Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Why Do People Adopt, or Reject, Smartphone Password Managers?

People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The study’s findings will help us to market these tools more effectively in order to encourage future adoption of password managers

The role of an omnipresent pocket device : smartphone attendance and the role of user habits

Smartphones are convergent, always-on pocket devices that have taken up an important role in the life of their users. This warrants a closer look into how this medium is used in every-day situations. Are goal-oriented incentives the main drive for smartphone usage, or do habits play a critical role? This study with 481 Belgian smartphone users attempts to describe the precedents of smartphone attendance by validating the model of media attendance (MMA), a social-cognitive theory of uses and gratifications (LaRose & Eastin, 2004). We surprisingly did not find evidence for a significant effect of habits on smartphone usage. We suggest two explanations. First, we suggest some uncertainties concerning the MMA methodology. Second, we suggest a more complex reality in which several habitual use patterns are shaped, dependent on user, context and device. This warrants a more in-depth study, using more advanced measures for smartphone usage and habit strength

Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

Problematic internet usage: the impact of objectively Recorded and categorized usage time, emotional intelligence components and subjective happiness about usage

Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI (sociability, emotionality, well-being, self-control), and happiness with phone use can predict PIU and its components (obsession, neglect, and control disorder). A total of 268 participants (Female: 61.6%) reported their demographic and completed a questionnaire that included Problematic Internet Usage Questionnaire short form (PIUQ–SF–6), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), level of happiness with the amount and frequency of smartphone use, and living conditions (whether alone or with others). Their smartphone usage was objectively recorded through a dedicated app. A series of one-way ANOVA revealed no significant difference in PIU for different living conditions and a significant difference in the subjective level of happiness with phone usage (F (3, 264) = 7.55, p \u3c .001), as well as of the frequency of usage where the unhappy group had higher PIU (F (3, 264) = 6.85, p \u3c .001). Multiple linear regression analysis showed that happiness with phone usage (β = −.17), the actual usage of communication (β = .17), social media (β = .19) and gaming apps (β = .13), and trait EI component of self-control (β = −.28) were all significant predictors of PIU. Moreover, gender, age, and happiness with the frequency of phone usage were not significant predictors of PIU. The whole model accounted for the total variance of PIU by 32.5% (Adjusted R2 = .287). Our study contributes to the literature by being among the few to rely on objectively recorded smartphone usage data and utilizing components of trait EI as predictors

Understanding Perceptions of Problematic Facebook Use: When People Experience Negative Life Impact and a Lack of Control

While many people use social network sites to connect with friends and family, some feel that their use is problematic, seriously affecting their sleep, work, or life. Pairing a survey of 20,000 Facebook users measuring perceptions of problematic use with behavioral and demographic data, we examined Facebook activities associated with problematic use as well as the kinds of people most likely to experience it. People who feel their use is problematic are more likely to be younger, male, and going through a major life event such as a breakup. They spend more time on the platform, particularly at night, and spend proportionally more time looking at profiles and less time browsing their News Feeds. They also message their friends more frequently. While they are more likely to respond to notifications, they are also more likely to deactivate their accounts, perhaps in an effort to better manage their time. Further, they are more likely to have seen content about social media or phone addiction. Notably, people reporting problematic use rate the site as more valuable to them, highlighting the complex relationship between technology use and well-being. A better understanding of problematic Facebook use can inform the design of context-appropriate and supportive tools to help people become more in control.Comment: CHI 201

The Double-edged Sword: A Mixed Methods Study of the Interplay between Bipolar Disorder and Technology Use

Human behavior is increasingly reflected or acted out through technology. This is of particular salience when it comes to changes in behavior associated with serious mental illnesses including schizophrenia and bipolar disorder. Early detection is crucial for these conditions but presently very challenging to achieve. Potentially, characteristics of these conditions\u27 traits and symptoms, at both idiosyncratic and collective levels, may be detectable through technology use patterns. In bipolar disorder specifically, initial evidence associates changes in mood with changes in technology-mediated communication patterns. However much less is known about how people with bipolar disorder use technology more generally in their lives, how they view their technology use in relation to their illness, and, perhaps most crucially, the causal relationship (if any exists) between their technology use and their disease. To address these uncertainties, we conducted a survey of people with bipolar disorder (N = 84). Our results indicate that technology use varies markedly with changes in mood and that technology use broadly may have potential as an early warning signal of mood episodes. We also find that technology for many of these participants is a double-edged sword: acting as both a culprit that can trigger or exacerbate symptoms as well as a support mechanism for recovery. These findings have implications for the design of both early warning systems and technology-mediated interventions

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations