CIRCADIAN COMPUTING: SENSING AND STABILIZING BIOLOGICAL RHYTHMS

This dissertation lays the groundwork for Circadian Computing with a novel and broad vision of technologies that support and adapt to our innate biological rhythms. Similar to most terrestrial organisms, human physiology and behavior are shaped by a 24-hour periodicity known as circadian rhythm. Indeed, almost every neurobehavioral process including our sleep, metabolism, cognitive performance, and mood reflects circadian rhythms. These rhythms ensure synchronization across different processes and as such, are crucial for our health and well-being. Persistent circadian disruption increases risk for cancer, obesity, and cardiovascular diseases. It has been associated with occupational accidents and serious loss of productivity in the workplace as well. Recent findings have also started identifying links between circadian disruption and mental illnesses including bipolar disorder and schizophrenia. However, in our modern world, circadian disruption is becoming increasingly widespread. The invention of artificial light fundamentally changed our ancestral sleep and wakeup patterns. Since then, we have gradually moved towards a 24-hour society. The recent development in entertainment and communication technologies has also resulted in an “always-on” ethos. The resulting trend is worrisome. Sleep pathologies are reaching an epidemic level with 70\% of the population suffering from significant circadian disruptions. As a result, recently there has been an increased focus on monitoring and identifying disruptions in circadian rhythms. However, these methods and findings are often limited to controlled lab environments. As a result, they are not adequate for granular monitoring of circadian disruptions in the wild over a longitudinal period of time. As such, there is a need for novel pervasive technologies for tracking, monitoring, and modeling circadian disruptions and its impact in the real world. There is also an opportunity for developing intervention tools for maintaining circadian stability. This dissertation is a leading step towards the broad vision of circadian-aware technologies for sensing, adapting to, and stabilizing our innate biological rhythms. In my PhD work, I have shown the feasibility of bringing a circadian-aware perspective across different application domains. Specifically, I have developed and evaluated methods for unobtrusively assessing circadian disruptions. I have also showed that behavioral and contextual data can be used for modeling and predicting alertness — a circadian process integral to our cognitive performance. I have also developed, deployed, and evaluated a data driven tool focusing on identifying circadian anomalies in patients with bipolar disorder. With this groundwork in place, I believe that there is an exciting opportunity lying ahead for Circadian Computing. In particular, circadian-aware technologies can potentially reshape a number of application domains including education and learning, optimized scheduling, mental health care, and chronotherapy. I hope this dissertation motivates a circadian perspective in future technology development and contributes to the shared effort of improving our productivity, health, and well-being

Cognition-aware systems to support information intake and learning

Knowledge is created at an ever-increasing pace putting us under constant pressure to consume and acquire new information. Information gain and learning, however, require time and mental resources. While the proliferation of ubiquitous computing devices, such as smartphones, enables us to consume information anytime and anywhere, technologies are often disruptive rather than sensitive to the current user context. While people exhibit different levels of concentration and cognitive capacity throughout the day, applications rarely take these performance variations into account and often overburden their users with information or fail to stimulate. This work investigates how technology can be used to help people effectively deal with information intake and learning tasks through cognitive context-awareness. By harvesting sensor and usage data from mobile devices, we obtain people's levels of attentiveness, receptiveness, and cognitive performance. We subsequently use this cognition-awareness in applications to help users process information more effectively. Through a series of lab studies, online surveys, and field experiments we follow six research questions to investigate how to build cognition-aware systems. Awareness of user's variations in levels of attention, receptiveness, and cognitive performance allows systems to trigger appropriate content suggestions, manage user interruptions, and adapt User Interfaces in real-time to match tasks to the user's cognitive capacities. The tools, insights, and concepts described in this book allow researchers and application designers to build systems with an awareness of momentary user states and general circadian rhythms of alertness and cognitive performance

Objective physical activity and sleep characteristic measurements using a triaxial accelerometer in eight year olds

Background: Secular trends demonstrate that young children are less active and sleep less. Inequity in an individual\u27s energy balance is known to have poor health outcomes. Academic achievement, academic behavior, and weight status are proxy indicators for health and psychosocial outcomes in this study. Current guidelines in place for sleep and physical activity in childhood are the result of data collected in the form of self-reports. Quantification and qualification of physical activity dimensions and sleep characteristics are essential not only for the purpose of clearly establishing parameters but also for the intent of verifying optimal health outcomes and evaluating interventions related to conditions of energy balance. Purpose: The purpose of this research was to determine the relationships amongst and between the objective dimensions of physical activity, sleep, weight status, academic achievement, and academic behavior. Methods: This cross-sectional correlational descriptive design study monitored the physical activity and sleep duration for 24 hours per day for 7 consecutive days with triaxial accelerometers. Data was successfully gathered on 55 low socioeconomic income African American eight-year-olds. Weight status was measured and body mass index (BMI) was calculated. Standardized scores, subjective grades from the teachers, and attendance records were obtained from the schools. A qualitative component gathered demographic information related to home life, meal habits, and play times. Results: This sample was predominantly overweight/obese. Light intensity activity accounted for 86% of their daytime hours while vigorous activity accounted for less than 1%. Moderate-vigorous activity bouts were inversely significantly correlated with the standardized reading scores. Students with failing reading scores had significantly more time per day in light activity and less time in moderate intensity activity. This sample averaged 8 hours of sleep per night. Students with failing math scores had significantly longer mean wake episodes at night. A significant difference between hours of sleep and weight status was seen. The overweight/obese child slept, on average, less than the normal weight child. Conclusions: Sleep is an important health indicator. Lack of sleep has academic implications. Different weight classifications may benefit from different interventional activities. Future studies should be conducted with larger and diverse samples

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders: Evidence Report

In April 2010, President Obama declared a space pioneering goal for the United States in general and NASA in particular. "Fifty years after the creation of NASA, our goal is no longer just a destination to reach. Our goal is the capacity for people to work and learn and operate and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite." Thus NASA's Strategic Objective 1.1 emerged as "expand human presence into the solar system and to the surface of Mars to advance exploration, science, innovation, benefits to humanity, and international collaboration" (NASA 2015b). Any space flight, be it of long or short duration, occurs in an extreme environment that has unique stressors. Even with excellent selection methods, the potential for behavioral problems among space flight crews remain a threat to mission success. Assessment of factors that are related to behavioral health can help minimize the chances of distress and, thus, reduce the likelihood of adverse cognitive or behavioral conditions and psychiatric disorders arising within a crew. Similarly, countermeasures that focus on prevention and treatment can mitigate the cognitive or behavioral conditions that, should they arise, would impact mission success. Given the general consensus that longer duration, isolation, and confined missions have a greater risk for behavioral health ensuring crew behavioral health over the long term is essential. Risk, which within the context of this report is assessed with respect to behavioral health and performance, is addressed to deter development of cognitive and behavioral degradations or psychiatric conditions in space flight and analog populations, and to monitor, detect, and treat early risk factors, predictors and other contributing factors. Based on space flight and analog evidence, the average incidence rate of an adverse behavioral health event occurring during a space mission is relatively low for the current conditions. While mood and anxiety disturbances have occurred, no behavioral emergencies have been reported to date in space flight. Anecdotal and empirical evidence indicate that the likelihood of an adverse cognitive or behavioral condition or psychiatric disorder occurring greatly increases with the length of a mission. Further, while cognitive, behavioral, or psychiatric conditions might not immediately and directly threaten mission success, such conditions can, and do, adversely impact individual and crew health, welfare, and performance

The combined benefits of dispositional mindfulness and trait self-compassion as potential buffers of the effects of perceived stress on sleep quality in college-aged young adults.

Using a nonclinical sample of 108 undergraduates between the ages of 18 to 25 years old, this cross-sectional study investigated the relationship between dispositional mindfulness (as measured by the Five Facet Mindfulness Questionnaire; Baer et al., 2006) and sleep quality (as measured by the Pittsburgh Sleep Quality Index; Buysse et al., 1989). Second, it evaluated the association between trait self-compassion (as measured by the Self-Compassion Scale; Neff, 2003b) and sleep quality. Third, it aimed to test for an interaction effect between dispositional mindfulness and trait self-compassion as buffers against the adverse effects of perceived stress on sleep quality. Results showed that there was a significant moderate and negative association between dispositional mindfulness and sleep quality (r = -.48, p \u3c .01). Similarly, results showed that there was a significant moderate and negative association between trait self-compassion and sleep quality (r = -.38, p \u3c .01). Taken together, these findings indicate that in this sample of college-aged young adults, higher levels of dispositional mindfulness and trait self-compassion respectively were associated with better sleep quality. This is consistent with previous research examining the associations between dispositional mindfulness and sleep quality (Howell et al., 2008; Lau et al., 2008; Murphy et al., 2012) as well as between trait self-compassion and sleep quality (Brown et al., 2021; Butz & Stalhberg, 2018; Hu et al., 2018). Therefore, the present study\u27s findings add to the extant body of literature demonstrating associations between higher levels of dispositional mindfulness and trait self-compassion respectively with better sleep quality. Contrary to the study\u27s Hypotheses 3a and 3b, the hypothesized three-way interaction among perceived stress, dispositional mindfulness, and trait self-compassion was not supported, given that moderated moderation analyses revealed no significant interaction among these three variables (b = -.001, t(100) = -.53, p = .60, 95% Confidence Interval: [-.006, .004], ΔR2 = .002, ΔF = .28). This indicates that in the current sample, the strength of the association between perceived stress and poor sleep quality did not vary based on participants\u27 levels of dispositional mindfulness and trait self-compassion