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Introduction: College students experience high levels of depression, anxiety, and stress during the academic year as they juggle the competing demands of young adulthood and their studies. The COVID-19 pandemic heightened this experience. Healthful diets and sleep patterns are associated with improved physical and mental well-being. Purpose: The goal of this study was to identify if there was a relationship between changes in sleep and diet during the COVID-19 pandemic. Methods: A group of college students was surveyed online using Qualtrics software during the first two months of lockdown restrictions at a large, midwestern university in the United States. Results: More participants reported no changes in diet and increased hours of sleep than reported declines or improvements in diet, or reductions or retained sleep patterns. Participants who maintained their diet were likely to maintain their current sleep pattern. Conclusion: College students demonstrated resilience in healthful behaviors during the first two months of COVID-19 lockdown. [ABSTRACT FROM AUTHOR]

Amid unprecedented political polarization, the United States continues to grapple with the simultaneous crises of COVID‐19 and systemic racism. We examine potential predictors of COVID‐19 mask wearing at the intersection of these crises, with particular attention to the behavior of conservatives and young people, who have resisted compliance with efforts to contain the spread of COVID‐19. Specifically, we investigate perceptions of racial/ethnic inequities in the healthcare system and civic values as predictors of COVID‐19 mask‐wearing frequency and potential moderators of the relationship between political orientation and COVID‐19 mask‐wearing frequency. Diverse college students at a university in southern California participated in our longitudinal survey, which was initiated prior to the COVID‐19 pandemic (N = 431). We found that perceptions of inequities and civic values predicted mask wearing, whereas political conservatism was negatively related to mask wearing. Further, conservative participants were more likely to wear masks if they reported greater perceptions of inequities. Our results provide a foundation for future research that may inform targeted public health interventions aimed at encouraging responsible COVID‐19 behavior and fostering dialogue on systemic equities in a contentious political environment. [ABSTRACT FROM AUTHOR]

An increasing number of healthcare systems allow people to monitor behavior and provide feedback on health and wellness. Most applications, however, only offer feedback on behavior in form of visualization and data summaries. This paper presents a different approach-called impact factor analysis-in which machine learning techniques are used to infer the progression of a primary health parameter and then apply parameter ranking to investigate which behavioral data have the highest 'impact' on health. We have applied this approach to improve the MONARCA personal health application for patients suffering from bipolar disorder. In the MONARCA system, patients report their daily mood score and by analyzing self-reported and automatically sensed behavioral data with this mood score, the system is able to identify the impact of different behavior on the patient's mood. We report from a study involving ten bipolar patients, in which we were able to estimate mood values with an average mean absolute error of 0.5. This was used to rank the behavior parameters whose variations indicate changes in the mental state. The rankings acquired from our algorithms correspond to the patients' rankings, identifying physical activity and sleep as the highest impact parameters. These results revealed the feasibility of identifying behavioral impact factors. This data analysis motivated us to design an impact factor inference engine as part of the MONARCA system. To our knowledge, this is a novel approach in monitoring and control of mental illness, and we argue that the impact factor analysis can be useful in the design of other health and wellness systems. [ABSTRACT FROM AUTHOR]