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

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Mobile Health

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

  1. CS65 Smartphone Programming (2013). http://www.cs.dartmouth.edu/~campbell/cs65/cs65.html

  2. Dartmouth College Weekly Schedule Diagram (2013). http://oracle-www.dartmouth.edu/dart/groucho/timetabl.diagram

  3. Depression (2016). http://www.nimh.nih.gov/health/topics/depression/index.shtml

  4. funf-open-sensing-framework (2013). https://code.google.com/p/funf-open-sensing-framework/

  5. PACO (2013). https://code.google.com/p/paco/

  6. StudentLife Dataset (2014). http://studentlife.cs.dartmouth.edu/

  7. SurveyMonkey (2013). https://www.surveymonkey.com/

  8. 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)

    Article  Google Scholar 

  9. Aldwin, C.M.: Stress, coping, and development: An integrative perspective. Guilford Press (2007)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Book  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Cohen, J.: Statistical power analysis for the behavioral sciencies. Routledge (1988)

    Google Scholar 

  19. Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. Journal of health and social behavior pp. 385–396 (1983)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Personal and ubiquitous computing 10(4), 255–268 (2006)

    Article  Google Scholar 

  23. 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

  24. 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)

    Article  Google Scholar 

  25. 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)

    Google Scholar 

  26. Hawthorne, G.: Measuring social isolation in older adults: development and initial validation of the friendship scale. Social Indicators Research 77(3), 521–548 (2006)

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. 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

  32. Kroenke, K., Spitzer, R.L., Williams, J.B.: The phq-9. Journal of general internal medicine 16(9), 606–613 (2001)

    Article  Google Scholar 

  33. 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)

    Google Scholar 

  34. 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)

    Google Scholar 

  35. 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)

    Book  Google Scholar 

  36. 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)

    Google Scholar 

  37. Martinez, D.: Predicting student outcomes using discriminant function analysis. (2001)

    Google Scholar 

  38. 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)

    Book  Google Scholar 

  39. Pollak, J.P., Adams, P., Gay, G.: PAM: a photographic affect meter for frequent, in situ measurement of affect. In: Proc. of SIGCHI (2011)

    Book  Google Scholar 

  40. Puiatti, A., Mudda, S., Giordano, S., Mayora, O.: Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder. In: Proc. of EMBC (2011)

    Book  Google Scholar 

  41. 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)

    Book  Google Scholar 

  42. 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)

    Google Scholar 

  43. 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)

    Article  Google Scholar 

  44. Russell, D.W.: UCLA loneliness scale (version 3): Reliability, validity, and factor structure. Journal of personality assessment 66(1), 20–40 (1996)

    Article  Google Scholar 

  45. Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4, 1–32 (2008)

    Article  Google Scholar 

  46. 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

  47. 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)

    Article  Google Scholar 

  48. 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

  49. 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)

    Article  Google Scholar 

  50. 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)

    Article  Google Scholar 

  51. 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

  52. 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

  53. 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)

    Google Scholar 

  54. 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)

    Google Scholar 

  55. 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)

    Article  Google Scholar 

  56. Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. Neural Networks, IEEE Transactions on 16(3), 645–678 (2005)

    Article  Google Scholar 

  57. 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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  • DOI: https://doi.org/10.1007/978-3-319-51394-2_2

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