Stefanie M. Tignor

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.

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.

The meaning of guilt: Reconciling the past to inform the future.

Despite decades of empirical research, a deceptively simple question remains unanswered: Is guilt good? Whereas some researchers assert that routine experiences of guilt (i.e., “trait guilt”) are maladaptive and indicative of poor psychological adjustment, others assert trait guilt to be adaptive and indicative of a prosocial disposition. In the current research we outline the theoretical underpinnings of 2 of the most commonly employed measures of trait guilt: unsituated measures (e.g., the Personal Feelings Questionnaire (PFQ; Harder & Lewis, 1987) and situated scenario-based measures (e.g., the Test of Self-Conscious Affect [TOSCA]; Tangney, Wagner, & Gramzow, 1989). We examine the construct validity of both measure types across 3 studies using a variety of traits (self- and informant-reported), states, and behaviors. Results provide overwhelming support for a “2-construct” argument, with PFQ guilt (our unsituated measure of choice) and TOSCA guilt (our situated measure of choice) displaying divergent results across nearly all traits, states, and behaviors measured. While the correlates of PFQ guilt were consistently maladaptive, the correlates of TOSCA guilt were consistently adaptive. Furthermore, only the PFQ predicted daily experiences of negative affect and state guilt. TOSCA guilt was unrelated to negative affective experience in daily life, thereby calling into question its conceptualization as an affective trait. Findings using the TOSCA and PFQ shame scales are also presented. We conclude by presenting a preliminary process model of guilt that may have utility for designing future research studies and developing new guilt questionnaires.