More than a third of all adults in the United States are considered obese. Due to the high costs of health care for obese adults and children, obesity has become a national health crisis. Many government programs have been developed to curtail obesity in adults and children. Unfortunately, there has only been limited success. Past research has shown that obesity has been linked to stress and eating while stressed. Emotional eating occurs when individuals respond to certain emotions, such as stress, by eating to cope with the emotion. Research has shown a correlation between nursing and disordered eating. Given the stressful working environment of mental health workers, the purpose of this study was to explore the experiences of mental health workers who self-identify as emotional eaters. Selye's stress response theory and Heatherton and Baumeister's affect regulation model provided the theoretical framework for this study. Participants included 12 purposefully selected individuals from a specific mental health agency who responded to semi-structured interview questions. Data were analyzed for themes and patterns. The major themes included stress related to mental health work, food patterns altered due to stress, and ways to manage emotional eating. Future research should include a larger sample size across different geographical regions and agencies and the inclusion of individuals who do not self-identify as emotional eaters. With greater knowledge and understanding on the reasons people choose to eat when stressed, individuals and employers may be able to gain insight and make changes that would allow them to manage stress at work without food.

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There is often an assumption that there is a right and a wrong way for consumers to behave. For example, with regard to eating, people should make food choices based on maximizing vitamins and minerals and not consuming more calories than one expends in a day. Likewise, it is assumed that buying products to conspicuously signal a message to another is wasteful and maladaptive. The research in this thesis challenges these assumptions and argues that these behaviours can be both adaptive and maladaptive depending on one’s environmental conditions. In this thesis, I describe three experiments that examine how perception of environmental harshness affects appetite for different types of foods. The data shows that food desirability in adulthood varies depending on early childhood socio-economic status, the type of environmental stressor (harsh social, harsh economic and harsh physical safety) and the intensity of the stressors within each of these environments. It was also found that different types of environmental harshness differentially affects food desire based on energy density and food category type. In addition to the experiments on harshness and food desirability, I have examined how environmental harshness affects desire for products that are used to conspicuously signal information to others. For example, under conditions of environmental stress, products may be used to advertise that a male possesses financial or physical power which is desirable to a potential mate. Likewise, a women may buy products to display she possess financial power or she may purchase products that augment her beauty and sexual attractiveness. These studies reveal that product desire is also affected by different types of environmental harshness and the intensity of the stress generated by these environmental conditions. Through the research described in this thesis, we gain a more comprehensive understanding of the proximate variables that influence two subsets of consumer behaviour, namely food desire and product signalling, and how these behaviours may have been selected for due to their adaptive value.

This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. This research proposes a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects in the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments, with iLog Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. SaYoPillow has 96% accuracy which is close to other existing research works. This research can not only help in keeping an individual self-aware by providing immediate feedback to change the lifestyle of the person in order to lead a healthier life, but can also play a significant role in the state-of-the-art by allowing computing on the edge devices.