Gabriella M. Harari

StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges

Smartphones now offer the promise of collecting behavioral data unobtrusively, in situ, as it unfolds in the course of daily life. Data can be collected from the onboard sensors and other phone logs embedded in today’s off-the-shelf smartphone devices. These data permit fine-grained, continuous collection of people’s social interactions (e.g., speaking rates in conversation, size of social groups, calls, and text messages), daily activities (e.g., physical activity and sleep), and mobility patterns (e.g., frequency and duration of time spent at various locations). In this article, we have drawn on the lessons from the first wave of smartphone-sensing research to highlight areas of opportunity for psychological research, present practical considerations for designing smartphone studies, and discuss the ongoing methodological and ethical challenges associated with research in this domain. It is our hope that these practical guidelines will facilitate the use of smartphones as a behavioral observation tool in psychological science.

Smartphone sensing methods for studying behavior in everyday life

Human behavior is the focus of many studies in the social, health, and behavioral sciences. Yet, few studies use behavioral observation methods to collect objective measures of behavior as it occurs in daily life, out in the real world — presumably the context of ultimate interest. Here, we provide a review of recent studies focused on measuring human behavior using smartphones and their embedded mobile sensors. To draw attention to current advances in the field of smartphone sensing, we describe the daily behaviors captured using these methods, which include movement behaviors (physical activity, mobility patterns), social behaviors (face-to-face encounters, computer-mediated communications), and other daily activities (non-mediated and mediated activities). We conclude by pointing to promising areas of future research for studies using Smartphone Sensing Methods (SSMs) in the behavioral sciences.

Reducing Identity Distress: Results of an Identity Intervention for Emerging Adults

Emerging adulthood is a transitional period between adolescence and adulthood where positive and negative life trajectories tend to diverge, with issues surrounding identity formation playing a key role. The present study evaluated the Miami Adult Development Project, a self-facilitated identity-focused intervention. The sample consisted of 141 emerging adults (19−29 years old; M = 23.08) who completed pretest and posttest assessments. Results indicated participation in the intervention relative to the comparison group was associated with lower levels of identity distress and higher levels of well-being via the reduction of identity distress and the development of a consolidated identity (commitment and synthesis). The present study provides evidence for the effectiveness of positive identity interventions during emerging adulthood.

Capturing Situational Information with Smartphones and Mobile Sensing Methods

Smartphones are pervasive companions to many people as they go about their daily lives. In addition, they are equipped with a wide array of sensors making it possible to measure objective information about people and situations, many times, with great fidelity, over long periods of time, in a way that is both unobtrusive and ecologically valid. Therefore, we argue that smartphones and other forms of mobile sensing are ideally suited to measuring situations. In particular, we describe how sensing methods can be used to assess situational cues, characteristics and classes. Copyright

An Evaluation of Students’ Interest in and Compliance With Self-Tracking Methods

Self-tracking consists of recording the behaviors that occur in one’s daily life. Self-tracking studies can provide researchers with passively sensed information about individual’s daily behaviors and environments and actively logged information (e.g., self-reports). This method has great promise for obtaining detailed records of behavior in naturalistic contexts, but it is not known what factors would motivate individuals to participate in self-tracking studies. Here, we analyze students’ interest in self-tracking and their compliance with self-tracking using smartphones. Three dimensions of self-tracking motivations were identified: productivity and health behaviors, well-being and daily activities, and social life on campus; these motivations were related to participation preferences and individual characteristics. We also present evidence from three studies that suggest personalized feedback combined with other incentives (course credit toward a class assignment, monetary compensation, or a prize reward) can be an effective recruitment strategy. Recommendations for the design of future self-tracking studies are presented.

Understanding the Role of Places and Activities on Mobile Phone Interaction and Usage Patterns

User interaction patterns with mobile apps and notifications are generally complex due to the many factors involved. However a deep understanding of what influences them can lead to more acceptable applications that are able to deliver information at the right time. In this paper, we present for the first time an in-depth analysis of interaction behavior with notifications in relation to the location and activity of users. We conducted an in-situ study for a period of two weeks to collect more than 36,000 notifications, 17,000 instances of application usage, 77,000 location samples, and 487 days of daily activity entries from 26 students at a UK university. Our results show that users’ attention towards new notifications and willingness to accept them are strongly linked to the location they are in and in minor part to their current activity. We consider both users’ receptivity and attentiveness, and we show that different response behaviors are associated to different locations. These findings are fundamental from a design perspective since they allow us to understand how certain types of places are linked to specific types of interaction behavior. This information can be used as a basis for the development of novel intelligent mobile applications and services.

StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 weeks term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data

We present the first large-scale (9270-user) study of data from both mobile and networked appliances for Big-Five personality inference. We correlate aggregated behavioral and physical health features with personalities, and perform binary classification using SVM and Decision Tree. We find that it is possible to infer each Big-Five personality at accuracies of 75% from this dataset despite its size and complexity (mix of mobile and appliance) as prior methods offer similar accuracy levels. We would like to achieve better accuracies and this study is a first step towards seeing how to model such data.

Using human raters to characterize the psychological characteristics of GPS-based places

This paper showcases an approach to combining smart-phone sensing technology, web mapping services, and psychological assessments to enhance our understanding of the psychological characteristics of places. For two weeks, twenty-six students used a smartphone app that passively collected GPS sensor data. Human raters then characterized their most frequently visited places on a number of psychological characteristics, such as ambience (e.g. how safe, urban, lively a place was perceived) and personality (e.g. a places perceived extroversion and conscientiousness). We explored the relationship between these place characteristics and participants own personality traits, showing how the personality traits of the average visitor to a location can be similar or different from the places characteristics. We conclude with a discussion of how this approach can be used in future research on places.