Research paper

Introduction The World Health Organization defines health as a state of complete physical, mental, and social well- being and not merely the absence of disease or infirmity. 1 Mental disorders present a substantial burden for good health as they have deleterious effects on the individual, society, and the worldwide economy, 2-4 making their prevention and treatment a public health priority. 5-7 Responding to the demand for accessible and sustainable mental health care services, internet- delivered psychological interventions offer access to evidence-based treatment and positive clinical outcomes while maintaining quality of care and reducing costs. 8,9 Extensive research has reported possible effectiveness of these interventions for treating psychological disorders. 9-13 However, more complete understanding of the clinical use of digital therapy programs requires further research. 14-16 Most previous studies explored the association between use of the interventions and outcomes, relying on single metrics, such as raw use counts. 17,18 Other studies suggest that single metrics are unlikely to sufficiently capture associations between engagement and outcomes, especially when compared with other factors, such as the actual level of attention or interactivity during an intervention. 19,20 Thus, identifying different behavioral patterns of engagement and linking these patterns to clinical outcomes offer new opportunities for personalizing treatment delivery to reduce nonadherence to therapy and enhance possible effectiveness. 20,21 The aim of this study was to examine whether different types of patient behaviors manifest in the way people engage with an internet-based cognitive behavioral therapy (iCBT) intervention for symptoms of depression and anxiety. We used machine learning to build a probabilistic graphical modeling framework to understand longitudinal patterns of engagement with iCBT. 22-24 We hypothesized that these patterns would allow us to infer distinct, heterogeneous patient behavior subtypes. We further hypothesized that these subtypes are associated with the intervention's success of improving mental health and that different subtypes of engagement are associated with differences in clinical outcomes. Methods Study Design We used clinical measures and behavioral engagement data from SilverCloud Health. SilverCloud Health is an evidence-based, online, self-administered platform that delivers iCBT alongside feedback from trained human supporters. 25,26 We used deidentified data from 67 468 patients on the Space From Depression and Anxiety treatment program between January 31, 2015, and March 31, 2019. We removed 12 864 individuals who had no supporter assigned and restricted analysis to the remaining 54 604 patients who viewed the program content at least once. The program consists of 8 core modules covering the CBT principles for treating symptoms of depression and anxiety. Content is delivered using textual and audiovisual materials, interactive tools, and personal stories. The platform includes several interactive tools, such as journal, quizzes, mood trackers, and other CBT-based exercises. Human supporters provide guidance to patients in the first 8 weeks of treatment. Further details of the platform and tools are available in the eMethods and eTable 1 in the Supplement. Data analysis was carried out between April 1 and October 31, 2019. All users provided written or oral consent for their anonymized data to be used in routine evaluations for service monitoring and improvement. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. Per the Common Rule, institutional review board review was not required for this study, which used deidentified publicly available data. Clinical Outcomes We assessed symptoms of depression using the Patient Health Questionnaire-9 (PHQ-9) and symptoms of anxiety using the Generalized Anxiety Disorder-7 (GAD-7). We used a PHQ-9 cut point JAMA Network Open | Psychiatry Machine Learning Approach to Understanding Patterns of Engagement for Mental Health Interventions JAMA Network Open. 2020;3(7):e2010791. doi:10.1001/jamanetworkopen.2020.10791 (Reprinted) July 17, 2020 2/12 Downloaded From: https://jamanetwork.com/ on 07/07/2023

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