT) are adapted to the food systems area and fine-tuned for tasks such as text classification and text similarity, which represents a solution to the problem of finding relevant articles in the food systems domain. The proposed search system uses several taxonomies and data augmentation to achieve the results, which are visualized in a created website. Linking food systems research articles to taxonomy terms shows good accuracy, with models finetuned on domain data achieving better performance on classification task. The best fine-tuning strategy for SPECTER and SciBERT is the combination of domain adaptation and classification. Fine-tuning for text similarity for SBERT improves SBERT performance only slightly. The proposed method can be used in other domains than food systems