Amelia J Scott, Jennie Walker, Eyal Karin, Milena Gandy, Joanne Dudeney, Andreea I Heriseanu, Madelyne A Bisby, Louise Sharpe, Shehzad Ali, Nickolai Titov, Blake F Dear
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Demographic (e.g., age, gender, employment, education), clinical (e.g., health condition, medication usage, multimorbidity), psychological (e.g., baseline symptom severity), and treatment-related variables were examined. Outcomes included clinically meaningful response (≥ 50% improvement in symptoms) and deterioration (≥ 30% increase in symptoms) in depression, anxiety, and self-reported disability. Multivariable regression models were built to identify significant predictor variables of response and deterioration. Additional indicators of model fit were reported, including the area under the curve and Negelkerke's <i>R</i>².</p><p><strong>Results: </strong>Several predictors of response and deterioration were identified within individual models; however, few predictors were significant across > 1 outcome, while none were consistent across all three outcomes. 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引用次数: 0
摘要
简介：本研究旨在确定参与者和治疗相关的特征，这些特征与慢性健康状况患者的互联网干预的反应和恶化有关。了解这些因素对于这些治疗方法的开发和实施至关重要。方法：数据来自一项随机对照试验，该试验是一项针对慢性疾病患者的跨诊断、互联网提供的干预措施（N = 590）。检查了人口统计学（如年龄、性别、就业、教育）、临床（如健康状况、药物使用、多病）、心理（如基线症状严重程度）和治疗相关变量。结果包括有临床意义的缓解（症状改善≥50%）和恶化（症状加重≥30%）的抑郁、焦虑和自我报告的残疾。建立多变量回归模型以确定反应和恶化的显著预测变量。报告了模型拟合的其他指标，包括曲线下面积和Negelkerke’s R²。结果：在单个模型中确定了几个预测反应和恶化的因素；然而，很少有预测因子在bbb1结局中具有显著性，而在所有三个结局中没有一个是一致的。预测参与者结果的最终模型解释了7%-16%的反应或恶化可能性差异。结论：结果表明，很少有预测因素与受试者对治疗的反应或恶化有意义相关。令人鼓舞的是，潜在的预后变量（例如，更严重的基线症状和多病）也不显著。研究结果支持这种治疗方法的广泛适用性，以及在阶梯式护理框架中远程提供干预措施的作用。（PsycInfo Database Record (c) 2025 APA，版权所有）。
Exploring predictors of clinical response to a transdiagnostic, internet-delivered psychological intervention for people with chronic health conditions.
Introduction: This study aims to identify participant- and treatment-related characteristics associated with response and deterioration in an internet-delivered intervention for people with chronic health conditions. Understanding these factors is critical for the development and delivery of these treatments.
Method: Data were drawn from a randomized controlled trial of a transdiagnostic, internet-delivered intervention for people with chronic health conditions (N = 590). Demographic (e.g., age, gender, employment, education), clinical (e.g., health condition, medication usage, multimorbidity), psychological (e.g., baseline symptom severity), and treatment-related variables were examined. Outcomes included clinically meaningful response (≥ 50% improvement in symptoms) and deterioration (≥ 30% increase in symptoms) in depression, anxiety, and self-reported disability. Multivariable regression models were built to identify significant predictor variables of response and deterioration. Additional indicators of model fit were reported, including the area under the curve and Negelkerke's R².
Results: Several predictors of response and deterioration were identified within individual models; however, few predictors were significant across > 1 outcome, while none were consistent across all three outcomes. The final models predicting participant outcomes explained 7%-16% of the variance in the likelihood of response or deterioration.
Conclusion: Results suggest that few predictors are meaningfully associated with participants' response or deterioration to treatment. Encouragingly, potentially prognostic variables (e.g., more severe baseline symptoms and multimorbidity) were also nonsignificant. The results support the broad applicability of this treatment approach and for the role of remotely delivered interventions within stepped care frameworks. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
期刊介绍：
Health Psychology publishes articles on psychological, biobehavioral, social, and environmental factors in physical health and medical illness, and other issues in health psychology.
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