Personalized Prescriptions of Therapeutic Skills from Patient Characteristics: An Ecological Momentary Assessment Approach
Objective: Rather than relying on a single psychotherapeutic orientation, most clinicians draw from a range of therapeutic approaches to treat their clients. To date, no data-driven approach exists for personalized predictions of which skill domain would be most therapeutically beneficial for a given patient. The present study combined ecological momentary assessment (EMA) and machine learning to test a data-driven approach for predicting patient-specific skill-outcome associations.Method: Fifty (Mage= 37 years old, 54% female, 84% White) adults received training in Behavioral Therapy (BT) and Dialectical Behavior Therapy (DBT) skills within a behavioral health partial hospital program. Following discharge, patients received four EMA surveys per day for two weeks (total observations = 2,036) assessing use of therapeutic skills and positive/negative affect (PA/NA). Clinical and demographic characteristics were submitted to elastic net regularization to predict, via cross-validation, patient-specific associations between use of BT vs. DBT skills and level of PA/NA.Results: Cross-validated accuracy was 81% (sensitivity=93%; specificity=63%) in predicting whether a patient would exhibit a stronger association between use of BT vs. DBT skills and PA level. Predictors of positive DBT skills-PA associations included higher levels of non-suicidal self-injury and sleep disturbance, whereas predictors of positive BT skills-PA relations included higher emotional lability and anxiety disorder comorbidity, and lower psychomotor retardation/agitation and worthlessness/guilt. Corresponding models with NA yielded no predictors.