3 - (SYM 41) Which Client with Generalized Anxiety Disorder Benefits from a Mindfulness Ecological Momentary Intervention versus a Self-monitoring App? Developing a Multivariable Machine Learning Model

Background: Precision medicine methods (machine learning; ML) can identify which clients with generalized anxiety disorder (GAD) benefit from mindfulness ecological momentary intervention (MEMI) vs. self-monitoring app (SM). Prior efforts harnessing ML tended to use narrow predictor sets to build multivariate models that might inform optimal treatment assignment. Testing a broader predictor set comprising baseline symptom severity, demographic variables, and theory-based predictors with ML models can help to optimally determine which digital mental health intervention (DMHI) app works best for which person with GAD.

Methods: We used randomized controlled trial data of MEMI vs. SM for GAD (N = 110) and tested three ML models to predict one-month follow-up reliable improvement in GAD severity, perseverative cognitions (PC), trait mindfulness (TM), and executive function (EF). Eleven baseline predictors were tested regarding differential reliable change from MEMI vs. SM (age, sex, race, EF errors, inhibitory dyscontrol, set-shifting deficits, verbal fluency, working memory, GAD severity, TM, PC). We tested all ML models using nested 10-fold cross-validation, which minimizes biased estimates of the true error.

Results: The final top five prescriptive predictor models of all outcomes performed well (area under the receiver operating characteristic curve [AUC] = 0.752–0.886). These variables predicted better outcome from MEMI vs. SM: Higher GAD severity predicted more GAD improvement but less EF improvement. Elevated PC, inhibitory dyscontrol, and verbal dysfluency predicted better improvement in most outcomes. Greater set-shifting and TM predicted stronger improvements in GAD symptoms and TM. Older age predicted more alleviation of GAD and PC symptoms. Women exhibited more enhancements in trait mindfulness and EF than men. Caucasians benefitted more than non-Caucasians.

Conclusions: PC, TM, EF, and sociodemographic data could help predictive models optimize intervention selection for GAD. If future studies replicate our pattern of results and with ethical considerations in mind, some clinical implications merit attention. The MEMI could benefit clients with GAD seeking treatment in busy outpatient settings such as academic medical centers with long waitlists and heavy client loads. Clinicians could collect client data from self-reports and brief neurocognitive tests, then input them into the algorithm using the top five predictive factors model to predict MEMI effectiveness as part of a prescriptive calculator.