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    Original Investigation
    Psychiatry
    July 17, 2020

    A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions

    Author Affiliations
    • 1Microsoft Research Cambridge, Cambridge, United Kingdom
    • 2Clinical Research & Innovation, SilverCloud Health, Dublin, Ireland
    • 3E-Mental Health Group, School of Psychology, Trinity College Dublin, Dublin, Ireland
    • 4Faculty of Computer Science The University of Vienna, Vienna, Austria
    • 5School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
    JAMA Netw Open. 2020;3(7):e2010791. doi:10.1001/jamanetworkopen.2020.10791
    Key Points español 中文 (chinese)

    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 54 604 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.

    Abstract

    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 54 604 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, 19 930 [36.5%]), class 2 (late engagers, 11 674 [21.4%]), class 3 (high engagers with rapid disengagement, 13 936 [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.

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