Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany.
J Med Internet Res. 2022 Aug 30;24(8):e38261. doi: 10.2196/38261.
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.
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.
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.
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 age: 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; age: 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.
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.
抑郁症是慢性背痛（CBP）患者常见的合并症，导致治疗效果较差和增加医疗并发症。数字干预措施已被证明在预防和治疗抑郁症方面有效；然而，高辍学率是一个主要挑战，尤其是在临床环境中。
本研究旨在确定针对 CBP 合并症患者治疗和预防抑郁症的数字干预措施中辍学的预测因素。我们评估了哪些参与者特征可能与辍学有关，以及干预使用数据是否可以帮助在治疗早期更早地识别有辍学风险的个体。
数据来自两项大型随机对照试验，其中 253 名患有 CBP 和重度抑郁症或亚临床抑郁症状的患者接受了抑郁症的数字干预。在第一次分析中，检查了参与者的基线特征，作为辍学的潜在预测因素。在第二次分析中，我们评估了从参与者的基线特征和干预使用变量的组合中，可以从多大程度上预测在完成第一个模块后的辍学。辍学被定义为完成<6 个模块。分析使用逻辑回归进行。
从参与者的基线特征来看，较低的教育水平（优势比[OR]3.33，95%置信区间[CI]1.51-7.32）和较低及较高的年龄（二次效应；年龄：OR 0.62，95%CI 0.47-0.82，年龄：OR 1.55，95%CI 1.18-2.04）与较高的辍学风险显著相关。在旨在预测完成第一个模块后的辍学的分析中，较低和较高的年龄（年龄：OR 0.60，95%CI 0.42-0.85；年龄：OR 1.59，95%CI 1.13-2.23）、中等与较高的社会支持（OR 3.03，95%CI 1.25-7.33）和完成模块的天数较多（OR 1.05，95%CI 1.02-1.08）预测辍学风险较高，而在前一周报告的负面事件与较低的辍学风险相关（OR 0.24，95%CI 0.08-0.69）。结合基线特征和干预使用数据的模型产生了最准确的预测（接受者操作特征曲线下面积[AUC]为 0.72），并且明显优于仅基于基线特征（AUC=0.70）或仅基于干预使用数据（AUC=0.61）的模型。我们没有发现疼痛、残疾或抑郁严重程度对辍学有显著影响。
可以通过参与者的基线变量预测辍学，并且包含干预使用变量可能会提高治疗早期辍学的预测能力。能够从数字健康干预措施中识别出高辍学风险的个体，可以为干预措施的开发者和支持临床医生提供早期干预的能力，从而防止辍学的发生。
