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

doi:10.1145/2632048.2632054

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

Proceedings Article•DOI•

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

Rui Wang¹, Fanglin Chen¹, Zhenyu Chen¹, Tianxing Li¹, Gabriella M. Harari², Stefanie M. Tignor³, Xia Zhou¹, Dror Ben-Zeev¹, Andrew T. Campbell¹ - Show less +5 more•Institutions (3)

Dartmouth College¹, University of Texas at Austin², Northeastern University³

13 Sep 2014-pp 3-14

TL;DR: A Dartmouth term lifecycle is identified 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, while stress appreciably rises while positive affect, sleep, conversation and activity drops off.

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Abstract: 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.

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Journal Article•DOI•

Internet of Things: Architectures, Protocols, and Applications

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Pallavi Sethi¹, Smruti R. Sarangi¹•Institutions (1)

Indian Institute of Technology Delhi¹

26 Jan 2017-Journal of Electrical and Computer Engineering

TL;DR: This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly.

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Abstract: The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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1,025 citations

Cites background from "StudentLife: assessing mental healt..."

...[30] describe a mobile application that is based completely on a smartphone....
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...describe an application [30], which measures the stress level of a college student and is installed on the student’s smartphone....
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Journal Article•DOI•

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

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Sohrab Saeb¹, Mi Zhang², Chris Karr¹, Stephen M. Schueller¹, Marya E. Corden¹, Konrad P. Kording, David C. Mohr¹ - Show less +3 more•Institutions (2)

Northwestern University¹, Michigan State University²

15 Jul 2015-Journal of Medical Internet Research

TL;DR: The detection of daily-life behavioral markers using mobile phone global positioning systems and usage sensors and their use in identifying depressive symptom severity suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.

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Abstract: Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r =-.63, P =.005), normalized entropy (mobility between favorite locations; r =-.58, P =.012), and location variance (GPS mobility independent of location; r =-.58, P =.012). Phone usage features, usage duration, and usage frequency were also correlated ( r =.54, P =.011, and r =.52, P =.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. [J Med Internet Res 2015;17(7):e175]

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555 citations

Cites background from "StudentLife: assessing mental healt..."

...Other groups have found that phone sensors were effective at detecting social and sleep behaviors among patients with depression [20,21], and such features correlated significantly with severity of depressive symptoms [22]....
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Journal Article•

Stress, Coping, and Development: An Integrative Perspective

[...]

Susan Chizeck

01 Jan 1997-Clinical Sociology Review

524 citations

Journal Article•DOI•

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

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David C. Mohr¹, Mi Zhang², Stephen M. Schueller¹•Institutions (2)

Northwestern University¹, Michigan State University²

08 May 2017-Annual Review of Clinical Psychology

TL;DR: A layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health is provided, focused principally on smartphones, but also including studies of wearables, social media, and computers.

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Abstract: Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

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451 citations

Cites background or methods from "StudentLife: assessing mental healt..."

...Such sleep period markers have also been correlated with the severity of depressive symptoms (Wang et al. 2014)....
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...A second study, StudentLife, used the Patient Health Questionnaire-9 (PHQ-9) to assess depression among 48 students over 10 weeks (Wang et al. 2014)....
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Journal Article•DOI•

Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study.

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Jeremy F. Huckins¹, Alex W. daSilva¹, Weichen Wang¹, Elin Hedlund¹, Courtney Rogers¹, Subigya Nepal¹, Jialing Wu¹, Mikio Obuchi¹, Eilis I Murphy¹, Meghan L. Meyer¹, Dylan D. Wagner², Paul E. Holtzheimer³, Andrew T. Campbell¹ - Show less +9 more•Institutions (3)

Dartmouth College¹, Ohio State University², Dartmouth–Hitchcock Medical Center³

17 Jun 2020-Journal of Medical Internet Research

TL;DR: Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed, and a wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting.

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Abstract: Background: The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. Objective: By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media? Methods: Behaviors such as the number of locations visited, distance traveled, duration of phone usage, number of phone unlocks, sleep duration, and sedentary time were measured using the StudentLife smartphone sensing app. Depression and anxiety were assessed using weekly self-reported ecological momentary assessments of the Patient Health Questionnaire-4. The participants were 217 undergraduate students, with 178 (82.0%) students providing data during the Winter 2020 term. Differences in behaviors and self-reported mental health collected during the Winter 2020 term compared to previous terms in the same cohort were modeled using mixed linear models. Results: During the first academic term impacted by COVID-19 (Winter 2020), individuals were more sedentary and reported increased anxiety and depression symptoms (P<.001) relative to previous academic terms and subsequent academic breaks. Interactions between the Winter 2020 term and the week of the academic term (linear and quadratic) were significant. In a mixed linear model, phone usage, number of locations visited, and week of the term were strongly associated with increased amount of COVID-19–related news. When mental health metrics (eg, depression and anxiety) were added to the previous measures (week of term, number of locations visited, and phone usage), both anxiety (P<.001) and depression (P=.03) were significantly associated with COVID-19–related news. Conclusions: Compared with prior academic terms, individuals in the Winter 2020 term were more sedentary, anxious, and depressed. A wide variety of behaviors, including increased phone usage, decreased physical activity, and fewer locations visited, were associated with fluctuations in COVID-19 news reporting. While this large-scale shift in mental health and behavior is unsurprising, its characterization is particularly important to help guide the development of methods to reduce the impact of future catastrophic events on the mental health of the population.

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449 citations

Cites background or methods from "StudentLife: assessing mental healt..."

...These measures of sleep have been shown to be accurate within 30 minutes for total sleep duration [6]....
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...Smartphone sensing data and EMA surveys were administered using the StudentLife application (iOS and Android) [6]....
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...A handful of studies have employed ecological momentary assessments (EMAs) to assess depression and anxiety more frequently and in near-real-time [5-8]....
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...Unlock duration is a measurement of time during which a mobile phone is unlocked and the screen is on; it is calculated from the time the user unlocks the phone until they either manually relock the phone or it autolocks due to disuse (the iOS default is 30 seconds, while Android defaults vary by manufacturer; users can also alter this by changing their phone settings)....
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...Study components include smartphone mobile sensing through the StudentLife app [6], EMAs and surveys focusing on a variety of college experience components, and functional neuroimaging [12]....
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Open Access

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University of North Carolina at Chapel Hill¹

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"StudentLife: assessing mental healt..." refers methods in this paper

...We calculate the degree of correlation between sensing/EMA data and outcomes using the Pearson correlation [16] where r (−1 ≤ r ≤ 1) indicates the strength and direction of the correlation, and p the significance of the finding....
[...]

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Abstract: In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.

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TL;DR: In addition to making criteria-based diagnoses of depressive disorders, the PHQ-9 is also a reliable and valid measure of depression severity, which makes it a useful clinical and research tool.

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Abstract: OBJECTIVE: While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity.

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"StudentLife: assessing mental healt..." refers methods in this paper

...PHQ-9 Depression Scale....
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...We find a number of significant correlations (p ≤ 0.05) between sleep duration, conversation frequency and duration, colocation (i.e., number of Bluetooth encounters) and PHQ-9 depression, as shown Table 3....
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...There is little work on correlations between continuous and automatic sensing data from smartphones and mental health outcomes such as PHQ-9....
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...Table 2 shows the pre-post PHQ-9 depression severity for the group of students in the study....
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...Finally, we find a significant positive correlation (r = 0.412, p = 0.010) between the validated single item stress EMA [38] and the post PHQ-9 scale....
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Journal Article•DOI•

A global measure of perceived stress.

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Sheldon Cohen¹, Thomas W. Kamarck, Robin J. Mermelstein•Institutions (1)

Carnegie Mellon University¹

01 Dec 1983-Journal of Health and Social Behavior

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Abstract: This paper presents evidence from three samples, two of college students and one of participants in a community smoking-cessation program, for the reliability and validity of a 14-item instrument, the Perceived Stress Scale (PSS), designed to measure the degree to which situations in one's life are appraised as stressful. The PSS showed adequate reliability and, as predicted, was correlated with life-event scores, depressive and physical symptomatology, utilization of health services, social anxiety, and smoking-reduction maintenance. In all comparisons, the PSS was a better predictor of the outcome in question than were life-event scores. When compared to a depressive symptomatology scale, the PSS was found to measure a different and independently predictive construct. Additional data indicate adequate reliability and validity of a four-item version of the PSS for telephone interviews. The PSS is suggested for examining the role of nonspecific appraised stress in the etiology of disease and behavioral disorders and as an outcome measure of experienced levels of stress.

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23,500 citations

"StudentLife: assessing mental healt..." refers background in this paper

...There are no literal interpretation of flourishing scale, perceived stress scale (PSS) and UCLA loneliness scale instruments, as discussed in the Dataset Section....
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...perceived stress scale stress level (PSS)[17]...
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...The perceived stress scale (PSS) [17] measures the degree to which situations in a person’s life are stressful....
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...Second, we identify strong correlation between automatic sensing data and a broad set of well-known mental wellbeing measures, specifically, PHQ-9 depression, perceived stress (PSS), flourishing, and loneliness scales....
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...Mental well-being surveys. survey measure patient health depression levelquestionnaire (PHQ-9) [26] perceived stress scale stress level (PSS)[17] flourishing scale flourishing level[19] UCLA loneliness loneliness levelscale [36] and the total traveled distance (i.e., outdoor mobility) per day; 2) conversation data, including conversation duration and frequency per day; 3) sleep data, including sleep duration, sleep onset and waking time; and finally 4) location data, including GPS, inferred buildings when the participant is indoors, and the number of co-located Bluetooth devices....
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Journal Article•DOI•

Ecological Momentary Assessment

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Saul Shiffman¹, Arthur A. Stone², Michael R. Hufford•Institutions (2)

University of Pittsburgh¹, State University of New York System²

27 Mar 2008-Annual Review of Clinical Psychology

TL;DR: Ecological momentary assessment holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.

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Abstract: Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects’ current behaviors and experiences in real time, in subjects’ natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects’ lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding ligh...

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4,286 citations

"StudentLife: assessing mental healt..." refers background or methods in this paper

...The StudentLife app integrates MobileEMA, a flexible ecological momentary assessment [37] (EMA) component to probe students’ states (e....
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...We use in-situ ecological momentary assessment (EMA) [37] to capture additional human behavior beyond what the surveys and automatic sensing provide....
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