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First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing

Published: 07 July 2022 Publication History

Abstract

The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social demands. First-year students need to gain new skills and strategies to cope with these new demands in order to make good decisions, ease their transition to independent living and ultimately succeed. In general, first-generation students are less prepared when they enter college in comparison to non-first-generation students. This presents additional challenges for first-generation students to overcome and be successful during their college years. We study first-year students through the lens of mobile phone sensing across their first year at college, including all academic terms and breaks. We collect longitudinal mobile sensing data for N=180 first-year college students, where 27 of the students are first-generation, representing 15% of the study cohort and representative of the number of first-generation students admitted each year at the study institution, Dartmouth College. We discuss risk factors, behavioral patterns and mental health of first-generation and non-first-generation students. We propose a deep learning model that accurately predicts the mental health of first-generation students by taking into account important distinguishing behavioral factors of first-generation students. Our study, which uses the StudentLife app, offers data-informed insights that could be used to identify struggling students and provide new forms of phone-based interventions with the goal of keeping students on track.

Supplementary Material

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Supplemental movie, appendix, image and software files for, First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing

References

[1]
Saeed Abdullah, Mark Matthews, Ellen Frank, Gavin Doherty, Geri Gay, and Tanzeem Choudhury. 2016. Automatic detection of social rhythms in bipolar disorder. Journal of the American Medical Informatics Association 23, 3 (2016), 538--543.
[2]
Daniel A Adler, Dror Ben-Zeev, Vincent WS Tseng, John M Kane, Rachel Brian, Andrew T Campbell, Marta Hauser, Emily A Scherer, and Tanzeem Choudhury. 2020. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth and uHealth 8, 8 (2020), e19962.
[3]
Apple 2021. Extending Your App's Background Execution Time. https://developer.apple.com/documentation/uikit/app_and_environment/scenes/preparing_your_ui_to_run_in_the_background/extending_your_app_s_background_execution_time.
[4]
AWARE framework 2013. Aware: Open-source Context Instrumentation Framework For Everyone. http://www.awareframework.com/.
[5]
Yoav Benjamini, Abba M Krieger, and Daniel Yekutieli. 2006. Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 3 (2006), 491--507.
[6]
Janet Mancini Billson and Margaret Brooks Terry. 1982. In Search of the Silken Purse: Factors in Attrition among First-Generation Students. Revised. (1982).
[7]
Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi, and Alex (Sandy) Pentland. 2014. Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits. In Proceedings of the 22Nd ACM International Conference on Multimedia (MM '14). ACM, New York, NY, USA, 477--486. https://doi.org/10.1145/2647868.2654933
[8]
Mehdi Boukhechba, Alexander R. Daros, Karl Fua, Philip I. Chow, Bethany A. Teachman, and Laura E. Barnes. 2018. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones. Smart Health (July 2018). https://doi.org/10.1016/j.smhl.2018.07.005
[9]
Mehdi Boukhechba, Yu Huang, Philip Chow, Karl Fua, Bethany A Teachman, and Laura E Barnes. 2017. Monitoring social anxiety from mobility and communication patterns. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. 749--753.
[10]
Pierre Bourdieu, John G Richardson, et al. 1986. Handbook of Theory and Research for the Sociology of Education. The forms of capital (1986), 241--258.
[11]
Noli Brazil and Matthew Andersson. 2018. Mental Well-Being and Changes in Peer Ability From High School to College. Youth & Society (March 2018), 0044118X1876452. https://doi.org/10.1177/0044118X18764526
[12]
Ian Brissette, Michael F Scheier, and Charles S Carver. 2002. The role of optimism in social network development, coping, and psychological adjustment during a life transition. Journal of personality and social psychology 82, 1 (2002), 102.
[13]
Khanh Van T Bui. 2002. First-generation college students at a four-year university: Background characteristics, reasons for pursuing higher education, and first-year experiences. College Student Journal 36, 1 (2002), 3--12.
[14]
Elizabeth A Canning, Jennifer LaCosse, Kathryn M Kroeper, and Mary C Murphy. 2020. Feeling like an imposter: the effect of perceived classroom competition on the daily psychological experiences of first-generation college students. Social Psychological and Personality Science 11, 5 (2020), 647--657.
[15]
Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, 1293--1304.
[16]
Zhenyu Chen, Mu Lin, Fanglin Chen, Nicholas D Lane, Giuseppe Cardone, Rui Wang, Tianxing Li, Yiqiang Chen, Tanzeem Choudhury, and Andrew T Campbell. 2013. Unobtrusive sleep monitoring using smartphones. In 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops. IEEE, 145--152.
[17]
Colleen S Conley, Alexandra C Kirsch, Daniel A Dickson, and Fred B Bryant. 2014. Negotiating the transition to college: Developmental trajectories and gender differences in psychological functioning, cognitive-affective strategies, and social well-being. Emerging Adulthood 2, 3 (2014), 195--210.
[18]
Catherine M Coveney. 2014. Managing sleep and wakefulness in a 24-hour world. Sociology of health & illness 36, 1 (2014), 123--136.
[19]
Robert Crosnoe and Chandra Muller. 2014. Family socioeconomic status, peers, and the path to college. Social problems 61, 4 (2014), 602--624.
[20]
Kadir Demirci, Mehmet Akgönül, and Abdullah Akpinar. 2015. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions 4, 2 (2015), 85--92.
[21]
Jessica M Dennis, Jean S Phinney, and Lizette Ivy Chuateco. 2005. The role of motivation, parental support, and peer support in the academic success of ethnic minority first-generation college students. Journal of college student development 46, 3 (2005), 223--236.
[22]
Kevin Eagan, Ellen Bara Stolzenberg, Joseph J Ramirez, Melissa C Aragon, Maria Ramirez Suchard, and Sylvia Hurtado. 2014. The American freshman: National norms fall 2014. Los Angeles: Higher Education Research Institute, UCLA (2014).
[23]
Fifth Edition et al. 2013. Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc 21 (2013), 591--643.
[24]
Daniel Eisenberg, Ezra Golberstein, and Justin B Hunt. 2009. Mental health and academic success in college. The BE Journal of Economic Analysis & Policy 9, 1 (2009).
[25]
Walid El Ansari and Christiane Stock. 2010. Is the health and wellbeing of university students associated with their academic performance? Cross sectional findings from the United Kingdom. International journal of environmental research and public health 7, 2 (2010), 509--527.
[26]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, Vol. 96. 226--231.
[27]
Farhan, Yue, Morillo, Ware, Lu, Bi, Kamath, Russell, Bamis, and Wang. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In 2016 IEEE Wireless Health (WH). IEEE, Bethesda, MD, USA, 1--8. https://doi.org/10.1109/WH.2016.7764553
[28]
Aaron Fisher, Cynthia Rudin, and Francesca Dominici. 2019. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. J. Mach. Learn. Res. 20, 177 (2019), 1--81.
[29]
Kim Fromme, William R. Corbin, and Marc I. Kruse. 2008. Behavioral Risks during the Transition from High School to College. Developmental psychology 44, 5 (Sept. 2008), 1497--1504. https://doi.org/10.1037/a0012614
[30]
Google Activity Recognition Api. 2019. Google Activity Recognition Api. https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognitionClient.
[31]
Gabriella M Harari, Samuel D Gosling, Rui Wang, Fanglin Chen, Zhenyu Chen, and Andrew T Campbell. 2017. Patterns of behavior change in students over an academic term: A preliminary study of activity and sociability behaviors using smartphone sensing methods. Computers in Human Behavior 67 (2017), 129--138.
[32]
Gabriella M Harari, Nicholas D Lane, Rui Wang, Benjamin S Crosier, Andrew T Campbell, and Samuel D Gosling. 2016. Using smartphones to collect behavioral data in psychological science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science 11, 6 (2016), 838--854.
[33]
Douglas N Harris. 2010. How do school peers influence student educational outcomes? Theory and evidence from economics and other social sciences. Teachers college record 112, 4 (2010), 1163--1197.
[34]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.
[35]
Cassandra Holinka. 2015. Stress, emotional intelligence, and life satisfaction in college students. College Student Journal 49, 2 (2015), 300--311.
[36]
Joann Horton. 2015. Identifying at-risk factors that affect college student success. International Journal of Process Education 7, 1 (2015), 83--101.
[37]
Yu Huang, Haoyi Xiong, Kevin Leach, Yuyan Zhang, Philip Chow, Karl Fua, Bethany A. Teachman, and Laura E. Barnes. 2016. Assessing Social Anxiety Using Gps Trajectories and Point-of-interest Data. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '16). ACM, New York, NY, USA, 898--903. https://doi.org/10.1145/2971648.2971761
[38]
Jeremy F Huckins, Alex W DaSilva, Rui Wang, Weichen Wang, Elin L Hedlund, Eilis I Murphy, Richard B Lopez, Courtney Rogers, Paul E Holtzheimer, William M Kelley, et al. 2019. Fusing mobile phone sensing and brain imaging to assess depression in college students. Frontiers in neuroscience 13 (2019), 248.
[39]
iOS Core Motion. 2019. iOS Core Motion. https://developer.apple.com/documentation/coremotion.
[40]
Terry T Ishitani. 2003. A longitudinal approach to assessing attrition behavior among first-generation students: Time-varying effects of pre-college characteristics. Research in higher education 44, 4 (2003), 433--449.
[41]
Sharon Rae Jenkins, Aimee Belanger, Melissa Londoño Connally, Adriel Boals, and Kelly M Durón. 2013. First-generation undergraduate students' social support, depression, and life satisfaction. Journal of College Counseling 16, 2 (2013), 129--142.
[42]
Laura E. Knouse, Greg Feldman, and Emily J. Blevins. 2014. Executive functioning difficulties as predictors of academic performance: Examining the role of grade goals. Learning and Individual Differences 36 (Dec. 2014), 19--26. https://doi.org/10.1016/j.lindif.2014.07.001
[43]
Kurt Kroenke, Robert L Spitzer, and Janet BW Williams. 2001. The PHQ-9: validity of a brief depression severity measure. Journal of general internal medicine 16, 9 (2001), 606--613.
[44]
Kurt Kroenke, Robert L Spitzer, Janet BW Williams, and Bernd Löwe. 2009. An ultra-brief screening scale for anxiety and depression: the PHQ-4. Psychosomatics 50, 6 (2009), 613--621.
[45]
Emily G Lattie, Rachel Kornfield, Kathryn E Ringland, Renwen Zhang, Nathan Winquist, and Madhu Reddy. 2020. Designing Mental Health Technologies that Support the Social Ecosystem of College Students. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--15.
[46]
Vladimir I Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, Vol. 10. 707--710.
[47]
Robert LiKamWa, Yunxin Liu, Nicholas D Lane, and Lin Zhong. 2013. MoodScope: Building a Mood Sensor from Smartphone Usage Patterns. (2013), 13.
[48]
Hubert W Lilliefors. 1967. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of the American statistical Association 62, 318 (1967), 399--402.
[49]
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017).
[50]
Mary J Lindstrom and Douglas M Bates. 1988. Newton---Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. J. Amer. Statist. Assoc. 83, 404 (1988), 1014--1022.
[51]
Herbert Warren Marsh. 2006. Self-concept theory, measurement and research into practice: The role of self-concept in educational psychology. British Psychological Society London.
[52]
Stephen M Mattingly, Julie M Gregg, Pino Audia, Ayse Elvan Bayraktaroglu, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Sidney K D'Mello, Anind K Dey, et al. 2019. The Tesserae project: Large-scale, longitudinal, in situ, multimodal sensing of information workers. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1--8.
[53]
Graziella Pagliarulo McCarron and Karen Kurotsuchi Inkelas. 2006. The gap between educational aspirations and attainment for first-generation college students and the role of parental involvement. Journal of College Student Development 47, 5 (2006), 534--549.
[54]
Deanna LH McFadden. 2016. Health and academic success: A look at the challenges of first-generation community college students. Journal of the American Association of Nurse Practitioners 28, 4 (2016), 227--232.
[55]
Abhinav Mehrotra and Mirco Musolesi. 2017. Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring. (2017).
[56]
Sanjay S Mehta, John J Newbold, and Matthew A O'Rourke. 2011. Why do first-generation students fail? College Student Journal 45, 1 (2011), 20--36.
[57]
Microsoft 2020. AppCenter platform. https://appcenter.ms/.
[58]
Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino Audia, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K Dey, et al. 2019. Differentiating higher and lower job performers in the workplace using mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 2 (2019), 1--24.
[59]
Sean P Mullen, John F Adamek, Madhura Phansikar, Brent Roberts, and Christopher Larrison. 2020. A Path Analysis of the Role of First-Generation Status and Engagement in Social Interaction, Physical Activity, and Therapy in Satisfaction with Life among College Students. (2020).
[60]
Subigya Nepal, Gonzalo J. Martinez, Shayan Mirjafari, Stephen Mattingly, Vedant Das Swain, Aaron Striegel, Pino G. Audia, and Andrew T. Campbell. 2021. Assessing the Impact of Commuting on Workplace Performance Using Mobile Sensing. IEEE Pervasive Computing 20, 4 (Oct. 2021), 52--60. https://doi.org/10.1109/mprv.2021.3112399
[61]
Subigya Nepal, Shayan Mirjafari, Gonzalo J. Martinez, Pino Audia, Aaron Striegel, and Andrew T. Campbell. 2020. Detecting Job Promotion in Information Workers Using Mobile Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 3, Article 113 (sep 2020), 28 pages. https://doi.org/10.1145/3414118
[62]
Subigya Nepal, Weichen Wang, Vlado Vojdanovski, Jeremy F Huckins, Alex daSilva, Meghan Meyer, and Andrew Campbell. 2022. COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing. In CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI '22). Association for Computing Machinery, New York, NY, USA, Article 42, 19 pages. https://doi.org/10.1145/3491102.3502043
[63]
Mikio Obuchi, Jeremy F Huckins, Weichen Wang, Alex daSilva, Courtney Rogers, Eilis Murphy, Elin Hedlund, Paul Holtzheimer, Shayan Mirjafari, and Andrew Campbell. 2020. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 1 (2020), 1--22.
[64]
Venet Osmani, Alban Maxhuni, Agnes Grünerbl, Paul Lukowicz, Christian Haring, and Oscar Mayora. 2013. Monitoring activity of patients with bipolar disorder using smart phones. In Proceedings of International Conference on Advances in Mobile Computing & Multimedia. 85--92.
[65]
Skyler Place, Danielle Blanch-Hartigan, Channah Rubin, Cristina Gorrostieta, Caroline Mead, John Kane, Brian P Marx, Joshua Feast, Thilo Deckersbach, Andrew Nierenberg, et al. 2017. Behavioral indicators on a mobile sensing platform predict clinically validated psychiatric symptoms of mood and anxiety disorders. Journal of medical Internet research 19, 3 (2017), e75.
[66]
Lucila Ramos-Sánchez and Laura Nichols. 2007. Self-efficacy of first-generation and non-first-generation college students: The relationship with academic performance and college adjustment. Journal of college counseling 10, 1 (2007), 6--18.
[67]
Gerald M Reid, Melissa K Holt, Chelsey E Bowman, Dorothy L Espelage, and Jennifer Greif Green. 2016. Perceived social support and mental health among first-year college students with histories of bullying victimization. Journal of Child and Family Studies 25, 11 (2016), 3331--3341.
[68]
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016).
[69]
Sylvia Ruiz, Jessica Sharkness, Kimberly Kelly, Linda DeAngelo, and John Pryor. 2010. Findings from the 2009 administration of the Your First College Year (YFCY): National aggregates. Los Angeles: Higher Education Research Institute at the University of California Los Angeles (2010).
[70]
Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, and David C Mohr. 2016. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4 (2016), e2537.
[71]
Sohrab Saeb, Mi Zhang, Christopher J Karr, Stephen M Schueller, Marya E Corden, Konrad P Kording, and David C Mohr. 2015. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. Journal of medical Internet research 17, 7 (2015).
[72]
Koustuv Saha, Manikanta D Reddy, Vedant das Swain, Julie M Gregg, Ted Grover, Suwen Lin, Gonzalo J Martinez, Stephen M Mattingly, Shayan Mirjafari, Raghu Mulukutla, et al. 2019. Imputing missing social media data stream in multisensor studies of human behavior. In 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 178--184.
[73]
A. Sano, A.J. Phillips, A. Z. Yu, A. W. McHill, S. Taylor, N. Jaques, C. A. Czeisler, E. B. Klerman, and R. W. Picard. 2015. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN). 1--6. https://doi.org/10.1109/BSN.2015.7299420
[74]
Mike Schuster and Kuldip K Paliwal. 1997. Bidirectional recurrent neural networks. IEEE transactions on Signal Processing 45, 11 (1997), 2673--2681.
[75]
Sandra Servia-Rodríguez, Kiran K Rachuri, Cecilia Mascolo, Peter J Rentfrow, Neal Lathia, and Gillian M Sandstrom. 2017. Mobile sensing at the service of mental well-being: a large-scale longitudinal study. In Proceedings of the 26th International Conference on World Wide Web. 103--112.
[76]
Jessica Sharkness and Linda DeAngelo. 2011. Measuring student involvement: A comparison of classical test theory and item response theory in the construction of scales from student surveys. Research in Higher Education 52, 5 (2011), 480--507.
[77]
Giovanni Sogari, Catalina Velez-Argumedo, Miguel I Gómez, and Cristina Mora. 2018. College students and eating habits: A study using an ecological model for healthy behavior. Nutrients 10, 12 (2018), 1823.
[78]
Patrick T Terenzini, Leonard Springer, Patricia M Yaeger, Ernest T Pascarella, and Amaury Nora. 1996. First-generation college students: Characteristics, experiences, and cognitive development. Research in Higher education 37, 1 (1996), 1--22.
[79]
Jordan Thibodeaux, Aaron Deutsch, Anastasia Kitsantas, and Adam Winsler. 2017. First-Year College Students' Time Use: Relations With Self-Regulation and GPA. Journal of Advanced Academics 28, 1 (Feb. 2017), 5--27. https://doi.org/10.1177/1932202X16676860
[80]
Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267--288.
[81]
Vincent W-S Tseng, Akane Sano, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Marta Hauser, John M Kane, Emily A Scherer, Rui Wang, Weichen Wang, et al. 2020. Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. Scientific reports 10, 1 (2020), 1--17.
[82]
John Von Neumann, RH Kent, HR Bellinson, and BI t Hart. 1941. The mean square successive difference. The Annals of Mathematical Statistics 12, 2 (1941), 153--162.
[83]
MaryBeth Walpole. 2003. Socioeconomic status and college: How SES affects college experiences and outcomes. The review of higher education 27, 1 (2003), 45--73.
[84]
Rui Wang, Fanglin Chen, Zhenyu Chen, Tianxing Li, Gabriella Harari, Stefanie Tignor, Xia Zhou, Dror Ben-Zeev, and Andrew T Campbell. 2014. 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. ACM, 3--14.
[85]
Rui Wang, Gabriella Harari, Peilin Hao, Xia Zhou, and Andrew T. Campbell. 2015. 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). ACM, New York, NY, USA, 295--306. https://doi.org/10.1145/2750858.2804251 event-place: Osaka, Japan.
[86]
Rui Wang, Weichen Wang, Min SH Aung, Dror Ben-Zeev, Rachel Brian, Andrew T Campbell, Tanzeem Choudhury, Marta Hauser, John Kane, Emily A Scherer, et al. 2017. Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 110.
[87]
Rui Wang, Weichen Wang, Alex daSilva, Jeremy F. Huckins, William M. Kelley, Todd F. Heatherton, and Andrew T. Campbell. 2018. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (March 2018), 1--26. https://doi.org/10.1145/3191775
[88]
Weichen Wang, Gabriella M Harari, Rui Wang, Sandrine R Müller, Shayan Mirjafari, Kizito Masaba, and Andrew T Campbell. 2018. Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 141.
[89]
Weichen Wang, Shayan Mirjafari, Gabriella Harari, Dror Ben-Zeev, Rachel Brian, Tanzeem Choudhury, Marta Hauser, John Kane, Kizito Masaba, Subigya Nepal, et al. 2020. Social sensing: assessing social functioning of patients living with schizophrenia using mobile phone sensing. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--15.
[90]
Weichen Wang, Jialing Wu, Subigya Kumar Nepal, Alex daSilva, Elin Hedlund, Eilis Murphy, Courtney Rogers, and Jeremy F. Huckins. 2021. On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic Based on Pre-COVTD-19 On-Campus Colocation. In Proceedings of the 2021 International Conference on Multimodal Interaction (Montréal, QC, Canada) (ICMI '21). Association for Computing Machinery, New York, NY, USA, 425--434. https://doi.org/10.1145/3462244.3479888
[91]
Karl R White. 1982. The relation between socioeconomic status and academic achievement. Psychological bulletin 91, 3 (1982), 461.
[92]
Xuhai Xu, Prerna Chikersal, Afsaneh Doryab, Daniella K Villalba, Janine M Dutcher, Michael J Tumminia, Tim Althoff, Sheldon Cohen, Kasey G Creswell, J David Creswell, et al. 2019. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (2019), 1--33.
[93]
Dollean C York-Anderson and Sharon L Bowman. 1991. Assessing the college knowledge of first-generation and second-generation college students. Journal of College Student Development (1991).
[94]
Han Yu and Akane Sano. 2020. Passive Sensor Data Based Future Mood, Health, and Stress Prediction: User Adaptation Using Deep Learning. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 5884--5887.
[95]
Xiao Zhang, Wenzhong Li, Xu Chen, and Sanglu Lu. 2018. MoodExplorer: Towards Compound Emotion Detection via Smartphone Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (Jan. 2018), 1--30. https://doi.org/10.1145/3161414

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      cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
      Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 6, Issue 2
      July 2022
      1551 pages
      EISSN:2474-9567
      DOI:10.1145/3547347
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      Published: 07 July 2022
      Published in IMWUT Volume 6, Issue 2

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      Author Tags

      1. first year
      2. first-generation students
      3. mental health
      4. mobile sensing

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