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Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review

机译:现状与未来技术的生态瞬间评估和治疗重大抑郁症的干预:系统审查

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摘要

Ecological momentary assessment (EMA) and ecological momentary intervention (EMI) are alternative approaches to retrospective self-reports and face-to-face treatments, and they make it possible to repeatedly assess patients in naturalistic settings and extend psychological support into real life. The increase in smartphone applications and the availability of low-cost wearable biosensors have further improved the potential of EMA and EMI, which, however, have not yet been applied in clinical practice. Here, we conducted a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to explore the state of the art of technology-based EMA and EMI for major depressive disorder (MDD). A total of 33 articles were included (EMA = 26; EMI = 7). First, we provide a detailed analysis of the included studies from technical (sampling methods, duration, prompts), clinical (fields of application, adherence rates, dropouts, intervention effectiveness), and technological (adopted devices) perspectives. Then, we identify the advantages of using information and communications technologies (ICTs) to extend the potential of these approaches to the understanding, assessment, and intervention in depression. Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances in machine learning for affective modelling.
机译:生态瞬间评估(EMA)和生态瞬间干预(EMI)是回顾自我报告和面对面治疗的替代方法,并且他们可以反复评估自然环境中的患者,并将心理支持延长到现实生活中。智能手机应用的增加和低成本可穿戴生物传感器的可用性进一步提高了EMA和EMI的潜力,然而,尚未在临床实践中应用。在这里,我们使用优选的报告项目进行了系统的评价,用于系统评价和荟萃分析(PRISMA)指导方针,探讨了基于技术的EMA和EMI的艺术,以获得主要抑郁症(MDD)。共用了33篇文章(EMA = 26; EMI = 7)。首先,我们提供了从技术(采样方法,持续时间,提示),临床(应用领域,依从性率,辍学,干预效果)和技术(采用设备)视角的研究详细分析。然后,我们确定使用信息和通信技术(ICT)来扩展这些方法对抑郁症的理解,评估和干预的潜力的优势。此外,我们指出了仍然需要在这一领域讨论的相关问题,我们讨论了EMA和EMI如何从使用传感器和生物传感器中受益,以及机器学习对情感建模的进步。

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