A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions
A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions
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DOI：
10.1001/jamanetworkopen.2020.10791
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发表时间：
2020-07-17
影响因子：
13.8
通讯作者：
Belgrave, Danielle
中科院分区：
文献类型：
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作者：
Chien, Isabel;Enrique, Angel;Belgrave, Danielle
机器学习技术能否用于识别患者参与基于互联网的认知行为疗法治疗抑郁和焦虑症状的异质性？在这项队列研究中，使用了54 & x202f；根据患者参与在线干预的情况，确定了604个个体，5个异质亚型。这些亚型与不同的患者行为模式和不同程度的抑郁和焦虑症状改善有关。意义本研究结果表明，患者行为模式可能阐明不同的参与方式，这有助于为患者进行更好的分诊，提供个性化的治疗活动，有助于改善结果，减轻精神健康障碍的总体负担。参与网络传递的心理干预与抑郁和焦虑症状相关的机制尚不清楚。目的根据人们如何参与基于互联网的认知行为疗法（iCBT）干预抑郁和焦虑症状来识别行为类型。设计、设置和参与者：54 & x202f；从2015年1月31日至2019年3月31日，604名被分配到“远离抑郁和焦虑”治疗项目的成年患者，基于iCBT参与模式的纵向异质性，使用机器学习技术进行概率潜变量建模，以推断不同的患者亚型。干预：临床医生支持的基于icbt的项目，遵循治疗抑郁和焦虑的临床指南，在web 2.0平台上交付。记录用户与iCBT程序交互的数据，以告知用户随时间的参与模式。临床结果包括抑郁症状（患者健康问卷-9 [PHQ-9]）和焦虑症状（广泛性焦虑障碍-7 [GAD-7]）；以PHQ-9≥10分、GAD-7≥8分作为抑郁和焦虑的判定标准。结果患者在平台上平均（SD）花费111.33（118.92）分钟，完成230.60（241.21）个工具。基线时，平均PHQ-9评分为12.96 (5.81)，GAD-7评分为11.85（5.14）。根据患者在14周内与不同项目部分的互动，确定了5种参与类型：1类（低参与，19 & x202f;930[36.5%]）， 2类（后期参与，11 & x202f;674[21.4%]）， 3类（快速脱离的高参与，13 & x202f;936[25.5%]）， 4类（中度减少的高参与，3258[6.0%]），和5类（最高参与，5799[10.6%]）。PHQ-9评分的估计平均下降（SE）为第3类6.65(0.14)，第5类5.88(0.14)，第4类5.39 (0.14)；2类降低率最低，为-4.41（0.13）。与第1类的PHQ-9分数下降相比，第2类的科恩效应量（SE）为-0.46(0.014)，第3类为-0.46(0.014)，第4类为-0.61(0.021)，第5类为-0.73（0.018）。GAD-7在各组之间也发现了类似的模式。本研究的结果可能有助于根据抑郁和焦虑个体的特定亚型定制干预措施。向支持者告知临床决策需求可能是成功采用机器学习见解的一条途径，从而整体改善临床结果。这项队列研究考察了机器学习在测试中的使用，以对抑郁症和焦虑症患者的行为类型进行分类。
Question Can machine learning techniques be used to identify heterogeneity in patient engagement with internet-based cognitive behavioral therapy for symptoms of depression and anxiety? Findings In this cohort study using data from 54 & x202f;604 individuals, 5 heterogeneous subtypes were identified based on patient engagement with the online intervention. These subtypes were associated with different patterns of patient behavior and different levels of improvement in symptoms of depression and anxiety. Meaning The findings of this study suggest that patterns of patient behavior may elucidate different modalities of engagement, which can help to conduct better triage for patients to provide personalized therapeutic activities, helping to improve outcomes and reduce the overall burden of mental health disorders.Importance The mechanisms by which engagement with internet-delivered psychological interventions are associated with depression and anxiety symptoms are unclear. Objective To identify behavior types based on how people engage with an internet-based cognitive behavioral therapy (iCBT) intervention for symptoms of depression and anxiety. Design, Setting, and Participants Deidentified data on 54 & x202f;604 adult patients assigned to the Space From Depression and Anxiety treatment program from January 31, 2015, to March 31, 2019, were obtained for probabilistic latent variable modeling using machine learning techniques to infer distinct patient subtypes, based on longitudinal heterogeneity of engagement patterns with iCBT. Interventions A clinician-supported iCBT-based program that follows clinical guidelines for treating depression and anxiety, delivered on a web 2.0 platform. Main Outcomes and Measures Log data from user interactions with the iCBT program to inform engagement patterns over time. Clinical outcomes included symptoms of depression (Patient Health Questionnaire-9 [PHQ-9]) and anxiety (Generalized Anxiety Disorder-7 [GAD-7]); PHQ-9 cut point greater than or equal to 10 and GAD-7 scores greater than or equal to 8 were used to define depression and anxiety. Results Patients spent a mean (SD) of 111.33 (118.92) minutes on the platform and completed 230.60 (241.21) tools. At baseline, mean PHQ-9 score was 12.96 (5.81) and GAD-7 score was 11.85 (5.14). Five subtypes of engagement were identified based on patient interaction with different program sections over 14 weeks: class 1 (low engagers, 19 & x202f;930 [36.5%]), class 2 (late engagers, 11 & x202f;674 [21.4%]), class 3 (high engagers with rapid disengagement, 13 & x202f;936 [25.5%]), class 4 (high engagers with moderate decrease, 3258 [6.0%]), and class 5 (highest engagers, 5799 [10.6%]). Estimated mean decrease (SE) in PHQ-9 score was 6.65 (0.14) for class 3, 5.88 (0.14) for class 5, and 5.39 (0.14) for class 4; class 2 had the lowest rate of decrease at -4.41 (0.13). Compared with PHQ-9 score decrease in class 1, the Cohen d effect size (SE) was -0.46 (0.014) for class 2, -0.46 (0.014) for class 3, -0.61 (0.021) for class 4, and -0.73 (0.018) for class 5. Similar patterns were found across groups for GAD-7. Conclusions and Relevance The findings of this study may facilitate tailoring interventions according to specific subtypes of engagement for individuals with depression and anxiety. Informing clinical decision needs of supporters may be a route to successful adoption of machine learning insights, thus improving clinical outcomes overall.This cohort study examines the use of machine learning in testing to categorize behavior types in individuals with depression and anxiety.