10,737 research outputs found

    Sociodemographic, nutritional and health status factors associated with adherence to Mediterranean diet in an agricultural Moroccan adult's population

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    Background. Numerous studies have demonstrated beneficial effects of adherence to the Mediterranean diet (MD) on many chronic diseases, including chronic kidney disease (CKD). Objective. The aim of this study was to assess the adherence of a rural population to the Mediterranean diet, to identify the sociodemographic and lifestyle determinants and to analyze the association between adherence to MD and CKD. Material and Methods. In a cross-sectional study, data on sociodemographic, lifestyle factors, clinical, biochemical parameters and diet were collected on a sample of 154 subjects. Adherence to MD was assessed according to a simplified MD score based on the daily frequency of intake of eight food groups (vegetables, legumes, fruits, cereal or potatoes, fish, red meat, dairy products and MUFA/SFA), using the sex specific sample medians as cut-offs. A value of 0 or 1 was assigned to consumption of each component according to its presumed detrimental or beneficial effect on health. Results. According to the simplified MD score, the study data show that high adherence (44.2%) to MD was characterized by intakes high in vegetables, fruits, fish, cereals, olive oil, and low in meat and moderate in dairy. Furthermore, several factors such as age, marital status, education level, and hypertension status were associated with the adherence to MD in the study population. The majority of subjects with CKD have poor adherence to the MD compared to non-CKD with a statistically insignificant difference. Conclusions. In Morocco, maintaining the traditional MD pattern play crucial role for public health. More research is needed in this area to precisely measure this association

    Living with erythropoietic protoporphyria:Bridging the gap between research and clinical practice

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    Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being

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    The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when and, to some extent, what an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited. My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies. The contributions of my dissertation are four-fold. First, I develop a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about one’s eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Physical activity, weight gain, and risk of mortality in adults

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    Why: As low physical activity levels associate with ill health and mortality, continuous monitoring of physical activity levels is needed to inform policy. Identifying aetiology causes for the obesity epidemic is important to prevent population weight gain. However, there are still uncertainties on how physical activity and weight at population level associate over time, and how physical activity and sedentary time collectively influence premature death. Aims and methods: To describe prevalence of device-measured physical activity in adults (40-84 years) in the Seventh Tromsø Study survey 2015-16 (Tromsø7) (Paper I). To examine accelerometry-criterion validity for two physical activity questionnaires (PAQ)s and one sedentary time questionnaire (Paper II). To examine whether occupational (Paper III) and leisure time physical activity (Paper IV) changes from one examination to the next are associated with subsequent body mass index (BMI) changes from the second to a third examination, across Tromsø Study surveys from 1974 to 2016 in prospective cohort designs. To examine associations between device-measured physical activity, sedentary time, and mortality in a one-step individual participant data meta-analysis of four prospective cohort studies (Tromsø7, The Healthy Ageing Initiative 2012-2019, The Norwegian National Physical Activity Survey 2008-09, The National Health and Nutrition Examination Survey 2003-06) (Paper V). Findings and conclusions: About 70% of all adults met current lower-limit physical activity guidelines of 150 minutes per week of moderate and vigorous physical activity (Paper I). Processing PAQs in crude groups may attenuate biases associated with self-reported physical activity as it provided clearer patterns of higher device-measured physical activity by higher grouped ranking, while continuous scales of the PAQs showed small correlation magnitudes with device-measured physical activity (Paper II). Population levels of occupational (Paper III) and leisure time (Paper IV) physical activity appear insufficient to prevent weight gain but rather it appears the association is reverse, population weight gain leads to physical activity declines (Paper IV). Physical activity, at any intensity, associates with a substantial lower mortality risk and meeting current lower-limit guidelines ameliorates the higher mortality risk associated with high sedentary time (Paper V). Importance: This thesis highlights the public health gain of increasing population levels of physical activity, and of preventing population weight gain to avoid physical activity declines

    Towards Aggregate Programming in pure Kotlin through compiler-level metaprogramming

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    The last few decades have seen significant technological advancements in computing, the internet, and mobile technology, leading to the growth of the Internet of Things (IoT). This has resulted in a network of physical devices embedded with sensors, software, and connectivity, which can collect and share data. However, this growth has also brought new challenges, such as the need for complex software engineering to take advantage of the computational infrastructure available while considering unpredictability and communication heterogeneity. This thesis explores the aggregate programming, which is a paradigm based on field calculus, and it allows for the easy manipulation of data across devices, making it possible to perform operation on the data of distributed systems, in a simple and efficient manner. The paradigm has been implemented in various programming languages and platforms, such as Protelis, Scafi and FCPP. This thesis proposes a new implementation of the aggregate programming paradigm, called Collektive. The aggregate programming paradigm requires the communication of the devices to be coordinated through the alignment, which keeps track of the computational state of each device. The work done in this thesis explores different Kotlin metaprogramming techniques in order to solve this problem, illustrating the final solution achieved through the implementation of a Kotlin compiler plugin, which is totally transparent and portable. The project provides the user a minimal DSL, which is compatible with multiple platforms, such as JVM, JavaScript and Kotlin Native. This is possible because of the features offered by Kotlin Multiplatform, which is used for the implementation of the DSL. Moreover, this thesis addresses the validation process carried out to test the correct behavior of the system, which guarantees that Collektive can be considered at the same level of the existing aggregate programming implementations

    Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

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    In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

    Modelling of sleep behaviors of patients with mood disorders

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    Sleep is an essential function of the human body. It has a restorative effect on both physical and mental health functions. Short and long-term consequences of sleep disruption include changes to stress response, anxiety, and depression, as well as deficiencies in memory, cognition, and performance. Several methods have been developed to assess sleep. While polysomnography is considered the golden standard of sleep assessment, researchers have focused on alternate ways of tracking sleep using non-intrusive and less costly methods such as actigraphy. Some studies suggested that screen activity from smartphones can be an indicator of the sleep and wake states of an individual as smartphone usage increased drastically in the last decade. Mood disorders are mental health conditions that disrupt the emotional state of individuals. Sudden and extreme mood changes interfere with the patients’ daily rhythm in many ways, including their sleep behavior. Timely diagnosis of the severity of mood disorders plays a critical role in their treatment process. Previous research shows strong links between decreased sleep quality in patients suffering from mood disorders. This thesis uses the data from a digital phenotyping study, Mobile Monitoring of Mood (MoMo-Mood), to analyze the sleep behaviors of patients with mood disorders using some sleep parameters. In addition, a predictive model is built to investigate the severity of depression using the information tracked via actigraph and bed sensor. Lastly, the perceived sleep quality from questionnaires is compared with the data tracked by these sensors to evaluate the differences in the three different groups of patients: bipolar disorder, borderline personality disorder, and major depressive disorder

    Post-Growth Geographies: Spatial Relations of Diverse and Alternative Economies

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    Post-Growth Geographies examines the spatial relations of diverse and alternative economies between growth-oriented institutions and multiple socio-ecological crises. The book brings together conceptual and empirical contributions from geography and its neighbouring disciplines and offers different perspectives on the possibilities, demands and critiques of post-growth transformation. Through case studies and interviews, the contributions combine voices from activism, civil society, planning and politics with current theoretical debates on socio-ecological transformation
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