As screen time (ST) constitutes an integral part of the daily lives of young children today, parents, educators, and researchers have started to explore the associations of ST with children’s cognitive, behavioral, and social outcomes. The majority of existing studies have primarily focused on the duration of ST in relation to these outcomes despite the importance of other aspects such as content and type of device in the context of an evolving digital landscape marked by high mobility, ubiquity, and diversity. Addressing this gap, the current study aimed to explore the intricate relations between multiple aspects of ST (i.e., duration and content), executive function (EF) difficulties, and school adjustment in school-aged children, with a particular focus on the mediating role of EF difficulties linking the relations between ST and school adjustment. The current study employed data from the Panel Study on Korean Children, tracking 1,484 South Korean children from third to fourth grade. The duration of ST was measured by the average daily hours spent on smart devices and computers. Parent reports of the levels of engagement in recreational and educational ST and EF difficulties were assessed on Likert scales. School adjustment was reported on by teachers. The results from a half-longitudinal mediation model demonstrated that more frequent engagement in educational ST was related to fewer EF difficulties, which was in turn associated with better school adjustment. The current findings suggest that multiple approaches are needed to effectively guide children’s ST use in their everyday lives and interventions that target EF might be an effective way to promote children's behavioral and social adjustment in school settings.

The relationship between sleep and physical activity is an area of growing scientific interest, particularly in the context of older adults. The importance of examining long sleep duration and its influence on physical activity in this demographic becomes increasingly relevant given rising healthcare costs. This dissertation aims to investigate this intricate relationship via secondary analysis by examining the effects of moderate time-in-bed (TIB) restriction (60 minutes per night)) on various intensities of physical activity (sedentary, light, moderate, vigorous, moderate-vigorous physical activity) in older adults classified as long sleepers and average duration sleepers. It was hypothesized that moderate TIB restriction would result in differential changes in physical activity levels across various intensities, with long sleepers exhibiting increased physical activity and average sleepers displaying decreased activity, potentially influenced by alterations in TST (total sleep time) and SE (sleep efficiency). Utilizing a randomized controlled trial design, this study examined the effect of treatment changes in objectively measures activity (waist actigraphy) and subjects physical activity levels as measured by the Godin Leisure-Time Exercise Questionnaire . Eligible participants were long sleepers (sleeping > 9 hours per night) and average sleepers (sleeping 7-9 hours per night). Both types of sleepers were either randomized to TIB restriction or asked to maintain their average sleep patterns. Mean TIB restriction compared with baseline was 39.5 minutes in average sleepers and 52.9 minutes in long sleepers randomized to TIB restriction . Contrary to the original hypothesis, no significant effect of TIB restriction was observed across all physical activity levels in either long sleepers or average sleepers. However, a notable association was found between increased sleep efficiency (+0.09% [SD = ± 4.64%]) and light physical activity (±31 minutes [SD = ± 104.81, R=0.445, P < 0.007]) in long sleepers undergoing TIB restriction. While this study presents several methodological limitations, including its nature as a secondary analysis and the less-than-intended achievement of TIB restriction, it adds a valuable layer to the existing body of research on sleep and physical activity in older adults. The findings suggest that moderate TIB restriction may not be sufficiently impactful to change behavior in physical activity levels, thus highlighting the need for more nuanced, targeted research in this domain.

This dissertation research project developed as an urgent response to physical inactivity, which has resulted in increased rates of obesity, diabetes, and metabolic disease worldwide. Incorporating enough daily physical activity (PA) is challenging for most people. This research aims to modulate the brain's reward systems to increase motivation for PA and, thus, slow the rapid increase in sedentary lifestyles. Transcranial direct current stimulation (tDCS) involves brain neuromodulation by facilitating or inhibiting spontaneous neural activity. tDCS applied to the dorsolateral prefrontal cortex (DLPFC) increases dopamine release in the striatum, an area of the brain involved in the reward–motivation pathways. I propose that a repeated intervention, consisting of tDCS applied to the DLPFC followed by a short walking exercise stimulus, enhances motivation for PA and daily PA levels in healthy adults. Results showed that using tDCS followed by short-duration walking exercise may enhance daily PA levels in low-physically active participants but may not have similar effects on those with higher levels of daily PA. Moreover, there was a significant effect on increasing intrinsic motivation for PA in males, but there were no sex-related differences in PA. These effects were not observed during a 2-week follow-up period of the study after the intervention was discontinued. Further research is needed to confirm and continue exploring the effects of tDCS on motivation for PA in larger cohorts of sedentary populations. This novel research will lead to a cascade of new evidence-based technological applications that increase PA by employing approaches rooted in biology.

Food insecurity is an economic and social condition involving limited or uncertain access to food. The problem of food insecurity in communities is influenced by economic conditions, food deserts, and barriers to accessing healthy food. Individuals experiencing food insecurity often endure concurrent problems of financial instability, hunger, and poor mental and physical health. Public and non-profit services in the U.S., such as the federally supported Supplemental Nutrition Assistance Program (SNAP) and community food banks, provide food-related assistance to individuals who are at a high risk of experiencing food insecurity. Unfortunately, many individuals who qualify for these services still experience food insecurity due to barriers preventing them from accessing food, which may include inadequate finances, transportation, skills, and information. Effective approaches for removing barriers that prevent individuals from accessing food are needed to mitigate the increased risk of hunger, nutritional deficiencies, and chronic disease among vulnerable populations. This dissertation tested a novel food insecurity intervention using informational nudges to promote food security through the elimination of information barriers to accessing food. The intervention used in this mixed-methods feasibility study consisted of informational nudges in the form of weekly text messages that were sent to food pantry clients experiencing food insecurity. The study aims were to test the efficacy and acceptability of the intervention by examining whether the informational nudges could enhance food pantry utilization, increase SNAP registration, and promote food security. Quantitative study results showed a lower prevalence of food insecurity in the intervention group than the control group. Qualitative findings revealed how the intervention group found the text messages to be helpful and informative. These study findings can enhance future food insecurity interventions aiming to eliminate barriers that prevent individuals who are food insecure from accessing healthy food.

Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.

The purpose of this investigation was to evaluate the influence of tap water safety perceptions on plain water intake (PWI) and hydration status in US Latinx adults. Participants (n=492; age, 28±7 y; 37.4% female) completed an Adapted Survey of Water Issues in Arizona and household watersecurity experience-based scales. A sub-sample (n=55; age, 33±14 y; body mass index, 27.77±6.60 kg·m2) completed dietary recalls on two weekdays and one weekend day via Automated Self-Administered 24-hour Dietary Assessment Tool to determine average PWI and total water intake (TWI). A 24-h urine sample was collected on one recall day and analyzed for urine osmolality (UOsm). Binary logistic regression determined odds ratios (OR) for the odds of perceiving tap water to be unsafe. Hierarchical linear regression was employed with 24-h UOsm and PWI as primary outcomes for the sub-sample. Overall, 51.2% of all participants and 52.7% of the sub-sample mistrust their tap water safety. The odds of mistrusting tap water were significantly greater (P<0.05) for each additional favorable perception of bottled over tap water (OR=1.94, 95% CI=1.50, 2.50), each additional negative home tap water experience (OR=1.32, 95% CI=1.12, 1.56), each additional use of alternatives and/or modifications to home tap water (OR=1.25, 95% CI=1.04, 1.51), and decreased water quality and acceptability (OR=1.21, 95% CI=1.01, 1.45). The odds of mistrusting tap water were significantly lower (P<0.05) for those whose primary source of drinking water is the public supply (municipal) (OR=0.07, 95% CI=0.01, 0.63) and for those with decreased water access (OR=0.56, 95% CI=0.48, 0.66). There were no differences (n=55, P>0.05) in TWI (2,678±1,139 mL), PWI (1,357±971), or 24-h UOsm (460±234 mosm·kg-1). Tap water safety perceptions did not significantly explain variance in PWI or 24-h UOsm (P > 0.05). In conclusion, Latinx mistrust in tap water safety is prevalent. Mistrust appears to be influenced by organoleptic perceptions and to lead to reliance on alternatives to the home drinking water system. Perceptions of tap water safety do not appear to be related to PWI, TWI, or hydration status in Latinx adults.

High fiber diets have been associated with improved cardiometabolic health with specific efforts to lower circulating levels of low-density lipoprotein (LDL cholesterol). Whole grain and grain-based foods are major contributors of dietary fiber in the American diet, of which wheat has been extensively studied. Corn, however, has not been well studied for its cholesterol-lowering properties. Further, the mechanisms by which grains improve cardiometabolic health require further exploration with regard to the human microbiome. The objective of this single-blind randomized controlled, crossover trial was to assess the impact of three different corn flours (whole grain, refined, and bran-enhanced refined flour mixture) on serum LDL cholesterol and the gut microbiota diversity and composition. Twenty-three participants were recruited, between the ages of 18-70 with hypercholesterolemia (Male = 10, Female = 13, LDL >120 mg/dL) who were not taking any cholesterol-lowering medications. Participants consumed each flour mixture for 4 weeks prepared as muffins and pita breads. At the beginning and end of each 4-week period serum for cholesterol assessment, anthropometrics, and stool samples were obtained. Serum cholesterol was assessed using a clinical analyzer. Stool samples were processed, and microbial DNA extracted and sequenced based on the 16S rRNA gene. A generalized linear model demonstrated a significant treatment effect (p=0.016) on LDL cholesterol and explained a majority of the variance (R-squared= 0.89). Post hoc tests revealed bran-enhanced refined flour had a significant effect on cholesterol in comparison to whole grain flour (p=0.001). No statistically significant differences were observed for gut microbial community composition (Jaccard and weighted Unifrac) after corn consumption. However, relative abundance analysis (LEfSE) identified Mycobacterium celatum (p=0.048 FDR=0.975) as a potential marker of post-corn consumption with this microbe being differentially less abundant following bran-enhanced flour treatment. These data suggest that corn flour consumption may be beneficial for individuals with hypercholesterolemia but the role of gut microbiota in this relationship requires further exploration, especially given the small sample size. Further research and analysis of a fully powered cohort is needed to more accurately describe the associations and potential mechanisms of corn-derived dietary fiber on circulating LDL cholesterol and the gut microbiota.

The research shows that existing interventions that attempt to reduce sedentary behavior are effective. The purposes of this review were to examine: (1) how adherent individuals are to workplace sedentary behavior interventions in the short and long term and (2) how the use of incentives impact adherence in the short and long term. It was found that short-term studies showed higher rates of adherence than medium-term studies. Studies that used incentives showed lower rates of adherence than studies that did not use incentives. Medium-term studies that used incentives showed the same rates of adherence as short-term studies that used incentives, indicating that incentives can benefit adherence in longer term interventions.

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.

This thesis paper examines the effects of increased standing and light physical activity in the workplace on postprandial glucose. Sedentary behavior is detrimental to our health, affecting metabolic risk factors. An easy way to implement change is by decreasing sedentary time in workplaces where sitting is common, such as office workspaces. To consider how postprandial glucose is affected by decreasing sedentary time, participants ate a standardized meal for lunch and were asked to decrease their sitting time by replacing it with standing and light physical activity.