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- All Subjects: Behavioral Sciences
- Genre: Masters Thesis
- Creators: Cooke, Nancy J.
- Creators: Johnston, Carol
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
This feasibility study explored the use of an evolutionary mismatch narrative in nutritional education intervention aiming to reduce ultra-processed foods in the diets of veterans with type 2 diabetes and improve diabetic outcomes. Ultra-processed foods are foods that are primarily manufactured through industrial processes. These foods are high in calories but low in nutritional content. Diets high in these foods have been linked to increased health risks. One of the major health risks is type 2 diabetes. Type 2 diabetes is a chronic disease that is developed when cells become unable to properly utilize insulin. Over time this may lead to additional health conditions such as nerve damage, cardiovascular disease, and renal disease. Evolutionary mismatch narrative nutritional intervention offers a different approach to nutritional education to help reduce ultra-processed foods in diets. This study was a randomized controlled feasibility study at the Phoenix VA. Eleven participants were enrolled and randomly selected to be given either an evolutionary mismatch narrative education intervention or general nutritional education about ultra-processed foods. 24-hour diet recalls and blood chemistry were collected and analyzed. Blood chemistry provided diabetes related measurements which included glucose, HbA1c, insulin, HOMA-IR, and C-reactive protein. Statistically significant findings in this study included percentage of ultra-processed foods decreasing for both control and experimental groups from week 0 to week 4 (p=0.014), and C-reactive protein levels between the control and experimental groups (p=0.042). However, baseline C-reactive protein concentrations were lower in the experimental group such that normalizing for group differences at baseline revealed no significant difference in C-reactive protein change between interventions (p = 1.000). There were no other statistically significant values regarding diabetes related measurements. The results from this study suggest that nutritional education in general may help decrease ultra-processed food consumption.
exponential rate, expected to alter the very foundations of our transportation system within the next 10-25 years. A crucial interaction has been born out this new technology: Human and automated drivers operating within the same environment. Despite the well- known dangers of automobiles and driving, autonomous vehicles and their consequences on driving environments are not well understood by the population who will soon be interacting with them every day. Will an improvement in the understanding of autonomous vehicles have an effect on how humans behave when driving around them? And furthermore, will this improvement in the understanding of autonomous vehicles lead to higher levels of trust in them? This study addressed these questions by conducting a survey to measure participant’s driving behavior and trust when in the presence of autonomous vehicles. Participants were given several pre-tests to measure existing knowledge and trust of autonomous vehicles, as well as to see their driving behavior when in close proximity to autonomous vehicles. Then participants were presented with an educational intervention, detailing how autonomous vehicles work, including their decision processes. After examining the intervention, participants were asked to repeat post-tests identical to the ones administered before the intervention. Though a significant difference in self-reported driving behavior was measure between the pre-test and post- test, there was no significant relation found between improvement in scores on the education intervention knowledge check and driving behavior. There was also no significant relation found between improvement in scores on the education intervention knowledge check and the change in trust scores. These findings can be used to inform autonomous vehicle and infrastructure design as well as future studies of the effects of autonomous vehicles on human drivers in experimental settings.