Measuring Adherence Within a Self-Guided Online Intervention for Depression and Anxiety: Secondary Analyses of a Randomized Controlled Trial

Maria Hanano, Leslie Rith-Najarian, Meredith Boyd, Denise Chavira, Maria Hanano, Leslie Rith-Najarian, Meredith Boyd, Denise Chavira

Abstract

Background: Self-guided online interventions offer users the ability to participate in an intervention at their own pace and address some traditional service barriers (eg, attending in-person appointments, cost). However, these interventions suffer from high dropout rates, and current literature provides little guidance for defining and measuring online intervention adherence as it relates to clinical outcomes.

Objective: This study aims to develop and test multiple measures of adherence to a specific self-guided online intervention, as guided by best practices from the literature.

Methods: We conducted secondary analyses on data from a randomized controlled trial of an 8-week online cognitive behavioral program that targets depression and anxiety in college students. We defined multiple behavioral and attitudinal adherence measures at varying levels of effort (ie, low, moderate, and high). Linear regressions were run with adherence terms predicting improvement in the primary outcome measure, the 21-item Depression, Anxiety, and Stress Scale (DASS-21).

Results: Of the 947 participants, 747 initiated any activity and 449 provided posttest data. Results from the intent-to-treat sample indicated that high level of effort for behavioral adherence significantly predicted symptom change (F4,746=17.18, P<.001; and β=-.26, P=.04). Moderate level of effort for attitudinal adherence also significantly predicted symptom change (F4,746=17.25, P<.001; and β=-.36, P=.03). Results differed in the initiators-only sample, such that none of the adherence measures significantly predicted symptom change (P=.09-.27).

Conclusions: Our findings highlight the differential results of dose-response models testing adherence measures in predicting clinical outcomes. We summarize recommendations that might provide helpful guidance to future researchers and intervention developers aiming to investigate online intervention adherence.

Trial registration: ClinicalTrials.gov NCT04361045; https://ichgcp.net/clinical-trials-registry/NCT04361045.

Keywords: adherence; anxiety; depression; online intervention; self-guided.

Conflict of interest statement

Conflicts of Interest: LR-N is the developer of the online platform used in this study and owns the copyright as registered with the United States Copyright Office. The authors have no other conflicts of interest to disclose.

©Maria Hanano, Leslie Rith-Najarian, Meredith Boyd, Denise Chavira. Originally published in JMIR Mental Health (https://mental.jmir.org), 28.03.2022.

Figures

Figure 1
Figure 1
Distribution plots of each adherence measure (Adherence measure, Skewness, Kutorsis).

References

    1. Beatty L, Binnion C. A Systematic review of predictors of, and reasons for, adherence to online psychological interventions. Int J Behav Med. 2016 Dec 8;23(6):776–794. doi: 10.1007/s12529-016-9556-9.10.1007/s12529-016-9556-9
    1. Fleming Theresa M, de Beurs Derek, Khazaal Yasser, Gaggioli Andrea, Riva Giuseppe, Botella Cristina, Baños Rosa M, Aschieri Filippo, Bavin Lynda M, Kleiboer Annet, Merry Sally, Lau Ho Ming, Riper Heleen. Maximizing the impact of e-Therapy and serious gaming: time for a paradigm shift. Front Psychiatry. 2016;7:65. doi: 10.3389/fpsyt.2016.00065. doi: 10.3389/fpsyt.2016.00065.
    1. Hollis C, Falconer CJ, Martin JL, Whittington C, Stockton S, Glazebrook C, Davies EB. Annual Research Review: Digital health interventions for children and young people with mental health problems - a systematic and meta-review. J Child Psychol Psychiatry. 2017 Apr 10;58(4):474–503. doi: 10.1111/jcpp.12663.
    1. Karyotaki E, Kleiboer A, Smit F, Turner DT, Pastor AM, Andersson G, Berger T, Botella C, Breton JM, Carlbring P, Christensen H, de Graaf E, Griffiths K, Donker T, Farrer L, Huibers MJH, 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. 2015 Apr 17;45(13):2717–2726. doi: 10.1017/s0033291715000665.
    1. Cuijpers P, Marks IM, van Straten A, Cavanagh K, Gega L, Andersson G. Computer-aided psychotherapy for anxiety disorders: a meta-analytic review. Cogn Behav Ther. 2009 Jun;38(2):66–82. doi: 10.1080/16506070802694776.912391320
    1. Andersson G, Cuijpers P. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther. 2009 Dec 15;38(4):196–205. doi: 10.1080/16506070903318960.917833314
    1. Waller R, Gilbody S. Barriers to the uptake of computerized cognitive behavioural therapy: a systematic review of the quantitative and qualitative evidence. Psychol. Med. 2008 Sep 24;39(5):705–712. doi: 10.1017/s0033291708004224.
    1. Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev. 2012 Jun;32(4):329–42. doi: 10.1016/j.cpr.2012.02.004.S0272-7358(12)00027-X
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar 31;7(1):e11. doi: 10.2196/jmir.7.1.e11. v7e11
    1. Beintner I, Vollert B, Zarski A, Bolinski F, Musiat P, Görlich Dennis, Ebert DD, Jacobi C. Adherence reporting in randomized controlled trials examining manualized multisession online interventions: systematic review of practices and proposal for reporting standards. J Med Internet Res. 2019 Aug 15;21(8):e14181. doi: 10.2196/14181. v21i8e14181
    1. Sieverink F, Kelders SM, van Gemert-Pijnen JE. Clarifying the concept of adherence to eHealth technology: systematic review on when usage becomes adherence. J Med Internet Res. 2017 Dec 06;19(12):e402. doi: 10.2196/jmir.8578. v19i12e402
    1. Christensen H, Griffiths KM, Farrer L. Adherence in internet interventions for anxiety and depression. J Med Internet Res. 2009 Apr 24;11(2):e13. doi: 10.2196/jmir.1194. v11i2e13
    1. De Geest S, Sabaté E. Adherence to long-term therapies: evidence for action. European Journal of Cardiovascular Nursing. 2016 Jun 22;2(4):323–323. doi: 10.1016/s1474-5151(03)00091-4.
    1. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. 2011 Aug 05;13(3):e52. doi: 10.2196/jmir.1772. v13i3e52
    1. Donkin L, Hickie IB, Christensen H, Naismith SL, Neal B, Cockayne NL, Glozier N. Rethinking the dose-response relationship between usage and outcome in an online intervention for depression: randomized controlled trial. J Med Internet Res. 2013 Oct 17;15(10):e231. doi: 10.2196/jmir.2771. v15i10e231
    1. Rith-Najarian L. A dissemination and implementation approach to preventing anxiety and depression in young people. Doctoral dissertation, University of California Los Angeles. 2019. [2022-01-26]. .
    1. Staudt M. Treatment engagement with caregivers of at-risk children: gaps in research and conceptualization. J Child Fam Stud. 2006 Aug 23;16(2):183–196. doi: 10.1007/s10826-006-9077-2.
    1. Van der Zanden R, Curie K, Van Londen M, Kramer J, Steen G, Cuijpers P. Web-based depression treatment: associations of clients' word use with adherence and outcome. J Affect Disord. 2014 May;160:10–3. doi: 10.1016/j.jad.2014.01.005. S0165-0327(14)00020-2
    1. Norton PJ. Depression Anxiety and Stress Scales (DASS-21): psychometric analysis across four racial groups. Anxiety Stress Coping. 2007 Sep;20(3):253–65. doi: 10.1080/10615800701309279.781200904
    1. Beard C, Hsu K, Rifkin L, Busch A, Björgvinsson T. Validation of the PHQ-9 in a psychiatric sample. J Affect Disord. 2016 Mar 15;193:267–73. doi: 10.1016/j.jad.2015.12.075.S0165-0327(15)31027-2
    1. Duckworth AL, Quinn PD. Development and validation of the short grit scale (grit-s) J Pers Assess. 2009 Mar 17;91(2):166–74. doi: 10.1080/00223890802634290.908607471
    1. Ryan RM, Plant RW, O'Malley S. Initial motivations for alcohol treatment: Relations with patient characteristics, treatment involvement, and dropout. Addictive Behaviors. 1995 May;20(3):279–297. doi: 10.1016/0306-4603(94)00072-7.
    1. Lyubomirsky S, Lepper HS. Measure of subjective happiness: preliminary reliability and construct validation. Social Indicators Research. 1999 Feb;46(2):137–155. doi: 10.1023/A:1006824100041.
    1. Ghani IMM, Ahmad S. Stepwise multiple regression method to forecast fish landing. Procedia - Social and Behavioral Sciences. 2010;8:549–554. doi: 10.1016/j.sbspro.2010.12.076.
    1. Enders CK. Applied Missing Data Analysis. New York, NY: Guilford Press; 2010.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological) 2018 Dec 05;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x.
    1. Headspace. [2022-01-26].
    1. Harrer M, Adam SH, Baumeister H, Cuijpers P, Karyotaki E, Auerbach RP, Kessler RC, Bruffaerts R, Berking M, Ebert DD. Internet interventions for mental health in university students: A systematic review and meta-analysis. Int J Methods Psychiatr Res. 2019 Jun 26;28(2):e1759. doi: 10.1002/mpr.1759.
    1. Christensen H, Griffiths KM, Korten AE, Brittliffe K, Groves C. A comparison of changes in anxiety and depression symptoms of spontaneous users and trial participants of a cognitive behavior therapy website. J Med Internet Res. 2004 Dec 22;6(4):e46. doi: 10.2196/jmir.6.4.e46.v6e46
    1. Peugh JL, Enders CK. Missing data in educational research: a review of reporting practices and suggestions for improvement. Review of Educational Research. 2016 Jun 30;74(4):525–556. doi: 10.3102/00346543074004525.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren