Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Mathematical Data Science Program, Dartmouth College, Hanover, NH, United States.
Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States.
J Affect Disord. 2023 Jan 1;320:201-210. doi: 10.1016/j.jad.2022.09.112. Epub 2022 Sep 24.
Generalized anxiety disorder (GAD) is a prevalent mental health disorder that often goes untreated. A core aspect of GAD is worry, which is associated with negative health outcomes, accentuating a need for simple treatments for worry. The present study leveraged pretreatment individual differences to predict personalized treatment response to a digital intervention.
Linear mixed-effect models were used to model changes in daytime and nighttime worry duration and frequency for 163 participants who completed a six-day worry postponement intervention. Ensemble-based machine learning regression and classification models were implemented to predict changes in worry across the intervention. Model feature importance was derived using SHapley Additive exPlanation (SHAP).
Moderate predictive performance was obtained for predicting changes in daytime worry duration (test r = 0.221, AUC = 0.77) and nighttime worry frequency (test r = 0.164, AUC = 0.72), while poor predictive performance was obtained for nighttime worry duration and daytime worry frequency. Baseline levels of worry and subjective health complaints were most important in driving model predictions.
A complete-case analysis was leveraged to analyze the present data, which was collected from participants that were Dutch and majority female.
This study suggests that treatment response to a digital intervention for GAD can be accurately predicted using baseline characteristics. Particularly, this worry postponement intervention may be most beneficial for individuals with high baseline worry but fewer subjective health complaints. The present findings highlight the complexities of and need for further research into daily worry dynamics and the personalizable utility of digital interventions.
广泛性焦虑障碍（GAD）是一种普遍存在的心理健康障碍，往往得不到治疗。GAD 的一个核心方面是担忧，它与负面健康结果有关，因此需要对担忧进行简单的治疗。本研究利用治疗前的个体差异来预测数字干预对个性化治疗的反应。
线性混合效应模型用于对 163 名完成六天担忧推迟干预的参与者的日间和夜间担忧持续时间和频率的变化进行建模。实施基于集成的机器学习回归和分类模型来预测干预过程中担忧的变化。使用 SHapley Additive exPlanation (SHAP) 推导出模型特征重要性。
对于预测日间担忧持续时间（测试 r=0.221，AUC=0.77）和夜间担忧频率（测试 r=0.164，AUC=0.72）的变化，获得了中等的预测性能，而对于夜间担忧持续时间和日间担忧频率的变化，获得了较差的预测性能。担忧和主观健康抱怨的基线水平对驱动模型预测最重要。
本研究利用完整案例分析来分析从荷兰和女性居多的参与者那里收集的数据，这可能会导致偏差。
这项研究表明，使用基线特征可以准确预测 GAD 的数字干预治疗反应。特别是，这种担忧推迟干预可能对基线担忧较高但主观健康抱怨较少的个体最有益。本研究结果强调了进一步研究日常担忧动态和数字干预个性化效用的复杂性和必要性。
