Faculty Publications
Permanent URI for this collectionhttps://hdl.handle.net/10217/234698
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Item Open Access Walking in an urban environment and a virtual reality replica: comparisons of physical activity duration and intensity(Colorado State University. Libraries, 2025) Spitzer, Amanda N., author; Ramey, Matea R., author; Yu, Yiqing ‘"Skylar", author; Oselinsky, Katrina M., author; McMahon, Katie, author; Kelley, Brendan, author; Rojas-Rueda, David, author; Dean, Daniel, author; LoTemplio, Sara B., author; Ortega, Francisco R., author; Graham, Dan J., authorIncreasing walking behavior is desirable from public health, environmental, and urban planning perspectives. Virtual reality (VR) has the potential to improve the design of walkable environments. However, the current research is necessary to determine whether walking decisions in VR mirror those in the real world (RW). Participants completed two study sessions: walking in a VR simulation of a historic district (VR session) and walking in the real-life district (RW session). During each session, participants were asked to complete three tasks (e.g., find a restaurant) and stop walking following task completion. Heart rate (HR) data contained a high degree of missingness, so no HR analyses are reported. Nevertheless, walking intensity is addressed through exploratory negative binomial and Poisson regression models predicting duration in light and moderate-to-vigorous physical activity using accelerometry. These models indicated no relationship between physical activity intensity in VR and the RW. Additionally, a paired t-test and mixed effects model indicated that walking duration was significantly longer in VR than the RW. However, exploratory analyses suggested order effects: those who walked first in the RW walked similar durations in both settings, but those that walked first in VR walked for about five minutes longer in VR (17.8) than in the RW (13.0). In conclusion, walking intensity in VR may not mimic walking intensity in the RW, but depending on order of conditions, walking decisions in VR may resemble RW decisions. Possible explanations for the observed order effects include history effects, VR navigation and skill transfer, and participant motivation.Item Open Access Using a neural network analysis to assess stressors in the farming community(Colorado State University. Libraries, 2020-04-16) Beseler, Cheryl, author; Stallones, Lorann, author; MDPI, publisherIn the 1980s and 1990s, with decreasing numbers of full-time farmers and adverse economic conditions, chronic stress was common in farmers, and remains so today. A neural network was implemented to conduct an in-depth analysis of stress risk factors. Two Colorado farm samples (1992-1997) were combined (n = 1501) and divided into training and test samples. The outcome, stress, was measured using seven stress-related items from the Center for Epidemiologic Studies-Depression Scale. The initial model contained 32 predictors. Mean squared error and model fit parameters were used to identify the best fitting model in the training data. Upon testing for reproducibility, the test data mirrored the training data results with 20 predictors. The results highlight the importance of health, debt, and pesticide-related illness in increasing the risk of stress. Farmers whose primary occupation was farming had lower stress levels than those who worked off the farm. Neural networks reflect how the brain processes signals from its environment and algorithms allow the neurons "to learn". This approach handled correlated data and gave greater insight into stress than previous approaches. It revealed how important providing health care access and reducing farm injuries are to reducing farm stress.Item Open Access Risk factors for brain health in agricultural work: a systematic review(Colorado State University. Libraries, 2022-03-13) Sturm, Emily Terese, author; Castro, Colton, author; Mendez Colmenares, Andrea, author; Duffy, John, author; Burzynzka, Agnieszka Z., author; Stallones, Lorann, author; Thomas, Michael L., author; MDPI, publisherCertain exposures related to agricultural work have been associated with neurological disorders. To date, few studies have included brain health measurements to link specific risk factors with possible neural mechanisms. Moreover, a synthesis of agricultural risk factors associated with poorer brain health outcomes is missing. In this systematic review, we identified 106 articles using keywords related to agriculture, occupational exposure, and the brain. We identified seven major risk factors: non-specific factors that are associated with agricultural work itself, toluene, pesticides, heavy metal or dust exposure, work with farm animals, and nicotine exposure from plants. Of these, pesticides are the most highly studied. The majority of qualifying studies were epidemiological studies. Nigral striatal regions were the most well studied brain area impacted. Of the three human neuroimaging studies we found, two focused on functional networks and the third focused on gray matter. We identified two major directions for future studies that will help inform preventative strategies for brain health in vulnerable agricultural workers: (1) the effects of moderators such as type of work, sex, migrant status, race, and age; and (2) more comprehensive brain imaging studies, both observational and experimental, involving several imaging techniques.