The Association Between Increased Levels of Patient Engagement With an Internet Support Group and Improved Mental Health Outcomes at 6-Month Follow-Up: Post-Hoc Analyses From a Randomized Controlled Trial

Emily M Geramita, Bea Herbeck Belnap, Kaleab Z Abebe, Scott D Rothenberger, Armando J Rotondi, Bruce L Rollman, Emily M Geramita, Bea Herbeck Belnap, Kaleab Z Abebe, Scott D Rothenberger, Armando J Rotondi, Bruce L Rollman

Abstract

Background: We recently reported that depressed and anxious primary care patients randomized to a moderated internet support group (ISG) plus computerized cognitive behavioral therapy (cCBT) did not experience improvements in depression and anxiety over cCBT alone at 6-month follow-up.

Objective: The 1% rule posits that 1% of participants in online communities generate approximately 90% of new user-created content. The aims of this study were to apply the 1% rule to categorize patient engagement with the ISG and identify whether any patient subgroups benefitted from ISG use.

Methods: We categorized the 302 patients randomized to the ISG as: superusers (3/302, 1.0%), top contributors (30/302, 9.9%), contributors (108/302, 35.8%), observers (87/302, 28.8%) and those who never logged in (74/302, 24.5%). We then applied linear mixed models to examine associations between engagement and 6-month changes in health-related quality of life (HRQoL; Short Form Health Survey Mental Health Component, SF-12 MCS) and depression and anxiety symptoms (Patient-Reported Outcomes Measurement Information System, PROMIS).

Results: At baseline, participant mean age was 42.6 years, 81.1% (245/302) were female, and mean Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder scale (GAD-7), and SF-12 MCS scores were 13.4, 12.6, and 31.7, respectively. Of the 75.5% (228/302) who logged in, 61.8 % (141/228) created ≥1 post (median 1, interquartile range, IQR 0-5); superusers created 42.3 % (630/1488) of posts (median 246, IQR 78-306), top contributors created 34.6% (515/1488; median 11, IQR 10-18), and contributors created 23.1 % (343/1488; median 3, IQR 1-5). Compared to participants who never logged in, the combined superuser + top contributor subgroup (n=33) reported 6-month improvements in anxiety (PROMIS: -11.6 vs -7.8; P=.04) and HRQoL (SF-12 MCS: 16.1 vs 10.1; P=.01) but not in depression. No other subgroup reported significant symptom improvements.

Conclusions: Patient engagement with the ISG was more broadly distributed than predicted by the 1% rule. The 11% of participants with the highest engagement levels reported significant improvements in anxiety and HRQoL.

Trial registration: ClinicalTrials.gov NCT01482806; https://ichgcp.net/clinical-trials-registry/NCT01482806 (Archived by WebCite at http://www.webcitation.org/708Bjlge9).

Keywords: anxiety; depression; internet support group; patient engagement.

Conflict of interest statement

Conflicts of Interest: None declared.

©Emily M Geramita, Bea Herbeck Belnap, Kaleab Z Abebe, Scott D Rothenberger, Armando J Rotondi, Bruce L Rollman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.07.2018.

Figures

Figure 1
Figure 1
Screenshot of our internet support group homepage (ottrial.pitt.edu).

References

    1. van der Eijk M, Faber MJ, Aarts JWM, Kremer JAM, Munneke M, Bloem BR. Using online health communities to deliver patient-centered care to people with chronic conditions. J Med Internet Res. 2013 Jun;15(6):e115. doi: 10.2196/jmir.2476.
    1. Rollman BL, Herbeck BB, Rotondi AJ. Internet support groups for health: ready for the Affordable Care Act. J Gen Intern Med. 2014 Nov;29(11):1436–1438. doi: 10.1007/s11606-014-2884-z.
    1. Callan JA, Wright J, Siegle GJ, Howland RH, Kepler BB. Use of computer and mobile technologies in the treatment of depression. Arch Psychiatr Nurs. 2017 Dec;31(3):311–318. doi: 10.1016/j.apnu.2016.10.002.
    1. Houston TK, Cooper LA, Ford DE. Internet support groups for depression: a 1-year prospective cohort study. Am J Psychiatry. 2002 Dec;159(12):2062–2068.
    1. Rotondi AJ, Anderson CM, Haas GL, Eack SM, Spring MB, Ganguli R, Newhill C, Rosenstock J. Web-based psychoeducational intervention for persons with schizophrenia and their supporters: one-year outcomes. Psychiatr Serv. 2010 Nov;61(11):1099–1105. doi: 10.1176/appi.ps.61.11.1099.
    1. Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. BMJ. 2004 May 15;328(7449):1166. doi: 10.1136/bmj.328.7449.1166.
    1. Griffiths KM, Calear AL, Banfield M. Systematic review on Internet Support Groups (ISGs) and depression (1). Do ISGs reduce depressive symptoms? J Med Internet Res. 2009;11(3):e40. doi: 10.2196/jmir.1270.
    1. Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry. 2013 Aug;35(4):332–338. doi: 10.1016/j.genhosppsych.2013.03.008.
    1. Rollman BL, Herbeck BB, Abebe KZ, Spring MB, Rotondi AJ, Rothenberger SD, Karp JF. Effectiveness of online collaborative care for treating mood and anxiety disorders in primary care: a randomized clinical trial. JAMA Psychiatry. 2018 Jan 01;75(1):56–64. doi: 10.1001/jamapsychiatry.2017.3379.
    1. Arthur C. What is the 1% rule? The Guardian; 2006. [2018-06-12]. .
    1. Nielsen J. The 90-9-1 rule for participation inequality in social media and online communities. 2006. [2018-06-12].
    1. van Mierlo T. The 1% rule in four digital health social networks: an observational study. J Med Internet Res. 2014;16(2):e33. doi: 10.2196/jmir.2966.
    1. van Mierlo T, Li X, Hyatt D, Ching AT. Demographic and indication-specific characteristics have limited association with social network engagement: evidence from 24,954 members of four health care support groups. J Med Internet Res. 2017 Feb 17;19(2):e40. doi: 10.2196/jmir.6330.
    1. Lattie EG, Ho J, Sargent E, Tomasino KN, Smith JD, Brown CH, Mohr DC. Teens engaged in collaborative health: the feasibility and acceptability of an online skill-building intervention for adolescents at risk for depression. Internet Interv. 2017 Jun;8:15–26. doi: 10.1016/j.invent.2017.02.003.
    1. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 22;166(10):1092–1097. doi: 10.1001/archinte.166.10.1092.
    1. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–613.
    1. Proudfoot J, Ryden C, Everitt B, Shapiro DA, Goldberg D, Mann A, Tylee A, Marks I, Gray JA. Clinical efficacy of computerised cognitive-behavioural therapy for anxiety and depression in primary care: randomised controlled trial. Br J Psychiatry. 2004 Jul;185:46–54.
    1. Rollman BL, Herbeck BB, Reynolds CF, Schulberg HC, Shear MK. A contemporary protocol to assist primary care physicians in the treatment of panic and generalized anxiety disorders. Gen Hosp Psychiatry. 2003;25(2):74–82.
    1. Spitzer RL, Williams JB, Kroenke K, Linzer M, deGruy FV, Hahn SR, Brody D, Johnson JG. Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. JAMA. 1994 Dec 14;272(22):1749–1756.
    1. Ware J, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996 Mar;34(3):220–233.
    1. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D, PROMIS Cooperative Group Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS): depression, anxiety, and anger. Assessment. 2011 Sep;18(3):263–283. doi: 10.1177/1073191111411667.
    1. Griffiths KM, Mackinnon AJ, Crisp DA, Christensen H, Bennett K, Farrer L. The effectiveness of an online support group for members of the community with depression: a randomised controlled trial. PLoS One. 2012;7(12):e53244. doi: 10.1371/journal.pone.0053244.
    1. Setoyama Y, Yamazaki Y, Namayama K. Benefits of peer support in online Japanese breast cancer communities: differences between lurkers and posters. J Med Internet Res. 2011;13(4):e122. doi: 10.2196/jmir.1696.
    1. Han JY, Hou J, Kim E, Gustafson DH. Lurking as an active participation process: a longitudinal investigation of engagement with an online cancer support group. Health Commun. 2014;29(9):911–923. doi: 10.1080/10410236.2013.816911.
    1. Shaw BR, Hawkins R, McTavish F, Pingree S, Gustafson DH. Effects of insightful disclosure within computer mediated support groups on women with breast cancer. Health Commun. 2006;19(2):133–142. doi: 10.1207/s15327027hc1902_5.
    1. Cavanagh K, Herbeck Belnap B, Rothenberger S, Abebe K, Rollman B. My care manager, my computer therapy and me: the relationship triangle in computerized cognitive behavioural therapy. Internet Interventions. 2018 Mar;11:11–19. doi: 10.1016/j.invent.2017.10.005.

Source: PubMed

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