Abstract
Given the recent increase in transdiagnostic research, it is important to discern how dimensional models of psychopathology could be used to guide personalized, dynamic assessment and treatment of symptoms. Using the person-specific approach described by Fisher (2015), we devised an initial 4-step algorithm for devising a treatment plan based on modular cognitive behavioral therapy using results obtained from within-person factor analyses and dynamic factor models. Then, we describe the improvement and digitization of the algorithm, termed Dynamic Assessment Treatment Algorithm (DATA). The development, structure, and clinical implications of DATA are discussed.
Highlights
For decades researchers have sought to identify dimensional, transdiagnostic factors that account for the pronounced covariation found among psychological disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM) [1]
More recently there has been an increase in transdiagnostic research and ─ with the advent of the National Institute of Mental Health (NIMH)’s Research Domain Criteria Project (RDoC) ─ a structural shift toward investigating dimensional models of psychopathology
The resulting algorithm, termed Dynamic Assessment Treatment Algorithm (DATA), uses a series of equations to (a) quantify the strength of each factor based on its predictive power within time and across time via a factor score, (b) utilize standardized factor loadings to quantify the strength of association between the factor scores and each item, (c) weight the item/factor relationship by mean level of each item, (d) and map items onto specific interventions via an item-module matching matrix
Summary
Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA). OPEN ACCESS Citation: Fernandez KC, Fisher AJ, Chi C (2017) Development and initial implementation of the Dynamic Assessment Treatment Algorithm (DATA). Data Availability Statement: The data is publicly available via the Open Science Framework (OSF) website: https://osf.io/xke9b/.
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