Skip to Main Content

Clinical Psychology and Special Education
2019. Vol. 8, no. 3, 101–124
doi:10.17759/cpse.2019080306
ISSN: 2304-0394 (online)

Methods for Preventing Depression on Digital Platforms and in Social Media

1448

M.M. Danina, N.V. Kiselnikova, E.A. Kuminskaya, E.V. Lavrova, P.A. Greskova

PDF (RUS)
Online translation in english
GS
Information
in Google Scholar

Abstract

The prevalence of depression among the population estimated at 8-12%. The World Health Organization admits that the existing help system is not sufficiently successful in dealing with depression, and gives priority to online methods – accessible and anonymous. So it can be used by a large number of people and can help to overcome the problem of stigmatization of people with depression. In the article current trends in using online diagnostics tools (mobile applications and gadgets) are discussed and detection of groups with depression risk in social media digital footprints are analyzed. The prospect of research consists in studying the mechanisms and identifying specific components of programs related to the preventive effect, as well as the possibilities of using online methods to work with other mental disorders.

General Information

Keywords: depression, online prevention, digital trace analysis, mobile applications, risk groups, social media

Journal rubric: Applied Research

Article type: scientific article

DOI: https://doi.org/10.17759/cpse.2019080306

For citation: Danina M.M., Kiselnikova N.V., Kuminskaya E.A., Lavrova E.V., Greskova P.A. Methods for Preventing Depression on Digital Platforms and in Social Media [Elektronnyi resurs]. Klinicheskaia i spetsial'naia psikhologiia = Clinical Psychology and Special Education, 2019. Vol. 8, no. 3, pp. 101–124. DOI: 10.17759/cpse.2019080306. (In Russ., аbstr. in Engl.)

References

  1. Vachkova S.N. Osobennosti setevykh form kommunikatsii sovremennykh shkol'nikov [Features of network forms of communication of modern schoolchildren]. Sotsial'naya psikhologiya i obshchestvo [Social Psychology and Society], 2014, vol. 5, no. 4, pp. 135–144. (In Russ., abstr. in Engl.).
  2. Ivanov V.G., Lazareva E.Yu., Nikolaev E.L. Primenenie sovremennykh informatsionno-kommunikatsionnykh tekhnologii v psikhoterapevticheskoi i psikhologicheskoi praktike (obzor zarubezhnykh issledovanii) [The use of modern information and communication technologies in psychotherapeutic and psychological practice (review of foreign studies)]. Problemy sovremennogo pedagogicheskogo obrazovaniya [Problems of Modern Pedagogical Education], 2017, vol. 57, no. 6, pp. 321–329. (In Russ., abstr. in Engl.).
  3. Menovshchikov V.Yu. Psikhologicheskaya pomoshch' v seti Internet. [Electronic resource] [Psychological help on the Internet]. Moscow, 2007. 178 p. URL: http://flogiston.ru/articles/netpsy/psyhelp_in_internet (Accessed 31.10.2019). (In Russ.).
  4. Sovkov S.V. Perspektivy i opyt ispol'zovaniya internet-tekhnologii v lechenii poslerodovoi depressii [Prospects and experience in the use of Internet technologies in the treatment of postpartum depression]. Meditsinskaya nauka i obrazovanie Urala [Medical Science and Education of the Urals], 2013, vol. 14, no. 3, pp. 168–170. (In Russ.).
  5. Soldatova G.U. Tsifrovaya sotsializatsiya v kul'turno-istoricheskoi paradigme: izmenyayushchiisya rebenok v izmenyayushchemsya mire [Digital socialization in the cultural-historical paradigm: a changing child in a changing world]. Sotsial'naya psikhologiya i obshchestvo [Social Psychology and Society], 2018, vol. 9, no. 3. pp. 71–80. doi:10.17759/sps.2018090308. (In Russ., abstr. in Engl.).
  6. Alhanai T., Ghassemi M., Glass J. Detecting depression with audio/text sequence modeling of interviews. Procedia Interspeech, 2018, vol. 2522, pp. 1716–1720. doi:10.21437/Interspeech.2018-2522
  7. Andersson G., Bergström J., Holländare F., et al. Internet-based self-help for depression: randomised controlled trial. The British Journal of Psychiatry, 2005, vol. 187, no. 5, pp. 456–461. doi:10.1192/bjp.187.5.456
  8. Anguera J.A., Gunning F.M., Areán P.A. Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof‐of‐concept randomized trial. Depression and Anxiety, 2017, vol. 34, no. 6, pp. 508–517. doi: 10.1002/da.22588
  9. Arean P.A., Hallgren K.A., Jordan J.T., et al. The Use and Effectiveness of Mobile Apps for Depression: Results from a Fully Remote Clinical Trial. Journal of Medical Internet Research, 2016, vol. 18, no. 12, p. 330. doi: 10.2196/jmir.6482
  10. Barnes C., Harvey R., Mitchell P., et al. Evaluation of an online relapse prevention program for bipolar disorder: an overview of the aims and methodology of a randomized controlled trial. Disease Management & Health Outcomes, 2007, vol. 15, no. 4, pp. 215–224. doi: 10.1037/prj0000270
  11. Barrera A.Z., Wickham R.E., Muñoz R.F. Online prevention of postpartum depression for Spanish- and English-speaking pregnant women: A pilot randomized controlled trial. Internet Interventions, 2015, vol. 2, no. 2, pp. 257–265. doi: 10.1016/j.invent.2015.06.002
  12. Birmaher B., Brent D., Laurel C., et al. Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): a replication study. Journal of the American Academy of Child & Adolescent Psychiatry, 1999, vol. 38, no. 10, pp. 1230–1236. doi: 10.1097/00004583-199910000-00011
  13. Brugha T.S., Wheatley S., Taub N.A., et al. Pragmatic randomized trial of antenatal intervention to prevent postnatal depression by reducing psychosocial risk
    factors. Psychological Medicine, 2000, vol. 30, no. 6, pp. 1273–1281. doi: 10.1017/S0033291799002937
  14. Buntrock C., Ebert D., Lehr D., et al. Effect of a Web-Based Guided Self-help Intervention for Prevention of Major Depression in Adults with Subthreshold Depression:
    A Randomized Clinical Trial. JAMA, 2016, vol. 315, no. 17, pp. 1854. doi: 10.1001/jama.2016.4326
  15. Cheng S.K., Dizon J. Computerised cognitive behavioural therapy for insomnia:
    a systematic review and meta-analysis. Psychotherapy and Psychosomatics, 2012, vol. 81, no. 4, pp. 206–216. doi: 10.1159/000335379.
  16. Choudhury M. de, Gamon M., Counts S., et al. Predicting Depression via Social Media. In Cohn A. (ed.), International AAAI Conference on Weblogs and Social Media, 2013, pp. 128–137.
  17. D'Alfonso S., Santesteban-Echarri O., Rice S., et al. Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health [Electronic Resource]. Frontiers in Psychology, 2017, no. 8, p. 796. URL: https://www.frontiersin.org/articles/10.3389 /fpsyg.2017.00796/full (Accessed 31.10.2019).
  18. Dandeneau S.D., Baldwin M.W., Baccus J.R., et al. Cutting stress off at the pass: reducing vigilance and responsiveness to social threat by manipulating attention. Journal of Personality and Social Psychology, 2007, vol. 93, no. 4, p. 651. doi: 10.1037/0022-3514.93.4.651
  19. Dao B., Nguyen T., Venkatesh S., et al. Nonparametric discovery of online mental health-related communities, Data Science and Advanced Analytics (DSAA). In E. Gaussier (eds.), IEEE International Conference, 2015, pp. 1–10. doi: 10.1109/DSAA.2015.7344841
  20. Ebert D., Lehr D., Baumeister H., et al. GET.ON Mood Enhancer: efficacy of Internet-based guided self-help compared to psychoeducation for depression: an investigator-blinded randomised controlled trial [Electronic Resource]. Trials, 2014, vol. 15, no. 1,
    p. 39. URL: https://trialsjournal.biomedcentral.com/articles/10.1186/1745-6215-15-39 (Accessed 31.10.2019)
  21. Farhan A.A., Yue C., Morillo R., et al. Behavior vs. Introspection: Refining prediction of clinical depression via smartphone sensing data. IEEE CHASE, 2016.
  22. Giosan C., Mogoaşe C., Cobeanu O., et al. Using a smartphone app to reduce cognitive vulnerability and mild depressive symptoms: study protocol of an exploratory randomized controlled trial. Trials, 2016, vol. 17, no. 1, p. 609. doi: 10.1186/s13063-016-1740-3.
  23. Haque A., Guo M., Miner A.S., et al. Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions [Electronic source]. 2018. URL: arXiv preprint arXiv:1811.08592 (Accessed 31.10.2019).
  24. Holländare F., Anthony S., Randestad M., et al. Two-year outcome of internet-based relapse prevention for partially remitted depression. Behaviour Research and Therapy, 2013, vol. 51, no 11, pp. 719–722. doi: 10.1016/j.brat.2013.08.002
  25. Kessler R.C., Berglund P., Demler O., et al. The Epidemiology of Major Depressive Disorder. JAMA, 2003, vol. 289, no. 23, p. 3095. doi: 10.1001/jama.289.23.3095
  26. Kovacs M., Garrison B. Hopelessness and eventual suicide: a 10-year prospective study of patients hospitalized with suicidal ideation. American Journal of Psychiatry, 1985, vol. 1, no. 42, pp. 559–563. doi: 10.1176/ajp.142.5.559
  27. Kuehner C. Gender differences in unipolar depression: an update of epidemiological findings and possible explanations. Acta Psychiatrica Scandinavica, 2003, vol. 108, no. 3, pp. 163–174. doi: 10.1034/j.1600-0447.2003.00204.x
  28. Liu P., Tov W., Kosinski M., et al. Do Facebook Status Updates Reflect Subjective Well-Being? Cyberpsychology, Behavior, and Social Networking, 2015, vol. 18, no 7,
    pp. 373–379. doi: 10.1089/cyber.2015.0022
  29. Ly K., Carlbring P., Andersson G. Behavioral activation-based guided self- help treatment administered through a smartphone application: study protocol for a randomized controlled trial [Electronic Resource]. Trials, 2012, no. 13, p. 62. doi: 10.1186/1745-6215-13-62 (Accessed 31.10.2019).
  30. Mackinnon A., Griffiths K. M., Christensen H. Comparative randomized trial of online cognitive–behavioral therapy and an information website for depression: 12-month outcomes. The British Journal of Psychiatry, 2008, vol. 192, no. 2, pp. 130–134.
    doi: 10.1192/bjp.bp.106.032078
  31. Marrs R.W. A meta‐analysis of bibliotherapy studies. American Journal of Community Psychology, 1995, vol. 23, no. 6, pp. 843–870. doi: 10.1007/BF02507018
  32. Meyer B., Berger T.F., Caspar C., et al. Effectiveness of a Novel Integrative Online Treatment for Depression (Deprexis): Randomized Controlled Trial [Electronic Resource]. Journal of Medical Internet Research, 2009, vol. 11, no. 2, p. 15. URL: https://www.jmir.org /2009/2/e15/ (Accessed 31.10.2019).
  33. Mohr D.C., Duffecy J., Jin L., et al. Multimodal e-mental health treatment for depression: a feasibility trial [Electronic Resource]. Journal of Medical Internet Research, 2010, vol. 12, no. 5, pp. 48. URL: https://mhealth.jmir.org/2019/1/e10948/ (Accessed 31.10.2019).
  34. Mowery D.L., Smith H., Cheney T., et al. Towards automatically classifying depressive symptoms from Twitter data for population health. In Nissim M. (ed.), Proceedings of the Workshop on Computational Modeling of People’s Opinions,
    Personality, and Emotions in Social Media (PEOPLES),     2016, pp. 182–191.
    doi: 8. 10.5210/ojphi.v8i1.6561
  35. Muñoz R.F., Cuijpers P., Smit F., et al. Prevention of major depression. Annual Review of Clinical Psychology, 2010, vol. 6, no. 1, pp. 181–212. doi: 10.1146/annurev-clinpsy-033109-132040
  36. Park J., Cha M., Kim H., et al. Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis. In Breslin J. (ed.), The 6th International AAAI Conference On Weblogs and Social Media, (ICWSM 2012). Trinity College in Dublin, Ireland, June 4–8, 2012,
    pp. 282–289.
  37. Pecina J., North F., Williams M. D., et al. Use of an on-line patient portal in
    a depression collaborative care management program. Journal of Affective Disorders, 2017, vol. 208, pp. 1–5. doi: 10.1016/j.jad.2016.08.034
  38. Ranney M.L., Freeman J.R., Connell G., et al. A Depression Prevention Intervention for Adolescents in the Emergency Department. Journal of Adolescent Health, 2016, vol. 59, no. 4, pp. 401–410. doi: 10.1016/j.jadohealth.2016.04.008
  39. Reece A.G., Danforth C.M. Instagram photos reveal predictive markers of depression [Electronic Resource]. EPJ Data Science, 2017, vol. 6, p. 15.
    URL: https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0110-z (Accessed 31.10.2019).
  40. Rice F., Rawal A., Riglin L., et al. Examining reward-seeking, negative self-beliefs and over-general autobiographical memory as mechanisms of change in classroom prevention programs for adolescent depression. Medical Research Council, 2015,
    pp. 320–327. doi: 10.1016/j.jad.2015.07.019
  41. Rice S.M., Goodall J., Hetrick S.E., et al. Online and Social Networking Interventions for the Treatment of Depression in Young People: A Systematic Review [Electronic Resource]. Journal of Medical Internet Research, 2014, vol. 16, no. 9, pp. 206.
    URL: https://www.jmir.org/2014/9/e206/ (Accessed 31.10.2019).
  42. Schwartz H.A., Sap M., Kern M.L., et al. Predicting individual well-being through the language of social media. Pacific Symposium on Biocomputing, 2016, vol. 21, pp. 516–527.
  43. Seabrook E.M., Kern M.L., Fulcher B.D., et al. Predicting depression from language-based emotion dynamics: longitudinal analysis of Facebook and Twitter status updates [Electronic Resource]. Journal of Medical Internet Research, 2018, vol. 20, no. 5, p. 168.
    URL: https://www.jmir.org/2018/5/e168/ (Accessed 31.10.2019).
  44. Tasnim M., Shahriyar R., Nahar N., et al. Intelligent Depression Detection and Support System: Statistical Analysis, Psychological Review and Design Implication [Electronic Resource]. IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 2016, pp. 1–6. URL: https://ieeexplore. ieee.org/xpl/conhome/7701172/proceeding (Accessed 31.10.2019).
  45. UN health agency reports depression now «leading cause of disability worldwide» [Electronic Resource]. UN News, 23 February 2017, Retrieved 27 June 2019. URL: https://news.un.org/en/story/2017/02/552062-un-health-agency-reports-depression-now-leading-cause-disability-worldwide (Accessed 31.10.2019).
  46. Välimäki M., Anttila K., Anttila M., et al. Web-Based Interventions Supporting Adolescents and Young People with Depressive Symptoms: Systematic Review and Meta-Analysis [Electronic Resource]. Journal of Medical Internet Research, 2017, vol, 5, no. 12,
    p. 180. URL: https://mhealth.jmir.org/2017/12/e180 (Accessed 31.10.2019).
  47. Van Zoonen K. Buntrock C., Ebert D.D., et al. Preventing the onset of major depressive disorder: a meta-analytic review of psychological interventions. International Journal of Epidemiology, 2014, vol. 43, no. 2, pp. 318–329. doi: 10.1093/ije/dyt175.
  48. Voogd E.L. de, Wiers R.W., Prins P.J., et al. Online attentional bias modification training targeting anxiety and depression in unselected adolescents: Short- and long-term effects of a randomized controlled trial. Behaviour Research and Therapy, 2016, vol. 87,
    pp. 11–22. doi: 10.1016/j.brat.2016.08.018
  49. Wee J., Jang S., Lee J., et al. The influence of depression and personality on
    social networking. Computers in Human Behavior, 2017, vol. 74, pp. 45–52.
    doi: 10.1016/j.chb.2017.04.003
  50. Wittchen H.U., Müller N., Pfister H., et al. Häufigkeit und Versorgung von Depressionen. Ergebnisse des bundesweiten Gesundheitssurveys. Psychische Störungen Erscheinungsformen Fortschritte der Medizin, 2000, vol. 118, no. 1, pp. 1–41.
  51. Yates A., Cohan A., Goharian N. Depression and self-harm risk assessment in online forums [Electronic Resource]. arXiv preprint arXiv:1709.01848, 2017. doi: 10.18653/v1/D17-1322 (Accessed 31.10.2019).
  52. Zhu C., Li B., Li A., et al. Predicting Depression from Internet Behaviors by Time-frequency Features [Electronic Resource]. IEEE/WIC/ACM International Conference on Web Intelligence, October 13-16, 2016. Hilton Omaha, USA, 2016, pp. 383–390. doi:10.1109/WI.2016.0060 (Accessed 31.10.2019).

Information About the Authors

Maria M. Danina, PhD in Psychology, Senior researcher of the Laboratory of Counselling Psychology and Psychotherapy, Psychological Institute of Russian Academy of Education, Moscow, Russia, e-mail: mdanina@yandex.ru

Natalia V. Kiselnikova, PhD in Psychology, Assistant Professor, Head of Laboratory of Counseling Psychology and Psychotherapy, FBSSI «Psychological Institute of Russian Academy of Education», Moscow, Russia, ORCID: https://orcid.org/0000-0003-0710-4972, e-mail: nv_psy@mail.ru

Evgenia A. Kuminskaya, Researcher, Laboratory of Counselling Psychology and Psychotherapy, Psychological Institute of Russian Academy of Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-5650-1096, e-mail: evgenia.kuminskaya@gmail.com

Elena V. Lavrova, PhD in Psychology, Senior Researcher of the Laboratory of Counselling Psychology and Psychotherapy, Psychological Institute of Russian Academy of Education, Moscow, Russia, ORCID: https://orcid.org/0000-0002-7144-4704, e-mail: may_day@list.ru

Polina A. Greskova, Student of the Faculty of Psychology, Saint-Petersburg State University, St.Petersburg, Russia, ORCID: https://orcid.org/0000-0002-5490-780X, e-mail: polina.greskova@gmail.com

Metrics

Views

Total: 1672
Previous month: 14
Current month: 18

Downloads

Total: 1448
Previous month: 17
Current month: 14