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 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.
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References
CS65 Smartphone Programming (2013). http://www.cs.dartmouth.edu/~campbell/cs65/cs65.html
Dartmouth College Weekly Schedule Diagram (2013). http://oracle-www.dartmouth.edu/dart/groucho/timetabl.diagram
Depression (2016). http://www.nimh.nih.gov/health/topics/depression/index.shtml
funf-open-sensing-framework (2013). https://code.google.com/p/funf-open-sensing-framework/
PACO (2013). https://code.google.com/p/paco/
StudentLife Dataset (2014). http://studentlife.cs.dartmouth.edu/
SurveyMonkey (2013). https://www.surveymonkey.com/
Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7(6), 643–659 (2011)
Aldwin, C.M.: Stress, coping, and development: An integrative perspective. Guilford Press (2007)
Bang, S., Kim, M., Song, S.K., Park, S.J.: Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors. In: Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pp. 5200–5203. IEEE (2008)
Bardram, J.E., Frost, M., Szántó, K., Marcu, G.: The monarca self-assessment system: a persuasive personal monitoring system for bipolar patients. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 21–30. ACM (2012)
Bergman, R.J., Bassett Jr, D.R., Klein, D.A.: Validity of 2 devices for measuring steps taken by older adults in assisted-living facilities. Journal of physical activity & health 5 (2008)
Bravata, D.M., Smith-Spangler, C., Sundaram, V., Gienger, A.L., Lin, N., Lewis, R., Stave, C.D., Olkin, I., Sirard, J.R.: Using pedometers to increase physical activity and improve health: a systematic review. Jama 298(19), 2296–2304 (2007)
Burns, M.N., Begale, M., Duffecy, J., Gergle, D., Karr, C.J., Giangrande, E., Mohr, D.C.: Harnessing context sensing to develop a mobile intervention for depression. Journal of medical Internet research 13(3) (2011)
Cameron, A.C., Windmeijer, F.A.: R-squared measures for count data regression models with applications to health-care utilization. Journal of Business & Economic Statistics 14(2), 209–220 (1996)
Chen, Z., Lin, M., Chen, F., Lane, N.D., Cardone, G., Wang, R., Li, T., Chen, Y., Choudhury, T., Campbell, A.T.: Unobtrusive sleep monitoring using smartphones. In: Proc. of PervasiveHealth (2013)
Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Bordello, G., Hemingway, B., et al.: The mobile sensing platform: An embedded activity recognition system. Pervasive Computing, IEEE 7(2), 32–41 (2008)
Cohen, J.: Statistical power analysis for the behavioral sciencies. Routledge (1988)
Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. Journal of health and social behavior pp. 385–396 (1983)
Cowie, R., Douglas-Cowie, E.: Automatic statistical analysis of the signal and prosodic signs of emotion in speech. In: Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, vol. 3, pp. 1989–1992. IEEE (1996)
Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D.w., Oishi, S., Biswas-Diener, R.: New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research 97(2), 143–156 (2010)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and ubiquitous computing 10(4), 255–268 (2006)
Fausett, L., Elwasif, W.: Predicting performance from test scores using backpropagation and counterpropagation. In: Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, vol. 5, pp. 3398–3402 vol.5 (1994). doi: 10.1109/ICNN.1994.374782
France, D.J., Shiavi, R.G., Silverman, S., Silverman, M., Wilkes, D.M.: Acoustical properties of speech as indicators of depression and suicidal risk. Biomedical Engineering, IEEE Transactions on 47(7), 829–837 (2000)
Frost, M., Doryab, A., Faurholt-Jepsen, M., Kessing, L.V., Bardram, J.E.: Supporting disease insight through data analysis: refinements of the monarca self-assessment system. In: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 133–142. ACM (2013)
Hawthorne, G.: Measuring social isolation in older adults: development and initial validation of the friendship scale. Social Indicators Research 77(3), 521–548 (2006)
John, O.P., Srivastava, S.: The big five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research 2, 102–138 (1999)
Kasckow, J., Zickmund, S., Rotondi, A., Mrkva, A., Gurklis, J., Chinman, M., Fox, L., Loganathan, M., Hanusa, B., Haas, G.: Development of telehealth dialogues for monitoring suicidal patients with schizophrenia: Consumer feedback. Community mental health journal pp. 1–4 (2013)
Kirmayer, L.J., Robbins, J.M., Dworkind, M., Yaffe, M.J.: Somatization and the recognition of depression and anxiety in primary care. The American journal of psychiatry (1993)
Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai, vol. 14, pp. 1137–1145 (1995)
Kotsiantis, S., Pintelas, P.: Predicting students marks in hellenic open university. In: Advanced Learning Technologies, 2005. ICALT 2005. Fifth IEEE International Conference on, pp. 664–668 (2005). doi: 10.1109/ICALT.2005.223
Kroenke, K., Spitzer, R.L., Williams, J.B.: The phq-9. Journal of general internal medicine 16(9), 606–613 (2001)
Lane, N.D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., Campbell, A.: Bewell: A smartphone application to monitor, model and promote wellbeing. In: Proc. of PervasiveHealth (2011)
Lu, H., Frauendorfer, D., Rabbi, M., Mast, M.S., Chittaranjan, G.T., Campbell, A.T., Gatica-Perez, D., Choudhury, T.: Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 351–360. ACM (2012)
Lu, H., Yang, J., Liu, Z., Lane, N.D., Choudhury, T., Campbell, A.T.: The jigsaw continuous sensing engine for mobile phone applications. In: Proc. of SenSys (2010)
Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, pp. 291–300. ACM (2010)
Martinez, D.: Predicting student outcomes using discriminant function analysis. (2001)
Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T.: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In: Proc. of SenSys (2008)
Pollak, J.P., Adams, P., Gay, G.: PAM: a photographic affect meter for frequent, in situ measurement of affect. In: Proc. of SIGCHI (2011)
Puiatti, A., Mudda, S., Giordano, S., Mayora, O.: Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder. In: Proc. of EMBC (2011)
Rabbi, M., Ali, S., Choudhury, T., Berke, E.: Passive and in-situ assessment of mental and physical well-being using mobile sensors. In: Proc. of UbiComp (2011)
Rachuri, K.K., Musolesi, M., Mascolo, C., Rentfrow, P.J., Longworth, C., Aucinas, A.: Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, pp. 281–290 (2010)
Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S.: Web usage mining for predicting final marks of students that use moodle courses. Computer Applications in Engineering Education 21(1), 135–146 (2013)
Russell, D.W.: UCLA loneliness scale (version 3): Reliability, validity, and factor structure. Journal of personality assessment 66(1), 20–40 (1996)
Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008)
Tamhane, A., Ikbal, S., Sengupta, B., Duggirala, M., Appleton, J.: Predicting student risks through longitudinal analysis. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pp. 1544–1552. ACM, New York, NY, USA (2014). doi: 10.1145/2623330.2623355. URL http://doi.acm.org/10.1145/2623330.2623355
Taylor, S.E., Welch, W.T., Kim, H.S., Sherman, D.K.: Cultural differences in the impact of social support on psychological and biological stress responses. Psychological Science 18(9), 831–837 (2007)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58(1), pp. 267–288 (1996). URL http://www.jstor.org/stable/2346178
Trockel, M.T., Barnes, M.D., Egget, D.L.: Health-related variables and academic performance among first-year college students: Implications for sleep and other behaviors. Journal of American college health 49(3), 125–131 (2000)
Tudor-Locke, C., Sisson, S.B., Collova, T., Lee, S.M., Swan, P.D.: Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Canadian Journal of Applied Physiology 30(6), 666–676 (2005)
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., Campbell, A.T.: Studentlife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’14, pp. 3–14. ACM, New York, NY, USA (2014). doi: 10.1145/2632048.2632054. URL http://doi.acm.org/10.1145/2632048.2632054
Wang, R., Harari, G., Hao, P., Zhou, X., Campbell, A.T.: Smartgpa: How smartphones can assess and predict academic performance of college students. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’15, pp. 295–306. ACM, New York, NY, USA (2015). doi: 10.1145/2750858.2804251. URL http://doi.acm.org/10.1145/2750858.2804251
Watanabe, J.i., Matsuda, S., Yano, K.: Using wearable sensor badges to improve scholastic performance. In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pp. 139–142. ACM (2013)
Watanabe, J.I., Yano, K., Matsuda, S.: Relationship between physical behaviors of students and their scholastic performance. In: Ubiquitous Intelligence and Computing, 2013 IEEE 10th International Conference on and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp. 170–177. IEEE (2013)
Watson, D., Clark, L.A., Tellegen, A.: Development and validation of brief measures of positive and negative affect: the panas scales. Journal of personality and social psychology 54(6), 1063 (1988)
Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. Neural Networks, IEEE Transactions on 16(3), 645–678 (2005)
Zafra, A., Romero, C., Ventura, S.: Multiple instance learning for classifying students in learning management systems. Expert Systems with Applications 38(12), 15,020–15,031 (2011). doi: http://dx.doi.org/10.1016/j.eswa.2011.05.044. URL http://www.sciencedirect.com/science/article/pii/S0957417411008281
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Wang, R. et al. (2017). StudentLife: Using Smartphones to Assess Mental Health and Academic Performance of College Students. In: Rehg, J., Murphy, S., Kumar, S. (eds) Mobile Health. Springer, Cham. https://doi.org/10.1007/978-3-319-51394-2_2
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