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Randomized Controlled Trial
. 2022 Aug 30;24(8):e38261.
doi: 10.2196/38261.

Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials

Isaac Moshe  1 , Yannik Terhorst  2 , Sarah Paganini  3 , Sandra Schlicker  4 , Laura Pulkki-Råback  1 , Harald Baumeister  2 , Lasse B Sander  5 , David Daniel Ebert  6
Affiliations
Randomized Controlled Trial

Predictors of Dropout in a Digital Intervention for the Prevention and Treatment of Depression in Patients With Chronic Back Pain: Secondary Analysis of Two Randomized Controlled Trials

Isaac Moshe et al. J Med Internet Res. .

Abstract

Background: Depression is a common comorbid condition in individuals with chronic back pain (CBP), leading to poorer treatment outcomes and increased medical complications. Digital interventions have demonstrated efficacy in the prevention and treatment of depression; however, high dropout rates are a major challenge, particularly in clinical settings.

Objective: This study aims to identify the predictors of dropout in a digital intervention for the treatment and prevention of depression in patients with comorbid CBP. We assessed which participant characteristics may be associated with dropout and whether intervention usage data could help improve the identification of individuals at risk of dropout early on in treatment.

Methods: Data were collected from 2 large-scale randomized controlled trials in which 253 patients with a diagnosis of CBP and major depressive disorder or subclinical depressive symptoms received a digital intervention for depression. In the first analysis, participants' baseline characteristics were examined as potential predictors of dropout. In the second analysis, we assessed the extent to which dropout could be predicted from a combination of participants' baseline characteristics and intervention usage variables following the completion of the first module. Dropout was defined as completing <6 modules. Analyses were conducted using logistic regression.

Results: From participants' baseline characteristics, lower level of education (odds ratio [OR] 3.33, 95% CI 1.51-7.32) and both lower and higher age (a quadratic effect; age: OR 0.62, 95% CI 0.47-0.82, and age2: OR 1.55, 95% CI 1.18-2.04) were significantly associated with a higher risk of dropout. In the analysis that aimed to predict dropout following completion of the first module, lower and higher age (age: OR 0.60, 95% CI 0.42-0.85; age2: OR 1.59, 95% CI 1.13-2.23), medium versus high social support (OR 3.03, 95% CI 1.25-7.33), and a higher number of days to module completion (OR 1.05, 95% CI 1.02-1.08) predicted a higher risk of dropout, whereas a self-reported negative event in the previous week was associated with a lower risk of dropout (OR 0.24, 95% CI 0.08-0.69). A model that combined baseline characteristics and intervention usage data generated the most accurate predictions (area under the receiver operating curve [AUC]=0.72) and was significantly more accurate than models based on baseline characteristics only (AUC=0.70) or intervention usage data only (AUC=0.61). We found no significant influence of pain, disability, or depression severity on dropout.

Conclusions: Dropout can be predicted by participant baseline variables, and the inclusion of intervention usage variables may improve the prediction of dropout early on in treatment. Being able to identify individuals at high risk of dropout from digital health interventions could provide intervention developers and supporting clinicians with the ability to intervene early and prevent dropout from occurring.

Keywords: adherence; attrition; back pain; comorbidity; depression; digital health; dropout; eHealth; internet intervention; law of attrition; mental health; mobile phone.

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Conflict of interest statement

Conflicts of Interest: SS has received fees for lectures or workshops from training institutes for psychotherapists in the context of digital health research. HB has received consultancy fees, fees for lectures or workshops from chambers of psychotherapists and training institutes for psychotherapists in the context of digital health research and license fees for an Internet-intervention. LBS has received consultancy fees, fees for lectures and workshops from chambers of psychotherapists and training institutes for psychotherapists in the context of digital health research. DDE has received consultancy fees/served in the scientific advisory board from several companies such as Minddistrict, Lantern, Novartis, Sanofi, Schoen Kliniken, Ideamed, German health insurance companies (BARMER, Techniker Krankenkasse) and a number of federal chambers for psychotherapy. He is a stakeholder of the Institute for health training online (GET.ON), which aims to implement scientific findings related to digital health interventions into routine care. The authors have no further conflicts to declare.

References

    1. Wu A, March L, Zheng X, Huang J, Wang X, Zhao J, Blyth FM, Smith E, Buchbinder R, Hoy D. Global low back pain prevalence and years lived with disability from 1990 to 2017: estimates from the Global Burden of Disease Study 2017. Ann Transl Med. 2020 Mar;8(6):299. doi: 10.21037/atm.2020.02.175. doi: 10.21037/atm.2020.02.175.atm-08-06-299 - DOI - PMC - PubMed
    1. GBD 2015 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016 Oct 08;388(10053):1545–602. doi: 10.1016/S0140-6736(16)31678-6. https://linkinghub.elsevier.com/retrieve/pii/S0140-6736(16)31678-6 S0140-6736(16)31678-6 - DOI - PMC - PubMed
    1. Bair MJ, Robinson RL, Katon W, Kroenke K. Depression and pain comorbidity: a literature review. Arch Intern Med. 2003 Nov 10;163(20):2433–45. doi: 10.1001/archinte.163.20.2433.163/20/2433 - DOI - PubMed
    1. Nicholas MK, Coulston CM, Asghari A, Malhi GS. Depressive symptoms in patients with chronic pain. Med J Aust. 2009 Apr 06;190(S7):S66–70. doi: 10.5694/j.1326-5377.2009.tb02473.x.nic10887_fm - DOI - PubMed
    1. Miles CL, Pincus T, Carnes D, Homer KE, Taylor SJ, Bremner SA, Rahman A, Underwood M. Can we identify how programmes aimed at promoting self-management in musculoskeletal pain work and who benefits? A systematic review of sub-group analysis within RCTs. Eur J Pain. 2011 Sep;15(8):775.e1–11. doi: 10.1016/j.ejpain.2011.01.016.S1090-3801(11)00033-4 - DOI - PubMed

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