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
Abstract
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
- 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.).
- 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.).
- 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.).
- 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.).
- 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.).
- 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
- 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
- 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
- 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
- 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
- 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
- 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
-
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 -
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 -
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. - 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.
- 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).
- 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
- 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
-
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) - Farhan A.A., Yue C., Morillo R., et al. Behavior vs. Introspection: Refining prediction of clinical depression via smartphone sensing data. IEEE CHASE, 2016.
- 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.
- 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).
- 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
- 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
- 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
- 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
-
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 - 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).
-
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 - 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
- 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).
- 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).
-
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 - 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
-
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. -
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 - 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
-
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). -
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 -
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). - 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.
-
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). - 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).
- 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).
-
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). - 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.
-
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 -
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 - 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.
- 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).
- 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
Metrics
Views
Total: 1643
Previous month: 13
Current month: 3
Downloads
Total: 1421
Previous month: 7
Current month: 4