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J Clin Child Adolesc Psychol. 2021 May-Jun;50(3):385-399. doi: 10.1080/15374416.2019.1703711. Epub 2020 Jan 7.
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Predicting treatment failure in regular care Internet-Delivered Cognitive Behavior Therapy for depression and anxiety using only weekly symptom measures.仅使用每周症状测量值预测常规护理互联网提供的认知行为疗法治疗抑郁症和焦虑症的治疗失败。
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辅助分析以支持适应性预防干预措施的制定。

Secondary Analysis to Inform the Development of Adaptive Preventive Interventions.

机构信息

Department of Human Development and Family Studies, Michigan State University, 522 West Circle Drive, East Lansing, MI, 48823, USA.

Department of Public Health Sciences, University of Miami, Miami, FL, USA.

出版信息

Clin Child Fam Psychol Rev. 2022 Dec;25(4):646-657. doi: 10.1007/s10567-022-00408-1. Epub 2022 Aug 4.

DOI:10.1007/s10567-022-00408-1
PMID:35925439
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10153946/
Abstract

For the past 30 years, scholars across the fields of epidemiology, education, psychology, and numerous other fields have worked to develop interventions designed to reduce risk and enhance protection to prevent mental, emotional, and behavioral problems across the lifespan. This article presents a series of next steps that leverage this foundational science to inform the development of adaptive preventive interventions. Adaptive preventive interventions (APIs) tailor the intervention to fit the diverse, sometimes changing, needs of participants with the goal of better prevention outcomes for more individuals. Secondary analyses of data from preventive intervention trials to identify moderators, mediators, and antecedents of attrition and intervention failure can be useful for designing effective APIs. Moderators that identify intervention effect heterogeneity can be used within an API to tailor the intervention to meet the unique needs of important participant subgroups. Mediators and predictors of disengagement and attrition can be helpful tailoring variables in an API to trigger change to the intervention. Preventive intervention trials that incorporate frequent assessment of potential mediators, moderators, and antecedents of attrition during the intervention period are needed. Secondary analyses of data from preventive intervention trials provide an important foundation for next-generation APIs.

摘要

在过去的 30 年里,流行病学、教育、心理学等多个领域的学者们一直在努力开发干预措施,旨在降低风险,增强保护,预防全生命周期的心理、情感和行为问题。本文提出了一系列后续步骤,利用这一基础科学为适应性预防干预措施的制定提供信息。适应性预防干预措施 (APIs) 使干预措施适应参与者多样化、有时变化的需求,目的是为更多人提供更好的预防效果。对预防干预试验数据的二次分析可以识别出辍学和干预失败的调节因素、中介因素和前因,这对设计有效的 API 很有用。可以在 API 中使用识别干预效果异质性的调节因素,根据重要参与者亚组的独特需求调整干预措施。脱离和辍学的中介因素和预测因素可以帮助在 API 中调整变量,以触发对干预措施的改变。需要在干预期间定期评估潜在的中介因素、调节因素和辍学前因的预防干预试验。对预防干预试验数据的二次分析为下一代 API 提供了重要基础。