StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones
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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Lower PSS score was correlated with higher conversation frequency during the day and evening, longer conversation duration during the day, and longer sleep duration.
“Lower Perceived Stress Scale (PSS) score was correlated with higher conversation frequency during the day (9 AM-6 PM: the time frame participants might be in classes) and the evening (6 PM-0 AM), longer conversation duration during the day, and longer sleep duration.”
Mobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
Significant correlations exist between conversation duration and/or frequency computed from audio data and PHQ-9 depression scores, flourishing scale, and perceived stress scale.
“Significant correlations are obtained between conversation duration and/or frequency computed from audio data and PHQ-9 depression scores, flourishing scale and perceived stress scale (PSS) [63].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [63]
StudentLife combined sensing and self-reports to assess the impact of student workload on stress, whereas Snapshot tracked their mood and sleep.
“For instance, StudentLife [63] combined sensing and self-reports to assess the impact of student workload on stress, whereas Snapshot [160] tracked their mood and sleep.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [63]
Wang et al found a negative association (r=−0.362, P=.03) between the NBDC and self-reported depressive symptoms on the StudentLife data set, which contained mobile phone data from 48 students across a 10-week term at Dartmouth College.
“Wang et al found a negative association (r=−0.362, P=.03) between the NBDC and self-reported depressive symptoms on the StudentLife data set, which contained mobile phone data from 48 students across a 10-week term at Dartmouth College [15].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [15]
Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.
“Previous studies [15-18] have been performed on relatively small (approximately 100 participants) homogeneous (eg, university students) cohorts of participants over relatively short periods (8-10 weeks), which may limit their generalizability.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [15]
Bluetooth features used in previous studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.
“Besides, Bluetooth features used in these studies [15-18] have been limited to basic statistical features (eg, sum, mean, and standard deviation), which are unable to characterize some nonlinear aspects (such as complexity, regularity, and periodicity) of the Bluetooth data.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [15]
Our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months) than previous Bluetooth-related studies.
“Compared with previous Bluetooth-related studies [15-18], our study was performed on a larger (N=316) multicenter data set with a longer follow-up (median 4 months).”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [15]
Instrumenting smartphones and wearables to capture in situ, fine-grained, and moment-by-moment data sets with sensing apps has made it possible to passively collect data sets in naturalistic settings.
“Instrumenting smartphones and wearables to capture in situ, fine-grained, and moment-by-moment data sets with sensing apps [20-24] has made it possible to passively collect data sets in naturalistic settings.”
Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions.
“Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions [14,22,25-28].”
The two most common reasons students go to sleep later, discussed in the StudentLife study, are academic demands and social events.
“We do not have any further data on why students go to sleep later but the two most common reasons discussed in the StudentLife study [96] are academic demands such as assignment due dates and exams and social events such as hanging out with friends and parties.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [96]
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- Authors
- Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T.
- Affiliations
- Dartmouth College (US)
- The University of Texas at Austin (US)
- Northeastern University (US)
- Dartmouth College
- Venue
- Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
- Publisher
- ACM
- Published
- 2014-09-13
- Pages
- 3-14
- OpenAlex
- W2156567116
- Cited by
- 1,116
- References
- 38
- Citations by year
- 2026: 88 · 2025: 81 · 2024: 96 · 2023: 81 · 2022: 120 · 2021: 117 · 2020: 115 · 2019: 105 · 2018: 110 · 2017: 84 · 2016: 72 · 2015: 42 · 2014: 4 · 2013: 1
- FWCI
- 175.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Developmental and Educational Psychology › Student Stress and Coping
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- student stress, workload, mental health, smartphone sensing, sleep patterns, physical activity, mood, sociability, academic performance, positive affect
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