Using Social Sensing to Understand the Links between Sleep, Mood, and Sociability

Abstract

Abstract-In recent years, reality mining experiments have provided several novel insights into human social behavior that would not have been possible without the novel use of smartphone sensing. In this work, we leverage the latest reality mining experiment to study social behavior from a public health perspective. In particular, we focus on sleep and mood as they have a considerable public health impact with serious societal and significant financial effects. We endeavor to explore and uncover the associations between sleep, mood and sociability by studying a population of healthy young adults going about their everyday life. We find significant associations between sleep and mood, reiterating observations in the literature. More importantly, we find that individuals with lower overall sociability tend to report poor mood more often, a statistically significant observation. In addition, we also uncover associations between daily sociability and sleep, a previously unreported observation. These results demonstrate the potential of reality mining studies for studying the sociological aspects of significant public health problems. Further, we hope that our work will provide the impetus for larger studies validating some of these observations and ultimately result in behavioral interventions that can improve public health through better social interaction.

Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity

“…In this work, we utilize physical activity as the behavioral input due to the large body of research that supports that there is a strong relation of mental wellbeing with activity levels and sleep [17], [18], [19], [20], [21], [22], [23], [24], [25]. Further there has been a large body of work that has shown that smartphone accelerometer data can be used to sense both physical activity through activity recognition [26], [27], [28], as well as sleep [29], [30].…”

Stress Recognition Using Wearable Sensors and Mobile Phones

“…and usage-tracking functions (call and SMS histories etc.). Some studies have worked on the mood or individual trait detection using smart phones [14,15,16,17,18]. Ma et al estimated mood defined from displeasure, tiredness and tensity in daily lives using mobile phone use data and the previous subjective mood state [14].…”

Opportunities for Smartphones in Clinical Care: The Future of Mobile Mood Monitoring

“…By combining momentary assessments and sensor data, we found with Emotion Sense (http://emotionsense.org/) that self-reported mood is related to sensed physical activity, and other researchers have found that self-reported mood is related to sensed sociability and sensed sleep. 3 These data also allow analysis of behavior change: students' deteriorating well-being over the course of a university semester can be seen in self-reports of decreased mood but can also be detected through decreases in sensed sociability and sensed physical activity. 4 In a clinical context, information about the links between psychological states and behavior could be used to inform treatment as well as monitor patients' progress.…”