For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era. PBT and the Individual 4 The Role of the Individual in the Coming Era of Process-Based Therapy Questioning assumptions in science is disruptive. Within a defined area of study, a priori analytic assumptions provide the scaffolding for which questions are asked, which methods are used, and which data are deemed relevant. Professionals often view questions, methods, and analytic units simply as the required tools of good science-not reflections of assumptions-and as a result there can be a sense of disorientation when times of upheaval arrive and assumptions are pointed out and critically examined. So it is today within the domain of mental health, and the intervention science linked to it. For decades is has been assumed that a satisfactory field of evidence-based treatment could emerge based on adequate experimental tests of protocols focused on psychiatric syndromes. This protocols for syndromes era had a coherent set of key strategic assumptions built into its scientific and public health strategy-but every one of them is now being openly questioned. At the same time, a powerful alternative strategic agenda is emerging that echoes some of the process-based and idiographic assumptions of the earliest days of behavioral research, as well as the therapy based upon it. We are reminded of that history by the very name of this, the oldest of all of the applied behavioral journals. However, revitalization of the study of change processes that apply idiographically is not a mere repeat of the past, since it encompasses questions, methods, and data that are distinct and new (Hayes & Hofmann, 2017, 2018; Hofmann & Hayes, 2018).
Historically speaking, the behavioral tradition advanced functional analysis as a method of applying existing principles to novel situations. In the more than half a century since that idea was advanced, functional analysis has either fallen into disuse, as in most of applied psychology, or has been used but modified to a point that is virtually inapplicable elsewhere, as in applied behavior analysis work with severe developmental disabilities. In this paper we argue that the current challenges with COVID-19 present an ideal time to reinvigorate functional analysis by combining it with the growing body of evidence on processes of change, organized under an extended evolutionary meta-model. This new form of process-based functional analysis takes advantage of the strengths of contextual behavioral science, while opening avenues of fruitful interaction with other wings of intervention and evolutionary science more generally. Using the psychological flexibility model as an example, we show how this approach solves the key problems of classical functional analysis and helps professionals deal with novel challenges such as those posed by COVID-19. Humanity is now facing an extraordinary and unexpected situation. Behavioral science needs to rise to that challenge in a way that provides both immediate practical value and greater assurance of long-term benefits for our understanding of human complexity more generally. Process-based functional analysis can be a vehicle to do just that.
This article presents preliminary findings from use of a novel computer program that implements an evidence-based psychological intervention to treat depression based on behavioral activation (BA) therapy. The program is titled "Building a Meaningful Life Through Behavioral Activation". The findings derive from an open trial with moderate to severely depressed individuals (N = 15) in an Intention to Treat sample. Hierarchical linear modeling (HLM) analyses revealed significant change over time on Beck Depression Inventory-Second Edition (BDI-II) scores, Revised Hamilton Depression Rating Scale scores, and significant contribution to BDI-II score variance by participant age over time, change over time in negative automatic thoughts, and change over time in BA scores. Piecewise HLM analyses revealed that significant change over time was associated uniquely with active treatment and not during 3 weeks of baseline measurement. In addition to treatment-associated significant change on all dependent measures over time, effect sizes were in the moderate to large range. Limitations are small sample size, nonrandomized control, research-recruited patients instead of purely treatment-seeking patients, possible rating bias by independent assessors who had knowledge that participants had received active treatment in this open trial, and the influence of additional services received in the post acute-treatment phase by some participants could have contributed to maintenance of gains reported for that period.
Background. Identifying the most important psychological drivers of well-being for a particular individual is critical to developing personalised interventions. Methods. We utilised three, intensive daily diary studies (within person measurement occasions N >50) across three data sets (n1=44; n2=37; n3=141) to examine within-person associations between clinically-relevant processes and a variety of outcomes. We utilised a novel idiographic algorithm, ”i-ARIMAX,” to calculate the strength of relationship (beta) between every process and every outcome within individuals. We then submitted all betas to meta-analytic methods. Results. All process-outcome links were highly heterogeneous between individuals. Processes that were associated with positive outcomes for some people were often unrelated to outcomes for others or associated with negative outcomes. Conclusion. i-ARIMAX might be used to guide personalised interventions and to reduce the number of candidate variables for complicated within-person analysis.
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