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Depression Management as Lifestyle Management: Exploring Existing Practices and Perceptions Among College Students

  • Jordan DodsonEmail author
  • Naika Saint Preux
  • Jenni Thang
  • Elizabeth V. Eikey
Conference paper
  • 201 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12051)

Abstract

Research is limited on college students’ existing approaches to managing depression, which are important to understand before designing and recommending non-digital and digital tools. We conducted a mixed methods survey with 109 college students about their awareness of and interest in non-digital and digital tools, what tools they use, how they use them, and their perceived effectiveness. In general, students are aware of and interested in both non-digital and digital tools. Therefore, we cannot discount the utility of both non-digital and digital tools, even among high technology users. We found 78 participants use non-digital tools, such as paper, art, and checklists, and 80 participants reported using digital tools, such as social media, texts, and YouTube/vlogging. From students’ perspectives, depression management is lifestyle management. Thus, they often use a combination of tools for connection and support, catharsis and outlet, keeping busy and distraction, organization and planning, and emotion and thought analysis and regulation, and they perceive these tools to be at least somewhat effective in managing their depression. This research emphasizes the need to understand current practices and perceptions and can be used as a foundation for other researchers, clinicians, and educators as they continue to find ways to support college students with depression.

Keywords

Depression Self-monitoring Non-digital and digital tools College students Mental health 

Notes

Acknowledgements

We would like to thank iSchool Inclusion Institute (i3) Director Dr. Kayla Booth, i3 Assistant Director and our research advisor Dr. Elizabeth Eikey, and Michael Depew, as well as the 2017, 2018, and 2019 cohorts. This work was supported by the National Center for Research Resources, the National Center for Advancing Translational Sciences, and the NIH (UL1 TR001414). It is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

References

  1. 1.
    Aarhus, R., Ballegaard, S.A.: Negotiating boundaries: managing disease at home. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1223–1232. ACM, New York (2010).  https://doi.org/10.1145/1753326.1753509
  2. 2.
    Addington, D., et al.: Depression in people with first-episode schizophrenia. Br. J. Psychiatry 172(S33), 90–92 (1998).  https://doi.org/10.1192/s0007125000297729CrossRefGoogle Scholar
  3. 3.
    Anderson, K., et al.: Mobile health apps to facilitate self-care: a qualitative study of user experiences. PLoS ONE 11(5), e0156164 (2016).  https://doi.org/10.1371/journal.pone.0156164CrossRefGoogle Scholar
  4. 4.
    Arean, P.A., et al.: The use and effectiveness of mobile apps for depression: Results from a fully remote clinical trial. J. Med. Internet Res. 18, 12 (2016).  https://doi.org/10.2196/jmir.6482CrossRefGoogle Scholar
  5. 5.
    Bayliss, E.A., et al.: Understanding the context of health for persons with multiple chronic conditions: moving from what is the matter to what matters. Ann. Fam. Med. 12(3), 260–269 (2014)CrossRefGoogle Scholar
  6. 6.
    Beck, A.T., et al.: Beck Depression Inventory-IIGoogle Scholar
  7. 7.
    Beigel, A., Murphy, D.L.: Unipolar and bipolar affective illness: differences in clinical characteristics accompanying depression. Arch. Gen. Psychiatry 24(3), 215–220 (1971).  https://doi.org/10.1001/archpsyc.1971.01750090021003CrossRefGoogle Scholar
  8. 8.
    Corbin, J., Strauss, A.: Managing chronic illness at home: three lines of work. Qual. Sociol. 8(3), 224–247 (1985).  https://doi.org/10.1007/BF00989485CrossRefGoogle Scholar
  9. 9.
    Dodson, J., et al.: Investigating health self-management among different generation immigrant college students with depression. In: Taylor, N.G., Christian-Lamb, C., Martin, M.H., Nardi, B. (eds.) iConference 2019. LNCS, vol. 11420, pp. 213–221. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-15742-5_20CrossRefGoogle Scholar
  10. 10.
    Doherty, G., et al.: Engagement with online mental health interventions: an exploratory clinical study of a treatment for depression. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1421–1430. ACM, New York (2012).  https://doi.org/10.1145/2207676.2208602
  11. 11.
    Dozois, D.J.A., et al.: A psychometric evaluation of the Beck Depression Inventory–II. Psychol. Assess. 10(2), 83–89 (1998).  https://doi.org/10.1037/1040-3590.10.2.83CrossRefGoogle Scholar
  12. 12.
    Eikey, E.V., Chen, Y., Zheng, K.: Do recovery apps even exist? Why college women with eating disorders use (but not recommend) diet and fitness apps over recovery apps. In: Taylor, N.G., Christian-Lamb, C., Martin, M.H., Nardi, B. (eds.) iConference 2019. LNCS, vol. 11420, pp. 727–740. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-15742-5_69CrossRefGoogle Scholar
  13. 13.
    Eikey, E.V., et al.: The use of general health apps among users with specific conditions: why college women with disordered eating adopt food diary apps. In: American Medical Informatics Association (AMIA) Symposium, San Francisco, CA (2018)Google Scholar
  14. 14.
    Eikey, E.V., Reddy, M.C.: “It’s definitely been a journey”: a qualitative study on how women with eating disorders use weight loss apps. In: ACM CHI Conference on Human Factors in Computing Systems (CHI), pp. 1–13. ACM, Denver (2017).  https://doi.org/10.1145/3025453.3025591
  15. 15.
    Epstein, R.M., et al.: “I didn’t know what was wrong:” how people with undiagnosed depression recognize, name and explain their distress. J. Gen. Intern. Med. 25(9), 954–961 (2010).  https://doi.org/10.1007/s11606-010-1367-0CrossRefGoogle Scholar
  16. 16.
    Firth, J., et al.: The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials 16(3), 287–298 (2017).  https://doi.org/10.1002/wps.20472CrossRefGoogle Scholar
  17. 17.
    Fleming, T., et al.: Beyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. J. Med. Internet Res. 20(6), e199 (2018).  https://doi.org/10.2196/jmir.9275CrossRefGoogle Scholar
  18. 18.
    Forty, L., et al.: Clinical differences between bipolar and unipolar depression. Br. J. Psychiatry 192(5), 388–389 (2008).  https://doi.org/10.1192/bjp.bp.107.045294CrossRefGoogle Scholar
  19. 19.
    Fox, S.: Health topics. Pew Research CenterGoogle Scholar
  20. 20.
    Häfner, H., et al.: Schizophrenia and depression: challenging the paradigm of two separate diseases - a controlled study of schizophrenia, depression and healthy controls. Schizophrenia Res. 77(1), 11–24 (2005).  https://doi.org/10.1016/j.schres.2005.01.004CrossRefGoogle Scholar
  21. 21.
    Huguet, A., et al.: A systematic review of cognitive behavioral therapy and behavioral activation apps for depression. PLoS ONE 11(5), e0154248 (2016).  https://doi.org/10.1371/journal.pone.0154248CrossRefGoogle Scholar
  22. 22.
    Hunt, J., Eisenberg, D.: Mental health problems and help-seeking behavior among college students. J. Adolesc. Health 46(1), 3–10 (2010).  https://doi.org/10.1016/j.jadohealth.2009.08.008CrossRefGoogle Scholar
  23. 23.
    Ibrahim, A.K., et al.: A systematic review of studies of depression prevalence in university students. J. Psychiatr. Res. 47(3), 391–400 (2013).  https://doi.org/10.1016/j.jpsychires.2012.11.015CrossRefGoogle Scholar
  24. 24.
    Kitzrow, M.A.: The mental health needs of today’s college students: challenges and recommendations (2003)Google Scholar
  25. 25.
    Krebs, P., Duncan, D.T.: Health app use among US mobile phone owners: a national survey. JMIR mHealth uHealth 3(4), e101 (2015).  https://doi.org/10.2196/mhealth.4924CrossRefGoogle Scholar
  26. 26.
    Lim, C.Y., et al.: Facilitating self-reflection about values and self-care among individuals with chronic conditions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (2019).  https://doi.org/10.1145/3290605.3300885
  27. 27.
    Løventoft, P.K., et al.: Designing daybuilder: an experimental app to support people with depression. In: Proceedings of the 12th Participatory Design Conference: Exploratory Papers, Workshop Descriptions, Industry Cases, vol. 2, pp. 1–4. ACM, New York (2012).  https://doi.org/10.1145/2348144.2348146
  28. 28.
    Lyubomirsky, S., et al.: Dysphoric rumination impairs concentration on academic tasks. Cogn. Ther. Res. 27(3), 309–330 (2003).  https://doi.org/10.1023/A:1023918517378CrossRefGoogle Scholar
  29. 29.
    Mohr, D.C., et al.: Three problems with current digital mental health research… and three things we can do about them. Psychiatr. Serv. 68(5), 427–429 (2017).  https://doi.org/10.1176/appi.ps.201600541CrossRefGoogle Scholar
  30. 30.
    Murnane, E.L., et al.: Personal informatics in interpersonal contexts: towards the design of technology that supports the social ecologies of long-term mental health management. In: Computer Supported Cooperative Work and Social Computing (CSCW) (2018).  https://doi.org/10.1145/3274396
  31. 31.
    National Institute of Mental Health: Depression. https://www.nimh.nih.gov/health/topics/depression/index.shtml#part_145397. Accessed 12 Sept 2019
  32. 32.
    Rubanovich, C.K., et al.: Health app use among individuals with symptoms of depression and anxiety: a survey study with thematic coding. JMIR Ment. Health 4(2), e22 (2017).  https://doi.org/10.2196/mental.7603CrossRefGoogle Scholar
  33. 33.
    Shen, N., et al.: Finding a depression app: a review and content analysis of the depression app marketplace. JMIR mHealth uHealth 3(1), e16 (2015).  https://doi.org/10.2196/mhealth.3713CrossRefGoogle Scholar
  34. 34.
    Smith, A., Anderson, M.: Social media use 2018: demographics and statistics. Pew Research Center (2018)Google Scholar
  35. 35.
    Tandoc, E.C., et al.: Facebook use, envy, and depression among college students: Is facebooking depressing? Comput. Hum. Behav. 43, 139–146 (2015).  https://doi.org/10.1016/J.CHB.2014.10.053CrossRefGoogle Scholar
  36. 36.
    Thomas, D.R.: A general inductive approach for qualitative data analysis (2003)Google Scholar
  37. 37.
    Torous, J., Powell, A.C.: Current research and trends in the use of smartphone applications for mood disorders. Internet Interv. 2(2), 169–173 (2015).  https://doi.org/10.1016/J.INVENT.2015.03.002CrossRefGoogle Scholar
  38. 38.
    Vidourek, R.A., et al.: Students’ benefits and barriers to mental health help-seeking. Health Psychol. Behav. Med. 2(1), 1009–1022 (2014).  https://doi.org/10.1080/21642850.2014.963586CrossRefGoogle Scholar
  39. 39.
    Vogel, E.A., et al.: Social comparison, social media, and self-esteem. Psychol. Popul. Media Cult. 3(4), 206–222 (2014).  https://doi.org/10.1037/ppm0000047CrossRefGoogle Scholar
  40. 40.
    Vredenburg, K., et al.: Depression in college students: personality and experiential factors. J. Couns. Psychol. 35(4), 419–425 (1988).  https://doi.org/10.1037/0022-0167.35.4.419CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jordan Dodson
    • 1
    • 5
    Email author
  • Naika Saint Preux
    • 2
    • 5
  • Jenni Thang
    • 3
    • 5
  • Elizabeth V. Eikey
    • 4
    • 5
  1. 1.The University of North Carolina at Chapel HillChapel HillUSA
  2. 2.The College of WestchesterWhite PlainsUSA
  3. 3.Indiana University BloomingtonBloomingtonUSA
  4. 4.University of CaliforniaSan DiegoUSA
  5. 5.The iSchool Inclusion Institute (i3)PittsburghUSA

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