Skip to main content

Possibilities to Improve Online Mental Health Treatment: Recommendations for Future Research and Developments

  • Conference paper
  • First Online:
Advances in Information and Communication Networks (FICC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 886))

Included in the following conference series:

Abstract

Online mental health treatment has the potential to meet the increasing demand for mental health treatment. But low adherence to the treatment remains a problem that endangers treatment outcomes and their cost-effectiveness. This literature review compares predictors of adherence and outcome for clinical and online treatment of mental disorders to identify ways to improve the efficacy of online treatment and increase clients’ adherence. Personalization of treatment and client improvement tracking appears to provide the most potential to improve clients’ outcome and increase the cost-effectiveness of online treatment. Overall, it was noticed that decision support tools to improve online treatment are commonly not utilized and that their influence on treatment is unknown. However, integration of statistical methods into online treatment and research of their influence on the client has begun. Decision support systems derived from predictors of adherence might be required for personalization of online treatments and to improve outcome and cost-effectiveness to ease the burden of mental disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lépine, J.P., Briley, M.: The increasing burden of depression. Neuropsychiatr. Dis. Treat. 7(Suppl 1), 3–7 (2011)

    Google Scholar 

  2. Alonso, J., Angermeyer, M.C., Bernert, S., Bruffaerts, R., Brugha, T.S., Bryson, H., de Girolamo, G., Graaf, R., Demyttenaere, K., Gasquet, I., Haro, J.M., Katz, S.J., Kessler, R.C., Kovess, V., Lépine, J.P., Ormel, J., Polidori, G., Russo, L.J., Vilagut, G., Almansa, J., Arbabzadeh-Bouchez, S., Autonell, J., Bernal, M., Buist-Bouwman, M.A., Codony, M., Domingo-Salvany, A., Ferrer, M., Joo, S.S., Martínez-Alonso, M., Matschinger, H., Mazzi, F., Morgan, Z., Morosini, P., Palacín, C., Romera, B., Taub, N., Vollebergh, W.A.M.: Prevalence of mental disorders in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr. Scandinavica. Suppl. 109(420), 21–27 (2004)

    Google Scholar 

  3. Kessler, R., Chiu, W.: Prevalence, severity, and comorbidity of twelve-month DSM-IV disorders in the national comorbidity survey replication (NCS- R). Arch. Gen. Psychiatry62(6), 617–627 (2005)

    Google Scholar 

  4. WHO. 2014 Mental health atlas, WHO, p. 72 (2014)

    Google Scholar 

  5. The Executive Board. Global burden of mental disorders and the need for a comprehensive , coordinated response from health and social sectors at the country level, World Health, pp. 6–9 (2012)

    Google Scholar 

  6. Cameron, P.A., Thompson, D.R.: Changing the health-care workforce. Int. J. Nurs. Pract. 11(1), 1–4 (2005)

    Article  Google Scholar 

  7. Gustavsson, A., Svensson, M., Jacobi, F., Allgulander, C., Alonso, J., Beghi, E., Dodel, R., Ekman, M., Faravelli, C., Fratiglioni, L., Gannon, B., Jones, D.H., Jennum, P., Jordanova, A., Jönsson, L., Karampampa, K., Knapp, M., Kobelt, G., Kurth, T., Lieb, R., Linde, M., Ljungcrantz, C., Maercker, A., Melin, B., Moscarelli, M., Musayev, A., Norwood, F., Preisig, M., Pugliatti, M., Rehm, J., Salvador-Carulla, L., Schlehofer, B., Simon, R., Steinhausen, H.C., Stovner, L.J., Vallat, J.M., den Bergh, P.V., van Os, J., Vos, P., Xu, W., Wittchen, H.U., Jönsson, B., Olesen, J.: Cost of disorders of the brain in Europe 2010. Eur. Neuropsychopharmacol. 21(10), 718–779 (2011)

    Google Scholar 

  8. Kessler, R.C., Heeringa, S., Lakoma, M.D., Petukhova, M., Rupp, A.E., Schoenbaum, M., Wang, P.S., Zaslavsky, A.M.: Individual and societal effects of mental disorders on earnings in the United States: results from the national comorbidity survey replication. Am. J. Psychiatry 165(6), 703–11 (2008)

    Article  Google Scholar 

  9. Insel, T.: Assessing the economic costs of serious mental illness. Am. J. Psychiat. 165(6), 663–665 (2008)

    Article  Google Scholar 

  10. Bloom, D.E., Cafiero, E., Jané-Llopis, E., Abrahams-Gessel, S., Reddy Bloom, L., Fathima, S., Feigl, A.B., Gaziano, T., Hamandi, A., Mowafi, M., O’Farrell, D., Ozaltin, E., Pandya, A., Prettner, K., Rosenberg, L., Seligman, B., Stein, A.Z., Weinstein, C., Weiss, J.: The global economic burden of noncommunicable diseases. World Economic Forum, pp. 1–46 (2011)

    Google Scholar 

  11. Christensen, H., Griffiths, K.M.: The prevention of depression using the internet. Med. J. Aust. 177(7), S122–S125 (2002)

    Google Scholar 

  12. Cuijpers, P., Van Straten, A., Andersson, G.: Internet-administered cognitive behavior therapy for health problems: a systematic review. J. Behav. Med. 31, 169–177 (2008). no. 0160-7715 (Print)

    Article  Google Scholar 

  13. Kohn, R., Saxena, S., Levav, I., Saraceno, B.: Thee treatment gap in mental health care health. Bull. World Health Organ. 82(11), 858–866 (2004)

    Google Scholar 

  14. Tate, D.F., Finkelstein, E.A., Khavjou, O., Gustafson, A.: Cost effectiveness of internet interventions: Review and recommendations. Ann. Behav. Med. 38(1), 40–45 (2009)

    Article  Google Scholar 

  15. Hedman, E., Andersson, E., Ljótsson, B., Andersson, G., Rück, C., Lindefors, N.: Cost-effectiveness of Internet-based cognitive behavior therapy vs. cognitive behavioral group therapy for social anxiety disorder: results from a randomized controlled trial. Behav. Res. Ther. 49(11), 729–736 (2011)

    Article  Google Scholar 

  16. Van Beugen, S., Ferwerda, M., Hoeve, D., Rovers, M.M., Spillekom-Van Koulil, S., Van Middendorp, H., Evers, A.W.M.: Internet-based cognitive behavioral therapy for patients with chronic somatic conditions: a meta-analytic review. J. Med. Internet Res. 16(3), 1–15 (2014)

    Google Scholar 

  17. Donker, T., Blankers, M., Hedman, E., Ljótsson, B., Petrie, K., Christensen, H.: Economic evaluations of Internet interventions for mental health: a systematic review. Psychol. Med. 45, 1–20 (2015)

    Article  Google Scholar 

  18. Furmark, T., Carlbring, P., Hedman, E., Sonnenstein, A., Clevberger, P., Bohman, B., Eriksson, A., Hållén, A., Frykman, M., Holmström, A., Sparthan, E., Tillfors, M., Ihrfelt, E.N., Spak, M., Eriksson, A., Ekselius, L., Andersson, G.: Guided and unguided self-help for social anxiety disorder: randomised controlled trial. Br. J. Psychiatry 195(5), 440–447 (2009)

    Article  Google Scholar 

  19. McCrone, P., Knapp, M., Proudfoot, J., Ryden, C., Cavanagh, K., Shapiro, D.A., Ilson, S., Gray, J.A., Goldberg, D., Mann, A., Marks, I., Everitt, B., Tylee, A.: Cost-effectiveness of computerised cognitive-behavioural therapy for anxiety and depression in primary care: randomised controlled trial. Br. J. Psychiatry 185, 55–62 (2004)

    Article  Google Scholar 

  20. SAMHSA: TIP 34: Brief Interventions and Brief Therapies for Substance Abuse, Brief Interventions and Brief Therapies For Substance Abuse, pp. 105–121 (2012)

    Google Scholar 

  21. Van Straten, A., Cuijpers, P., Smits, N.: Effectiveness of a web-based self-help intervention for symptoms of depression, anxiety, and stress: randomized controlled trial. J. Med. Internet Res. 10(1) (2008)

    Google Scholar 

  22. Van Ballegooijen, W., Cuijpers, P., Van Straten, A., Karyotaki, E., Andersson, G., Smit, J.H., Riper, H.: Adherence to internet-based and face-to-face cognitive behavioural therapy for depression: a meta-analysis. PLoS ONE 9(7), e100674 (2014)

    Google Scholar 

  23. Melville, K.M., Casey, L.M., Kavanagh, D.J.: Dropout from Internet-based treatment for psychological disorders. Br. J. Clin. Psychol./Br. Psychol. Soc. 49(4), 455–71 (2010)

    Article  Google Scholar 

  24. White, K.S., Allen, L.B., Barlow, D.H., Gorman, J.M., Shear, M.K., Woods, S.W.: Attrition in a multicenter clinical trial for panic disorder. J. Nerv. Ment. Dis. 198(9), 665–671 (2010)

    Article  Google Scholar 

  25. Vogenberg, F.R.: Predictive and prognostic models: implications for healthcare decision-making in a modern recession. Am. Health Drug Benefits 2(6), 218 (2009)

    Google Scholar 

  26. Cuijpers, P., van Straten, A., Warmerdam, L.: Problem solving therapies for depression: a meta-analysis. Eur. Psychiatry 22(1), 9–15 (2007)

    Article  Google Scholar 

  27. van Straten, A., Cuijpers, P., Smits, N.: Effectiveness of a web-based self-help intervention for symptoms of depression, anxiety, and stress: randomized controlled trial. J. Med. Internet Res. 10(1), e7 (2008)

    Google Scholar 

  28. Boettcher, J., Rozental, A., Andersson, G., Carlbring, P.: Side effects in internet-based interventions for social anxiety disorder. Internet Interv. 1(1), 3–11 (2014)

    Article  Google Scholar 

  29. Burcusa, S.L., Iacono, W.G.: Risk for recurrence in depression. Clin. Psychol. Rev. 27(8), 959–985 (2007)

    Article  Google Scholar 

  30. Michael, F.G.L., Vergare, J., Binder, R.L., Cook, I.A., Galanter, M.: Practice guideline for the psychiatric evaluation of adults (2006)

    Google Scholar 

  31. Bhugra, D., Bhui, K.: Cross-cultural psychiatric assessment. Adv. Psychiatr. Treat. 3(2), 103–110 (1997)

    Article  Google Scholar 

  32. Song, I., Diederich, J.: Speech analysis for mental health assessment using support vector machines. In: Mental Health Informatics (2014)

    Google Scholar 

  33. Abussa, M., Diederich, J., Al-ajmi, A., Language, N., Group, M.L.: Web mining and mental health. In: IAWTIC 2004 Proceedings. International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 12–14 (2004)

    Google Scholar 

  34. Pestian, J., Nasrallah, H.: Suicide note classification using natural language processing: a content analysis. Biomed. Inform. Insights 3, 19–28 (2010)

    Article  Google Scholar 

  35. Conte, H.R., Plutchik, R., Picard, S., Karasu, T.B., Vaccaro, D.: Self-report measures as predictors of psychotherapy outcome. Compr. Psychiatry 29(4), 355–360 (1988)

    Article  Google Scholar 

  36. Steketee, G., Shapiro, L.J.: Predicting behavioral treatment outcome for agoraphobia and obsessive compulsive disorder. Clin. Psychol. Rev. 15(4), 317–346 (1995)

    Article  Google Scholar 

  37. Keijsers, G.P., Kampman, M., Hoogduin, C.A.L.: Dropout prediction in cognitive behavior therapy for panic disorder. Behav. Ther. 32(4), 739–749 (2001)

    Article  Google Scholar 

  38. Meulenbeek, P., Seeger, K., ten Klooster, P.M.: Dropout prediction in a public mental health intervention for sub-threshold and mild panic disorder. Cogn. Behav. Ther. 8, e5 (2015)

    Google Scholar 

  39. Luborsky, L., Chandler, M., Auerbach, A.H., Cohen, J.: Factors influencing the outcome of psychotherapy: a review of quantitative research. Psychol. Bull. 75(3), 145–185 (1971)

    Article  Google Scholar 

  40. Conte, H.R., Plutchik, R., Picard, S., Karasu, T.B.: Can personality traits predict psychotherapy outcome? Compr. Psychiatry 32(1), 66–72 (1991)

    Article  Google Scholar 

  41. Keijsers, G.P., Hoogduin, C.A., Schaap, C.P.: Predictors of treatment outcome in the behavioural treatment of obsessive-compulsive disorder. Br. J. Psychiatry 165(6), 781–786 (1994)

    Article  Google Scholar 

  42. Perlis, R.H.: A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol. Psychiatry 74, 7–14 (2013)

    Article  Google Scholar 

  43. Karyotaki, E., Kleiboer, A., Smit, F., Turner, D., Pastor, A., Andersson, G., Berger, T., Botella, C., Breton, J., Carlbring, P., Christensen, H., De Graaf, E., Griffiths, K., Donker, T., Farrer, L., Huibers, M., Lenndin, J., Mackinnon, A., Meyer, B., Moritz, S., Riper, H., Spek, V., Vernmark, K., Cuijpers, P.: Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis. Psychol. Med. 45(13), 2717–2726 (2015)

    Article  Google Scholar 

  44. Alfonsson, S., Olsson, E., Hursti, T.: Motivation and treatment credibility predicts dropout, treatment adherence, and clinical outcomes in an internet-based cognitive behavioral relaxation program: a randomized controlled trial. J. Med. Internet Res. 18(3), e52 (2016)

    Article  Google Scholar 

  45. Suhara, Y., Xu, Y., Pentland, A.S.: DeepMood: forecasting depressed mood based on self-reported histories via recurrent neural networks. In: WWW, pp. 715–724 (2017)

    Google Scholar 

  46. Demirci, K., Akgönül, M., Akpinar, A.: Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J. Behav. Addict. 4(2), 85–92 (2015)

    Article  Google Scholar 

  47. Gomes-Schwartz, B.: Effective ingredients in psychotherapy: prediction of outcome from process variables. J. Consult. Clin. Psychol. 46, 1023–1035 (1978)

    Article  Google Scholar 

  48. Van, H.L., Schoevers, R.A., Kool, S., Hendriksen, M., Peen, J., Dekker, J.: Does early response predict outcome in psychotherapy and combined therapy for major depression? J. Affect. Disord. 105(1-3), 261–265 (2008)

    Article  Google Scholar 

  49. Lutz, W., Arndt, A., Rubel, J., Berger, T., Schröder, J., Späth, C., Meyer, B., Greiner, W., Gräfe, V., Hautzinger, M., Fuhr, K., Rose, M., Nolte, S., Löwe, B., Hohagen, F., Klein, J.P., Moritz, S.: Defining and predicting patterns of early response in a web-based intervention for depression. J. Med. Internet Res. 19(6), e206 (2017)

    Article  Google Scholar 

  50. Kegel, A.F., Flückiger, C.: Predicting psychotherapy dropouts: a multilevel approach. Clin. Psychol. Psychother.22(5), 377–386 (2015)

    Article  Google Scholar 

  51. Feather, J.S., Howson, M., Ritchie, L., Carter, P.D., Parry, D.T., Koziol-McLain, J.: Evaluation methods for assessing users’ psychological experiences of web-based psychosocial interventions: a systematic review. J. Med. Internet Res. 18(6), e181 (2016)

    Article  Google Scholar 

  52. Proudfoot, J., Clarke, J., Birch, M.-R., Whitton, A.E., Parker, G., Manicavasagar, V., Harrison, V., Christensen, H., Hadzi-Pavlovic, D.: Impact of a mobile phone and web program on symptom and functional outcomes for people with mild-to-moderate depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry 13(1), 312 (2013)

    Article  Google Scholar 

  53. Whitton, A.E., Proudfoot, J., Clarke, J., Birch, M.R., Parker, G., Manicavasagar, V., Hadzi-pavlovic, D.: Breaking open the black box : isolating the most potent features of a web and mobile phone-based intervention for depression , anxiety , and stress. JMIR Ment. Health 2, 1–13 (2015)

    Article  Google Scholar 

  54. Kelders, S.M., Bohlmeijer, E.T, Van Gemert-Pijnen, J.E.W.C.: Participants, usage, and use patterns of a web-based intervention for the prevention of depression within a randomized controlled trial. J. Med. Internet Res. 15(8), e172 (2013)

    Article  Google Scholar 

  55. Brindal, E., Freyne, J., Saunders, I., Berkovsky, S., Smith, G., Noakes, M.: Features predicting weight loss in overweight or obese participants in a web-based intervention: randomized trial. J. Med. Internet Res. 14(6), e173 (2012)

    Article  Google Scholar 

  56. Van Gemert-Pijnen, J.E.W.C., Kelders, S.M., Bohlmeijer, E.T.: Understanding the usage of content in a mental health intervention for depression: an analysis of log data. J. Med. Internet Res.16(1), e27 (2014)

    Google Scholar 

  57. Luborsky, L.: Therapeutic alliances as predictors of psychotherapy outcomes: factors explaining the predictive success. In: The Working Alliance: Theory, Research, and Practice (1994)

    Google Scholar 

  58. Ardito, R.B., Rabellino, D.: Therapeutic alliance and outcome of psychotherapy: historical excursus, measurements, and prospects for research. Front. Psychol. 2, 1–11 (2011)

    Google Scholar 

  59. Johansson, H., Eklund, M.: Patients’ opinion on what constitutes good psychiatric care. Scand. J. Caring Sci. 17(4), 339–346 (2003)

    Article  Google Scholar 

  60. Martin, D.J., Garske, J.P., Davis, M.K.: Relation of the therapeutic alliance with outcome and other variables: a meta-analytic review. J. Consult. Clin. Psychol. 68(3), 438–450 (2000)

    Article  Google Scholar 

  61. Horvath, A.O., Luborsky, L.: The role of the therapeutic alliance in psychotherapy. J. Couns. Clin. Psychol. 61(4), 561–573 (1993)

    Article  Google Scholar 

  62. Bachelor, A.: Clients’ and therapists’ views of the therapeutic alliance: similarities, differences and relationship to therapy outcome. Clin. Psychol. Psychother. 20(2), 118–135 (2013)

    Article  Google Scholar 

  63. Knaevelsrud, C., Maercker, A.: Internet-based treatment for PTSD reduces distress and facilitates the development of a strong therapeutic alliance: a randomized controlled clinical trial. BMC Psychiatry 7, 13 (2007)

    Article  Google Scholar 

  64. Reynolds, D.J., Stiles, W.B., Bailer, A.J., Hughes, M.R.: Impact of exchanges and client-therapist alliance in online-text psychotherapy. Cyberpsychology Behav. Soc. Netw. 16(5), 370–7 (2013)

    Article  Google Scholar 

  65. White, M., Stinson, J.N., Lingley-Pottie, P., McGrath, P.J., Gill, N., Vijenthira, A.: Exploring therapeutic alliance with an internet-based self-management program with brief telephone support for youth with arthritis: a pilot study. Telemed. J. e-Health 18(4), 271–6 (2012). The official journal of the American Telemedicine Association

    Article  Google Scholar 

  66. Bergman Nordgren, L., Carlbring, P., Linna, E., Andersson, G.: Role of the working alliance on treatment outcome in tailored internet-based cognitive behavioural therapy for anxiety disorders: randomized controlled pilot trial. JMIR Res. Protoc. 2(1), e4 (2013)

    Article  Google Scholar 

  67. Lueger, R.J.: Using feedback on patient progress to predict the outcome of psychotherapy. J. Clin. Psychol. 54(3), 383–393 (1998)

    Article  Google Scholar 

  68. Lambert, M.: Prevention of Treatment Failure: The Use of Measuring, Monitoring, and Feedback in Clinical Practice. American Psychological Association, Washington D.C. (2010)

    Book  Google Scholar 

  69. Knaup, C., Koesters, M., Schoefer, D., Becker, T., Puschner, B.: Effect of feedback of treatment outcome in specialist mental healthcare: meta-analysis. Br. J. Psychiatry J. Ment. Sci. 195(1), 15–22 (2009)

    Article  Google Scholar 

  70. Lueger, R.J.: The Integra/COMPASS tracking assessment system. Integr. Sci. Pract. 2(2), 20–23 (2012)

    Google Scholar 

  71. Lambert, M.J.: The outcome questionnaire-45. Integr. Sci. Pract. 2(1), 24–27 (2012)

    Google Scholar 

  72. Evans, C.: The CORE-OM (Clinical Outcomes in Routine Evaluation) and its derivatives. Integr. Sci. Pract. 2(2), 12–15 (2000)

    Google Scholar 

  73. Schibbye, P., Ghaderi, A., Ljótsson, B., Hedman, E., Lindefors, N., Rück, C., Kaldo, V.: Using early change to predict outcome in cognitive behaviour therapy: Exploring timeframe, calculation method, and differences of disorder-specific versus general measures. PLoS ONE 9(6), e100614 (2014)

    Article  Google Scholar 

  74. Finch, A.: Psychotherapy Quality Control: The Statistical Generation of Recovery Curves for Integration Into an Early Warning System. Brigham Young University, Department of Clinical Psychology (2000)

    Google Scholar 

  75. Lambert, M.J., Whipple, J.L., Smart, D.W., Vermeersch, D.A., Hawkins, E.J., Al, L.E.T.: The effects of providing therapists with feedback on patient progress during psychotherapy: are outcomes enhanced? Psychother. Res. 11(1), 49–68 (2001)

    Article  Google Scholar 

  76. Lambert, M.J., Whipple, J.L., Hawkins, E.J., Vermeersch, D.A., Nielsen, S.L., Smart, D.W.: Is it time for clinicians to routinely track patient outcome? A meta-analysis. Clin. Psychol. Sci. Pract. 10(3), 288–301 (2003)

    Google Scholar 

  77. Bolger, N., DeLongis, A., Kessler, R.C., Schilling, E.A.: Effects of daily stress on negative mood. J. Pers. Soc. Psychol. 57(5), 808–818 (1989)

    Article  Google Scholar 

  78. Jacelon, C.S., Imperio, K.: Participant diaries as a source of data in research with older adults. Qual. Health Res. 15(7), 991–7 (2005)

    Article  Google Scholar 

  79. Wichers, M., Simons, C.J.P., Kramer, I.M.A., Hartmann, J.A., Lothmann, C., Myin-Germeys, I., van Bemmel, A.L., Peeters, F., Delespaul, P., van Os, J.: Momentary assessment technology as a tool to help patients with depression help themselves. Acta Psychiatr. Scand. 124(4), 262–272 (2011)

    Article  Google Scholar 

  80. Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Annu. Rev. Clin. Psychol. 4(5), 1–32 (2008)

    Article  Google Scholar 

  81. Bremer, V., Becker, D., Funk, B., Lehr, D.: Predicting the individual mood level based on diary data. In: Proceedings of the Twenty-Fifth Conference on Information Systems (ECIS 2017) (2017)

    Google Scholar 

  82. Postel, M.G., De Haan, H.A., Ter Huurne, E.D., Becker, E.S., De Jong, C.A.: Effectiveness of a web-based intervention for problem drinkers and reasons for dropout: randomized controlled trial. J. Med. Internet Res.12(4), e68 (2010)

    Article  Google Scholar 

  83. Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., Riper, H.: Mobile phone-based unobtrusive ecological momentary assessment of day-to-day mood: an explorative study. J. Med. Internet Res. 18(3), e72 (2016)

    Article  Google Scholar 

  84. Becker, D., Bremer, V., Funk, B., Asselbergs, J., Riper, H., Ruwaard, J.: How to predict mood? Delving into features of smartphone-based data. In: Twenty-second Americas Conference on Information Systems, pp. 1–10 (2016)

    Google Scholar 

  85. van Breda, W., Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J., Riper, H.: Exploring and comparing machine learning approaches for predicting mood over time. Smart Innovation, Systems and Technologies, vol. 60, pp. 37–47. Springer Science and Business Media Deutschland GmbH (2016)

    Google Scholar 

  86. Ma, Y., Xu, B., Bai, Y., Sun, G., Zhu, R.: Daily mood assessment based on mobile phone sensing. In: Proceedings - BSN 2012: 9th International Workshop on Wearable and Implantable Body Sensor Networks, pp. 142–147, May 2012

    Google Scholar 

  87. Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12(2), 74 (2011)

    Article  Google Scholar 

  88. 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 - UbiComp 2012, p. 351 (2012)

    Google Scholar 

  89. Chang, K.H., Fisher, D., Canny, J.: AMMON: a speech analysis library for analyzing affect, stress, and mental health on mobile phones. In: Proceedings of the 2011 PhoneSense Conference (2011)

    Google Scholar 

  90. Saeb, S., Zhang, M., Kwasny, M.M., Karr, C.J., Kording, K., Mohr, D.C.: The relationship between clinical, momentary, and sensor-based assessment of depression. In: International Conference on Pervasive Computing Technologies for Healthcare : Proceedings of International Conference on Pervasive Computing Technologies for Healthcare, vol. 2015, pp. 7–10 (2015)

    Google Scholar 

  91. 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. J. Med. Internet Res. 13(3), e55 (2011)

    Article  Google Scholar 

  92. Gill, S., Contreras, O., Munoz, R.F., Leykin, Y.: Participant retention in an automated online monthly depression rescreening program: Patterns and predictors. Internet Interv. 1(1), 20–25 (2014)

    Article  Google Scholar 

  93. Rodgers, A.: Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile phone text messaging. Tob. Control. 14(4), 255–261 (2005)

    Article  Google Scholar 

  94. Patrick, K., Raab, F., Adams, M.A., Dillon, L., Zabinski, M., Rock, C.L., Griswold, W.G., Norman, G.J.: A text message-based intervention for weight loss: randomized controlled trial. J. Med. Internet Res. 11(1), 1–9 (2009)

    Article  Google Scholar 

  95. Gurol-Urganci, I., de Jongh, T., Vodopivec-Jamsek, V., Atun, R., Car, J.: Mobile phone messaging reminders for attendance at healthcare appointments. Cochrane Database Syst. Rev. no. 12, p. CD007458 (2013). (Review) SUMMARY OF FINDINGS FOR THE MAIN COMPARISON

    Google Scholar 

  96. Kannisto, K.A., Koivunen, M.H., Välimäki, M.A.: Use of mobile phone text message reminders in health care services: a narrative literature review. J. Med. Internet Res. 16(10), e222 (2014)

    Article  Google Scholar 

  97. Ludden, G.D., Van Rompay, T.J., Kelders, S.M., Van Gemert-Pijnen, J.E.: How to increase reach and adherence of web-based interventions: a design research viewpoint. J. Med. Internet Res. 17(7), e172 (2015)

    Article  Google Scholar 

  98. Kelders, S.M., Kok, R.N., Ossebaard, H.C., Van Gemert-Pijnen, J.E.W.C.: Persuasive system design does matter: a systematic review of adherence to web-based interventions. J. Med. Internet Res.14(6), 1–24 (2012)

    Article  Google Scholar 

  99. Looyestyn, J., Kernot, J., Boshoff, K., Ryan, J., Edney, S., Maher, C.: Does gamification increase engagement with online programs? A systematic review. PLoS ONE 12(3), 1–19 (2017)

    Article  Google Scholar 

  100. Brown, M., O’Neill, N., van Woerden, H., Eslambolchilar, P., Jones, M., John, A.: Gamification and adherence to web-based mental health interventions: a systematic review. JMIR Ment. Health 3(3), e39 (2016)

    Article  Google Scholar 

  101. McKay, J.R.: Studies of factors in relapse to alcohol, drug and nicotine use: a critical review of methodologies and findings. J. Stud. Alcohol 60(4), 566–576 (1999)

    Article  Google Scholar 

  102. Vittengl, J.R., Clark, L.A., Dunn, T.W., Jarrett, R.B.: Reducing relapse and recurrence in unipolar depression: a comparative meta-analysis of cognitive–behavioral therapy’s effects. J. Consult. Clin. Psychol. 75, 475–488 (2009)

    Article  Google Scholar 

  103. Kessing, L.V.: Severity of depressive episodes according to ICD-I0: prediction of risk of relapse and suicide. Br. J. Psychiatry 184(2), 153–156 (2004)

    Article  Google Scholar 

  104. Segal, Z.V., Kennedy, S., Gemar, M., Hood, K., Pedersen, R., Buis, T.: Cognitive reactivity to sad mood provocation and the prediction of depressive relapse. Arch. Gen. Psychiatry 63(7), 749–755 (2006)

    Article  Google Scholar 

  105. Pedersen, M.U., Hesse, M.: A simple risk scoring system for prediction of relapse after inpatient alcohol treatment. Am. J. Addict. 18(6), 488–493 (2009). American Academy of Psychiatrists in Alcoholism and Addictions

    Article  Google Scholar 

  106. Van Voorhees, B.W., Paunesku, D., Gollan, J., Reinecke, M., Basu, A.: Predicting future risk of depressive episode in adolescents: the chicago adolescent depression risk assessment (CADRA). Ann. Fam. Med. 6(6), 503–512 (2008)

    Article  Google Scholar 

  107. Ito, J.R., Donovan, D.M.: Predicting drinking outcome: demography, chronicity, coping, and aftercare. Addict. Behav. 15(6), 553–559 (1990)

    Article  Google Scholar 

  108. Farren, C.K., McElroy, S.: Predictive factors for relapse after an integrated inpatient treatment programme for unipolar depressed and bipolar alcoholics. Alcohol Alcohol. 45(6), 527–533 (2010)

    Article  Google Scholar 

  109. Farren, C.K., Snee, L., Daly, P., McElroy, S.: Prognostic factors of 2-year outcomes of patients with comorbid bipolar disorder or depression with alcohol dependence: importance of early abstinence. Alcohol Alcohol. 48(1), 93–98 (2013)

    Article  Google Scholar 

  110. Barnes, C., Harvey, R., Mitchell, P., Smith, M., Wilhelm, K.: Evaluation of an online relapse prevention program for bipolar disorder: an overview of the aims and methodology of a randomized controlled trial, pp. 215–224 (2007)

    Google Scholar 

  111. Holländare, F., Anthony, S.A., Randestad, M., Tillfors, M., Carlbring, P., Andersson, G., Engström, I.: Two-year outcome of internet-based relapse prevention for partially remitted depression. Behav. Res. Ther. 51(11), 719–722 (2013)

    Article  Google Scholar 

  112. Lobban, F., Dodd, A.L., Dagnan, D., Diggle, P.J., Griffiths, M., Hollingsworth, B., Knowles, D., Long, R., Mallinson, S., Morriss, R.M., Parker, R., Sawczuk, A.P., Jones, S.: Feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline): trial protocol. Contemp. Clin. Trials 41, 100–109 (2015)

    Article  Google Scholar 

  113. Lord, S., Moore, S.K., Ramsey, A., Dinauer, S., Johnson, K.: Implementation of a substance use recovery support mobile phone app in community settings: qualitative study of clinician and staff perspectives of facilitators and barriers. JMIR Ment. Health 3(2), e24 (2016)

    Article  Google Scholar 

  114. Kok, G., Bockting, C., Burger, H., Smit, F., Riper, H.: Mobile cognitive therapy: adherence and acceptability of an online intervention in remitted recurrently depressed patients. Internet Interv. 1(2), 65–73 (2014)

    Article  Google Scholar 

  115. Beckjord, E., Shiffman, S.: Background for real-time monitoring and intervention related to alcohol use. Alcohol Res. Curr. Rev. 36(1), 9–18 (2014)

    Google Scholar 

  116. Juarascio, A.S., Manasse, S.M., Goldstein, S.P., Forman, E.M., Butryn, M.L.: Review of smartphone applications for the treatment of eating disorders. Eur. Eat. Disord. Rev. 23(1), 1–11 (2015)

    Article  Google Scholar 

  117. Gustafson, D.H., Shaw, B.R., Isham, A., Baker, T., Boyle, M.G., Levy, M.: Explicating an evidence-based, theoretically informed, mobile technology-based system to improve outcomes for people in recovery for alcohol dependence. Subst. Use Misuse 46(1), 96–111 (2011)

    Article  Google Scholar 

  118. Chih, M.Y., Patton, T., McTavish, F.M., Isham, A.J., Judkins-Fisher, C.L., Atwood, A.K., Gustafson, D.H.: Predictive modeling of addiction lapses in a mobile health application. J. Subst. Abus. Treat. 46(1), 29–35 (2014)

    Article  Google Scholar 

  119. Doryab, A., Min, J.K., Wiese, J., Zimmerman, J., Hong, J.I.: Detection of behavior change in people with depression. In: AAAI Workshops Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 12–16 (2014)

    Google Scholar 

  120. Diederich, J.: Ex-ray: text classification and the assessment of mental health. In: Eighth Australian Document Computing Symposium ADCS 2002–2003 (2003)

    Google Scholar 

  121. D’Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., Gleeson, J., Alvarez-Jimenez, M.: Artificial intelligence-assisted online social therapy for youth mental health. Front. Psychol. 8, 1–13 (2017)

    Google Scholar 

  122. Graham, A.L., Cha, S., Papandonatos, G.D., Cobb, N.K., Mushro, A., Fang, Y., Niaura, R.S., Abrams, D.B.: Improving adherence to web-based cessation programs: a randomized controlled trial study protocol. Trials 14(48), 1–15 (2013)

    Article  Google Scholar 

  123. Graham, A.L., Jacobs, M.A., Cohn, A.M., Cha, S., Abroms, L.C., Papandonatos, G.D., Whittaker, R.: Optimising text messaging to improve adherence to web-based smoking cessation treatment: a randomised control trial protocol. BMJ Open 6(3), e010687 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dennis Becker .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Becker, D. (2019). Possibilities to Improve Online Mental Health Treatment: Recommendations for Future Research and Developments. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 886. Springer, Cham. https://doi.org/10.1007/978-3-030-03402-3_8

Download citation

Publish with us

Policies and ethics