Elizabeth L. Murnane

Towards circadian computing: "early to bed and early to rise" makes some of us unhealthy and sleep deprived

We often think of ourselves as individuals with steady capabilities. However, converging strands of research indicate that this is not the case. Our biochemistry varies significantly over the course of a 24 hour period. Consequently our levels of alertness, productivity, physical activity, and even sensitivity to pain fluctuate throughout the day. This offers a considerable opportunity for the UbiComp community to identify novel measurements and interventions that can leverage these daily variations. To illustrate this potential, we present results from an empirical study with 9 participants over 97 days investigating whether such variations manifest in low-level smartphone use, focusing on daily rhythms related to sleep. Our findings demonstrate that phone usage patterns can be used to detect and predict individual daily variations indicative of temporal preference, sleep duration, and deprivation. We also identify opportunities and challenges for measuring and enhancing well-being using these simple and effective markers of circadian rhythms.

Collective Smile: Measuring Societal Happiness from Geolocated Images

The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of population-wide sentiment, prior research commonly focuses on text-based analyses and ignores a treasure trove of sentiment-laden content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societal happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societal events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-technical systems.

Mobile health apps: adoption, adherence, and abandonment

A myriad of mobile technologies purport to help individuals change or maintain health-related behaviors, for instance by increasing motivation or self-awareness. We provide a fine-grained categorization of popular mobile health applications and also examine the perceived efficacy of apps along with reasons underlying both app adoption and abandonment. Our findings bear implications for future tools designed to support health management.

Cognitive rhythms: unobtrusive and continuous sensing of alertness using a mobile phone

Throughout the day, our alertness levels change and our cognitive performance fluctuates. The creation of technology that can adapt to such variations requires reliable measurement with ecological validity. Our study is the first to collect alertness data in the wild using the clinically validated Psychomotor Vigilance Test. With 20 participants over 40 days, we find that alertness can oscillate approximately 30% depending on time and body clock type and that Daylight Savings Time, hours slept, and stimulant intake can influence alertness as well. Based on these findings, we develop novel methods for unobtrusively and continuously assessing alertness. In estimating response time, our model achieves a root-mean-square error of 80.64 milliseconds, which is significantly lower than the 500ms threshold used as a standard indicator of impaired cognitive ability. Finally, we discuss how such real-time detection of alertness is a key first step towards developing systems that are sensitive to our biological variations.

Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health

OBJECTIVE To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.

Mobile manifestations of alertness: connecting biological rhythms with patterns of smartphone app use

Our body clock causes considerable variations in our behavioral, mental, and physical processes, including alertness, throughout the day. While much research has studied technology usage patterns, the potential impact of underlying biological processes on these patterns is under-explored. Using data from 20 participants over 40 days, this paper presents the first study to connect patterns of mobile application usage with these contributing biological factors. Among other results, we find that usage patterns vary for individuals with different body clock types, that usage correlates with rhythms of alertness, that app use features such as duration and switching can distinguish periods of low and high alertness, and that app use reflects sleep interruptions as well as sleep duration. We conclude by discussing how our findings inform the design of biologically-friendly technology that can better support personal rhythms of performance.

Development and Evaluation of a Smartphone-Based Measure of Social Rhythms for Bipolar Disorder

Dynamic psychological processes are most often assessed using self-report instruments. This places a constraint on how often and for how long data can be collected due to the burden placed on human participants. Smartphones are ubiquitous and highly personal devices, equipped with sensors that offer an opportunity to measure and understand psychological processes in real-world contexts over the long term. In this article, we present a novel smartphone approach to address the limitations of self-report in bipolar disorder where mood and activity are key constructs. We describe the development of MoodRhythm, a smartphone application that incorporates existing self-report elements from interpersonal and social rhythm therapy, a clinically validated treatment, and combines them with novel inputs from smartphone sensors. We reflect on lessons learned in transitioning from an existing self-report instrument to one that involves smartphone sensors and discuss the potential impact of these changes on the future of psychological assessment.

Quantifying the Changeable Self: The Role of Self-Tracking in Coming to Terms With and Managing Bipolar Disorder

There has been a recent increase in the development of digital self-tracking tools for managing mental illness. Most of these tools originate from clinical practice and are, as a result, largely clinician oriented. As a consequence, little is known about the self-tracking practices and needs of individuals living with mental illness. This understanding is important to guide the design of future tools to enable people to play a greater role in managing their health. In this article, we present a qualitative study focusing on the self-tracking practices of 10 people with bipolar disorder. We seek to understand the role self-tracking has played as they have come to grips with their diagnosis and attempted to self-manage their health. A central motivation for these participants is to identify risky patterns that may be harbingers of mood episodes, as well as positive trends that support recovery. What emerges is a fragmented picture of self-tracking, with no clear delineation between clinician-initiated and self-initiated practices, as well as considerable challenges participants face in making observations of themselves when their sense of self and emotional state is in flux, uncertain, and unreliable. Informed by these observations, we discuss the merits of a new form of self-tracking that combines manual and automated methods, addresses both clinician and individual needs, helps engage people with bipolar disorder in treatment, and seeks to overcome the significant challenges they face in self-monitoring.

Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms

Human physiology and behavior are deeply rooted in the daily 24 h temporal structure. Our biological processes vary significantly, predictably, and idiosyncratically throughout the day in accordance with these circadian rhythms, which in turn influence our physical and mental performance. Prolonged disruption of biological rhythms has serious consequences for physical and mental well-being, contributing to cardiovascular disease, cancer, obesity, and mental health problems. Here we present Circadian Computing, technologies that are aware of and can have a positive impact on our internal rhythms. We use a combination of automated sensing of behavioral traits along with manual ecological momentary assessments (EMA) to model body clock patterns, detect disruptions, and drive in-situ interventions. Identifying disruptions and providing circadian interventions is particularly valuable in the context of mental health—for example, to help prevent relapse in patients with bipolar disorder. More generally, such personalized, data-driven tools are capable of adapting to individual rhythms and providing more biologically attuned support in a number of areas including physical and cognitive performance, sleep, clinical therapy, and overall wellbeing. This chapter describes the design, development, and deployment of these “circadian-aware” systems: a novel class of technology aimed at modeling and maintaining our innate biological rhythms.

Playing with your data: towards personal informatics driven games

Personal Informatics technologies and the quantified-self movement focus on helping people collect personally meaningful information to gain self-knowledge, which can go hand in hand with the drive to change behavior or improve oneself. The field of serious games examines how games can be used for purposes beyond entertainment, with common applications in areas such as education, training, or health care. This workshop paper overviews our research aimed at bridging and expanding the scopes of these fields through the design of personal informatics driven games: gameful and playful approaches to data capture, self-reflection, and behavioral intervention.