Introduction
In recent years, the field of psychotherapy has seen a rise of novel transdiagnostic treatments that have the potential to provide easier access to evidence-based mental health care by addressing a wider range of disorders and lowering barriers to treatment (e.g., Dalgleish et al.,
2020; Sauer-Zavala, et al.,
2017a,
2017b; Schaeuffele, et al.,
2021b). Transdiagnostic treatments show promising effects when delivered face-to-face and over the internet (Newby et al.,
2015; Păsărelu et al.,
2017; Pearl & Norton,
2017), with equivalence to gold-standard single disorder protocols (e.g., Barlow et al.,
2017; Pearl & Norton,
2017). However, overall — across single disorder and transdiagnostic treatments — there remains a need to improve effectiveness and response rates. A better understanding of the active ingredients of psychotherapy can help to create more effective treatments and lower non-response rates (Kazdin,
2007). With the emergence of personalized and modularized treatments, knowledge on change mechanisms can also help to create a better fit between therapy and patient (Hofmann & Hayes,
2019). However, after years of research and advancements, the factors underlying change are still poorly understood (Cuijpers et al.,
2019; Kazdin,
2007). This may be due to the fact that investigating mechanisms of change is a complicated endeavor. Determining whether a factor constitutes a (causal) mechanism of change requires a theoretical foundation of putative mechanisms to guide the investigation, the establishment of temporal precedence (change occurs first in the mediator followed by change in symptoms), the inclusion of several mediators, and ideally a manipulation of the mediating variable (Cuijpers et al.,
2019; Kazdin,
2007).
A common distinction in mechanisms of change is between common and specific factors. Common factors are active across virtually all therapies and settings, whereas specific factors, as the name suggests, are more treatment-specific (Cuijpers et al.,
2019; Wampold,
2015). The most robust evidence on common factors has been found for therapeutic alliance and treatment expectancy (Baier et al.,
2020; Constantino et al.,
2018; Flückiger et al.,
2018). Therapeutic alliance and treatment expectancy have also been shown to predict and mediate outcomes in the internet-based setting (Berger,
2017; Boettcher et al.,
2013; El Alaoui et al.,
2015,
2016; Flückiger et al.,
2018; Probst et al.,
2019). In comparison, the body of research on relevant specific factors is less clear (e.g., Domhardt et al.,
2020; Kazantzis et al.,
2018). The number of potential candidate mechanisms impedes the study of specific factors, with every disorder-specific model encompassing specific processes that cause or maintain the disorder. Evidence on how these specific processes are related to outcome is difficult to aggregate and summarize across disorders. In contrast, the transdiagnostic approach, with its emphasis on shared mechanisms that should be targeted in treatment, provides an attractive framework for investigating mediating variables (Sauer-Zavala, et al.,
2017b).
The Unified Protocol (UP) is one example of a theory-driven transdiagnostic treatment for emotional disorders (Barlow et al.,
2016; Ellard et al.,
2010). The authors hypothesize that individuals with emotional disorders share a tendency to frequently and intensely experience negative affect and exhibit maladaptive reactions to these high negative emotions (Barlow et al.,
2014; Bullis et al.,
2019). The UP aims at changing these dysfunctional reactions towards emotions by increasing mindful emotion awareness as well as cognitive flexibility and decreasing avoidance (Barlow et al.,
2004). These processes are increasingly recognized as transdiagnostic mediators of change across CBT treatments (e.g., Eustis et al.,
2016; Goldin et al.,
2016; Kocovski et al.,
2015).
First studies started to investigate transdiagnostic processes in relation to outcome within the UP framework. As an important prerequisite, the modules of psychoeducation, mindful emotion awareness, cognitive flexibility, and countering emotional behaviors led to changes in these putative mechanisms (Sauer-Zavala, et al.,
2017a). Single case experimental design studies found that mindfulness, cognitive flexibility, and anxiety sensitivity were associated with outcome (Boswell,
2013; Boswell et al.,
2014; Brake et al.,
2016). Findings on avoidance are mixed. In single case experimental design studies, experiential avoidance did not exhibit substantial changes following treatment (Boswell et al.,
2014; Brake et al.,
2016) and did not predict or proceed changes in anxiety or depression in an individual with comorbid depression and generalized anxiety (Boswell et al.,
2014). In a larger RCT, Eustis et al. (
2019) investigated the mediating effect of experiential avoidance across the UP and single-disorder treatment protocols for mixed anxiety disorders. In contrast to these single case studies, experiential avoidance mediated and preceded changes across the UP and single-disorder protocols, suggesting that experiential avoidance is a general transdiagnostic mechanism in CBT (Eustis et al.,
2019). One meta-analysis aggregated the UP’s effects on emotion regulation (Sakiris & Berle,
2019). They differentiated between adaptive (e.g., mindfulness, reappraisal) and maladaptive (e.g. experiential avoidance, thought suppression) emotion regulation skills (Sakiris & Berle,
2019). Overall, they found preliminary evidence that adaptive strategies increased moderately over the course of treatment and that there were moderate to large effects on experiential avoidance and negligible effects on thought suppression.
Taken together, while there is preliminary evidence for the putative transdiagnostic mechanisms in the UP, the majority of findings are correlational, limited to anxiety disorders, and stem from small sample sizes. Only Eustis et al. (
2019) investigated mediating effects in a larger sample, but their investigation was limited to one transdiagnostic process.
Our primary goal for this study was to extend previous findings on the relevance of transdiagnostic processes, specifically mindfulness, cognitive flexibility, anxiety sensitivity, and avoidance, in the UP in an internet-based setting for emotional disorders beyond anxiety disorders. We were interested whether these transdiagnostic processes were malleable by treatment and to which extent they mediated treatment effects — both in isolation and in a multiple mediator model taking into account their interrelation.
Discussion
This study investigated transdiagnostic processes in an internet-based intervention based on the Unified Protocol for anxiety, depressive, and somatic symptom disorders. All transdiagnostic processes were malleable by treatment with medium to large effect sizes. In single mediator models, we found that cognitive flexibility, mindfulness, experiential avoidance, and behavioral activation partially mediated the relationship between treatment and symptom distress. When all significant mediators were collapsed into one model, the multiple mediation model revealed the indirect paths through mindfulness and cognitive flexibility as significant.
Results from the single mediator models are in line with previous research on the UP that found mindfulness, cognitive flexibility, and experiential avoidance predictive and associated with symptom change (Boswell et al.,
2014; Brake et al.,
2016; Eustis et al.,
2019). Anxiety sensitivity — while increasingly conceptualized as a transdiagnostic process and found to be associated with symptom change in the UP (Boettcher et al.,
2016; Boswell et al.,
2013) — might play a lesser role in depressive disorders and thus, may not be related to outcome to the same extent in our fairly depressed sample. Future studies should investigate whether these findings on transdiagnostic processes are dependent on diagnosis, as suggested by a recent study on mindfulness as a predictor of outcome in the UP (Woods et al.,
2020). The non-significant finding on anxiety sensitivity also raises the question of whether all participants benefit from the interoceptive exposure module that targets anxiety sensitivity. This highlights the potential of a flexible, modular application of the UP, where an individualized sequence of treatment components could be provided based on transdiagnostic processes (Fisher et al.,
2019; Sauer-Zavala et al.,
2018).
To our knowledge, transdiagnostic processes have not been investigated jointly in one mediation model for the UP as of yet. Mindfulness and cognitive flexibility, as measured by the SMQ and the reappraisal subscale of the ERQ, might capture the overarching aim of the UP best with their focus on how individuals react to distressing thoughts and images (and by that probably also emotions). These findings also tap into the debate whether it is more beneficial to decrease maladaptive or increase adaptive emotion regulation strategies in treatment (e.g., Southward et al.,
2021). In our study, the adaptive emotion regulation processes — mindfulness, cognitive flexibility, and behavioral activation — seemed to show more stable indirect effects than experiential avoidance (and anxiety sensitivity).
The disentanglement of transdiagnostic processes seems quite complex, given they are all related, overlapping, and may share a common underlying basis (e.g., Mansell & McEvoy,
2017; Spinhoven et al.,
2017). For instance, mindfulness training seems to facilitate exposure and decrease avoidance in some participants treated with the UP, highlighting the interplay and carryover effects between processes (Curreri et al.,
2020). CBT treatments have also shown to be mediated by transdiagnostic processes without explicitly targeting them, for example by mindfulness (e.g., Goldin et al.,
2016; Kocovski et al.,
2015) or experiential avoidance (e.g., Eustis et al.,
2016). Thus, based on our results, we cannot conclude that other treatment elements, besides mindfulness training and cognitive restructuring, are obsolete, since modules targeting avoidance may also enhance mindful emotion awareness and cognitive flexibility. These findings also challenge our theory-driven decision to measure mechanisms following the module that target them. Instead, additional research on the structure and hierarchy of transdiagnostic processes as well as dismantling component studies are needed to get a better understanding of the interplay of the processes in therapy and isolate effects.
Our findings need to be interpreted in the light of several limitations. First, the trial suffered from a high rate of missing values. Besides participants in the treatment group failing to fill out post-treatment assessments, dropout from the intervention affected the assessment of the transdiagnostic processes in the later modules. In order to ensure that participants received the proper dosage of treatment, we measured the transdiagnostic processes following the module that targeted them. But since the treatment was self-paced and the mean number of modules completed was
M = 7.22 (Schaeuffele et al.,
2020), this naturally meant that the assessments after completion of module 7 and 9 were limited to participants who progressed to this stage of treatment. Thus, our findings are preliminary and should be replicated with larger samples. Our sample size also did not permit us to analyze mediation in different subsamples, e.g. responders versus non-responders. However, we believe that examining mechanisms of change particularly in those patients experiencing change is a promising route of future research (DeRubeis et al.,
2014). The dropout also limits the generalizability of our results. We have several hypotheses as to why participants might have not completed the intervention. The self-paced setup and “one size fits all” transdiagnostic approach of the intervention may have been overwhelming and lacked personalization for participants, especially in a guided self-help context. In addition, the lack of adherence-fostering features like personalized reminders and progress reinforcement, as they are implemented in other guided internet-based interventions, may have been detrimental to adherence. Future applications could address these concerns by delivering the intervention in a personalized, modular fashion and including adherence-fostering features. Another limitation concerns the lack of temporal precedence that has been described as an important prerequisite for causation (Kazdin,
2007). When comparing treatment and waitlist, temporal precedence could not be established with how measurements were set up. While several other of the quality criteria of mediation studies outlined by Lemmens et al. (
2016) could be established (RCT with control group,
n > 40 per group, assessment of several mediators), the lack of temporal precedence does not allow to draw any conclusions on causation. However, internet-based treatments are shorter in comparison to face-to-face and it seems to take time for the effect on transdiagnostic processes to unfold as the delayed changes in mindfulness and cognitive flexibility indicate. These circumstances complicate the establishment of temporality. Our results were obtained within an internet-based delivery of the UP. While the face-to-face and internet-based setting generally seem to share findings on relevant mechanisms of change (e.g., for depression: Domhardt et al.,
2020; Lemmens et al.,
2016), we cannot rule out that setting-specific influences affected our findings and they, thus, may not translate to the face-to-face setting to the full extent. Direct comparisons between face-to-face and internet-based deliveries and their accompanying change mechanisms are sparse and should be subject of future studies. Linear mediation models, as employed in our study, may not be suitable to capture the complexity of therapeutic processes, since the relationship between outcome and mediator are dynamic, bidirectional, and non-linear (Hofmann et al.,
2020). More frequent and parallel assessments of mediators and outcomes are needed to understand the dynamic and agents of change in a more fine-grained manner. To facilitate this, shorter and change sensitive measures should be utilized.
Identifying the active ingredient of treatments and moving towards personalized treatments might benefit response rates. This also applies to our study where non-response rates of 31% (or 55% as a more conservative estimate) — while in line with meta-analytic findings on response rates in internet-based settings (Rozental et al.,
2019) — may be improved by providing more effective and personalized treatments. How could this be achieved? Our findings suggest that (internet-based) applications of the UP should especially emphasize and increase a mindful stance towards emotions as well as regulating emotions by cognitive reappraisal in order to enhance effects. Future studies should substantiate our findings with larger sample sizes and more frequent and parallel assessments.
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