Feasibility of Process-based Therapy in a Naturalistic Setting
Phase: NA
Conditions: Depressive Disorder, Anxiety Disorder
Sponsor: Goethe University

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Feasibility of process-based therapy in a naturalistic setting: Study protocol for 
a feasibility trial 
 
 
 
 
 
 
NCT number: NCT06530888 
 
 
 
 
 
 
 
Date of document: 05th of February 2026 
 
 
 
 
 
 
 

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Feasibility of process-based therapy in a naturalistic setting: Study protocol for 
a feasibility trial 
Ulrich Stangier, University of Frankfurt, Germany 
Bettina Hufschmidt, University of Frankfurt, Germany 
Lucie Pahlen, University of Frankfurt, Germany 
Viktoria Kohl, University of Frankfurt, Germany 
Desiree Bonarius, University of Frankfurt, Germany 
Arwin Nemani, University of Frankfurt, Germany 
Mareike Ebert, University of Frankfurt, Germany 
Stefan G. Hofmann, University of Marburg, Germany 
 
Corresponding author:  
Prof. Dr. Ulrich Stangier 
Department of Clinical Psychology and Psychotherapy 
Varrentrappstr. 40/42 
D-60486 Frankfurt/Germany 
Email: stangier@psych.uni-frankfurt.de 
 
 

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Abstract 
Background 
In the naturalistic setting of mental health care, the treatment decisions of 
psychotherapists are often based on theories or experiences related to treatment 
approaches. An alternative approach to treatment decisions is suggested by process-
based therapy (PBT), which emphasizes empirical and rational criteria for the 
selection of interventions. It utilizes ecological momentary assessment (EMA) data, 
incorporates feedback from dynamic network analysis, and supports interventions to 
be chosen on the basis of individual network models and empirical evidence from 
research related to change processes. Currently, there are no data on the feasibility 
and acceptability of PBT in practice. The present study investigates, in a naturalistic 
setting, whether PBT can be implemented by psychotherapists in mental health care. 
Furthermore, we explored the acceptability and efficacy of PBT compared with 
psychotherapy delivered in routine practice (r-PT). 
Methods 
The study is an explanatory feasibility trial with two parallel treatment conditions. 
Forty patients are recruited in psychotherapy practices and allocated to one of two 
interventions, PBT or r-PT. To control for therapist effects, randomization is stratified 
by therapist. 
Primary outcome is acceptance of the treatment by both therapists and patients, 
therapy expectations and dropout rates.  
Secondary outcomes are patients' and therapists’ attitudes toward the perceived 
benefits of the core PBT components and therapist-rated adherence to PBT 
rationale. Furthermore, as clinical outcomes, psychological symptoms of distress, 
depressive and anxious symptoms, patient-rated quality of life and psychological 
well-being will be compared between PBT and r-PT. Assessments of feasibility and 
clinical outcome variables will be conducted before, during, and after treatment. 
 
Conclusions 
The current study is the first to explore the implementation of PBTs in a naturalistic 
mental health care setting. If the feasibility of PBT is demonstrated, the results may 
provide new perspectives on the personalization of assessment and treatment on the 
basis of dynamic network analysis of EMA data and network-related treatment 
decision making. 
 
Trial registration 
 
Keywords 
Process-based Therapy, dynamic network analysis, feasibility trial, ecological 
momentary assessment, clinical decision-making 

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Background 
In mental health care, a wide range of psychological treatments are available, which 
have also been shown to be effective. A recent meta-analysis of uncontrolled studies 
of psychological interventions delivered in routine practice [1] revealed that high pre-
post effect sizes were associated with a reduction in depression and anxiety. 
However, it remains unclear whether positive effects assessed in studies can also be 
generalized to routine psychotherapy in mental health care, since clinical outcomes 
are rarely assessed and published. An exception is the Improving Access to 
Psychological Therapies program in England, which demonstrated that the 
implementation of stepped-care clinical guidelines may help bridge the gap between 
evidence-based research and practice [2]. 
However, treatment guidelines usually provide only rough recommendations for the 
use of evidence-based manuals related to diagnoses and leave open how the 
recommendations should be adapted to individual patients. Furthermore, empirical 
research indicates that in clinical practice, assessment of mental health problems is 
often unstructured, and interventions are mainly selected on the basis of personal 
experience or school-based treatment theories rather than empirical evidence [3,4]. 
Instead of developing more effective, new interventions, a promising approach to 
improve the outcomes of mental health care is personalization [5]. Personalization 
can be conceptualized as a process that involves a collaborative collection and 
processing of diagnostic information as well as shared decision-making in treatment 
selection. Personalization refers to treatment-related decisions that can be based on 
intuition and experience, theoretical models underlying treatment concepts, 
algorithms using scientific evidence for effective interventions, or precise statistical 
models used to predict the outcome of specific interventions on the basis of big data 
[5]. Evidence from preliminary randomized controlled trials suggests that treatment 
effectiveness can be enhanced when interventions are tailored to the individual [6]. 
Achieving such personalization requires that therapeutic strategies be closely aligned 
with a person’s specific difficulties and informed by research on mechanisms of 
change [7]. In this context, selecting interventions can be made more efficient by 
targeting the psychological processes that sustain an individual’s dysregulation and 
matching them with treatments capable of modifying these processes. Building on 
this rationale, Hofmann and Hayes (2019) proposed the process-based therapy 
framework [8], which involves identifying the core processes that maintain 
maladaptive patterns using ecological momentary assessment (EMA) and dynamic 
network analysis [9], and then choosing interventions supported by empirical 
evidence for their capacity to alter these key processes [10]. 
The process-based approach conceives of psychological disorders as individual 
networks of psychological processes, as opposed to the latent disease model, which 
assigns symptoms to an underlying disease [11]. In contrast to syndrome-based 
treatment packages, interventions are selected on the basis of change processes 
that have been empirically supported as mediators of treatment effects [12]. 
Furthermore, in an extended evolutionary meta-model, Hayes, Hofmann and 
Ciarrochi (2020) [13] proposed that mental health problems can be conceptualized as 
an idiographic network of maladaptive psychological processes in a specific context 
rather than as symptoms of a static state of disease. Within this evolutionary 
framework, change processes in psychotherapy are guided by the principles of 
variation, selection, and retention, modifying the elements of the maladaptive 
individual psychological network and creating an adaptive, more flexible network. 
 

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In summary, personalization is a promising way to improve the effectiveness of 
psychotherapy. However, whereas personalized treatment is associated with 
increased efficacy in randomized controlled trials, implementation in clinical practice 
may encounter barriers to data-driven and evidence-based decision algorithms [14]. 
Process-based therapy is characterized by two essential principles: 1) key factors 
maintaining psychological problems are identified via EMA and dynamic network 
analysis, and 2) interventions are selected on the basis of active ingredients that fit 
the key factors of the individual problem [8]. In our study, we examine the hypothesis 
that the diagnostic process and treatment selection, as intended in process-based 
therapy, are feasible for implementation in clinical practice. Furthermore, we sought 
to obtain preliminary indications of effectiveness of process-based therapy (PBT). 
Because the principles of PBT are not tied to, and may even transcend, specific 
diagnostic categories, the trial will include patients with a wide range of clinical 
diagnoses. 
 
Objectives 
The main objective of this study is to explore the feasibility of PBT in a natural mental 
health care setting delivered by practitioners. Feasibility comprises therapists’ and 
patients’ evaluation of acceptance, utility, attrition of patients and adherence of 
therapists with the treatment. To determine the degree of feasibility, we compare 
patients’ and therapists’ ratings of feasibility related to PBT. 
Overall treatment satisfaction will be considered a primary marker of how acceptable 
the intervention is to patients. This assessment will be complemented by patients’ 
ratings of treatment credibility, their willingness to engage with ecological momentary 
assessment (EMA), and their level of compliance with EMA procedures. Therapists 
will report on the practical value of EMA and dynamic network models for their clinical 
work, as well as on the extent to which they followed process-based principles in their 
treatment decisions. Finally, trajectories of symptom burden and general mental 
health will be tracked to yield preliminary insights into the impact of PBT under 
routine clinical conditions. 
 
 
Methods/design 
Design 
The current trial is an exploratory feasibility trial with two parallel treatment 
conditions, PBT and r-PT, delivered in a natural setting of mental health care. To 
control for systematic effects of therapist variables such as therapists’ competences 
and preferences, allocation will be stratified by therapists. Pairs of patients recruited 
by a therapist within his practice will be randomly assigned to one of the two study 
conditions. Thus, the same therapist implements PBT as well as r-PT. Adherence to 
both conditions is ensured by controlling the use of EMA and a questionnaire 
covering essential ingredients of PBT. All participants across the two intervention 
groups will receive 24 weekly sessions. A CONSORT diagram is provided in Fig. 1. 

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Figure 1: Consort flow diagram 
 
 
 
Notes: EMA: Ecological Momentary Assessment; PBT: Process-based Therapy; r-
PT: Routine Psychotherapy 
 
Assessments 
Outcome measures for the dependent variables will be collected at three time points: 
prior to treatment, during treatment (intermediate assessment), and after treatment 
completion (see Table 1). The intermediate assessment takes place after five 
sessions in order to evaluate changes associated with the use of EMA. 
The primary endpoints focus on treatment acceptability as an indicator of feasibility, 
based on both therapists’ and patients’ ratings obtained before and after the 
intervention. Additional feasibility indicators include therapists’ appraisals of the 
intervention itself, their adherence to process-based clinical decision-making 
throughout treatment, and patients’ level of compliance with EMA procedures. 
Furthermore, postintervention changes in patients’ self-reported emotional distress, 
psychological well-being, health-related quality of life, and overall mental health will 
be examined. 
The process variables include psychological flexibility, reflective functioning, adaptive 
behavior and cognitive behavioral therapy skills at pre-, intermediate- and post-
assessment. In addition, the therapeutic relationship will be assessed before and 
after treatment. 

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Table 1 Overview of measurements 
 
 
 
 
 
 
Measurement Points 
Inclusion 
Pre-
Treatment 
Baseline 
Phase 
Inter-
mediate 
Treatment 
Phase 
Post-
Treatment 
Treatment 
Sessions 
1-5 
6-24 
Week 
Instrument 
0 
1 
2-7 
8 
9-27 
28 
Therapist 
ICD-10  
Diagnosis 
X 
X 
CGI 
X 
X 
CGI-I 
X 
HAQ-11 
X 
X 
FAMOS 
X 
Intervention 
Checklist 
X 
PBDMQ 
every 4 
weeks (only 
PBT) 
TAUEN 
X (only 
PBT) 
CEQ 
X  
TEI 
X (only 
PBT) 
Patient 
DASS-10 
X 
X 
X 
EQ-5D 
X 
X 
PMH 
X 
X 
AAQ-II 
X 
X 
X 
RFQ 
X 
X 
X 
PBAT 
X 
X 
X 
CBTSQ 
X 
X 
 
X 
EMA (PBT) 
Daily   
(only PBT) 
 
X 
HAQ-11 
X 
X 
CEQ 
X 
                   
PAUEN 
X (only 
PBT) 
CSQ 
X 

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Abbreviations: 
HAQ-11, Helping Alliance Questionnaire; 
FAMOS, Fragebogen zur Analyse Motivationaler Schemata, Kurzversion (Brief Assessment of 
Motivational schemas); 
PBDMQ, Process-based decision-making questionnaire; 
TAUEN, Therapist attitude toward the utility of the EMA and network scale; 
CEQ, Credibility/Expectancy Questionnaire 
TEI, Treatment Evaluation Inventory 
DASS-10, Depression Anxiety Stress Scale 10 items; 
EQ-5D, EuroQol 5 Dimensions; 
PMH, Positive Mental Health Scale; 
AAQ, Acceptance and Action Questionnaire 
RFQ: Reflective Functioning Questionnaire; 
PBAT, Process-based assessment tool; 
CBTSQ, Cognitive behavioral therapy skills questionnaire 
EMA: Ecological Momentary Assessment. 
PAUEN, Patient attitudes toward the utility of the EMA and network scale; 
CSQ-8, Client Satisfaction Questionnaire 
CGI, Clinical Global Impression Rating Scales 
 
Measures 
Feasibility measures 
Primary outcome. The main outcome will be patient satisfaction at the end of 
treatment, measured with the Client Satisfaction Questionnaire (CSQ-8) [18], which 
will be used as a central indicator of how well the intervention is accepted. In 
addition, study attrition will be evaluated, defined as the percentage of participants 
who withdraw before completing the final assessment. Attrition rates of less than 20% 
are typically regarded as acceptable [19]. 
Furthermore, the therapist attitudes toward the utility of the EMA and networks scale 
(TAUEN) and the patient attitudes toward the utility of the EMA and networks scale 
(PAUEN) are used, which are modified versions of the Therapist and Client Attitudes 
Measures by Frumkin et al. (2021) [16]. The Treatment Evaluation Inventory (TEI) 
[17] will be used to assess therapists’ perceptions of treatment. It consists of 14 
statements that are evaluated on a 7-point Likert scale, with response options 
spanning from −3 (strongly disagree) to +3 (strongly agree). 
Secondary outcomes. Perceived treatment credibility will be assessed prior to the 
start of therapy using the Credibility/Expectancy Questionnaire (CEQ) [15]. This 
instrument comprises six items, divided into two subscales that capture credibility and 
expectancy, respectively. Health-related quality of life will be measured both before 
and after treatment with the EuroQol-5D (EQ-5D) [20]. Positive psychological well-
being will be assessed at the same time points using the nine-item Positive Mental 
Health Scale (PMH) [21]. Furthermore, levels of depression, anxiety, and stress will 
be evaluated with the short form of the Depression Anxiety Stress Scale (DASS-10) 
[22] at baseline, midtreatment, and posttreatment. 
Psychological flexibility is measured by three self-reported items from the Acceptance 
and Action Questionnaire Version 2 (AAQ-2) [23]. Furthermore, reflective functioning 
will be assessed via the Reflective Functioning Questionnaire (RFQ-8) [24], a 54-item 
self-report inventory evaluating the capacity to comprehend the internal mental states 
of oneself and others across two dimensions: certainty and uncertainty about mental 
states. Using the process-based assessment tool (PBAT) [25], selection and 

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retention of adaptive behavior will be measured before treatment, at intermediate 
treatment and after treatment. The Cognitive‒Behavioral‒Therapy Skills 
Questionnaire (CBTSQ) [26], consisting of six self-reported items, was used to 
measure patients’ use of cognitive behavioral therapy interventions in this study. 
The FAMOS (Questionnaire for the Analysis of Motivational Schemata, short version), 
also known as the Brief Assessment of Motivational Schemata [29], captures 
motivational schemata by measuring how people structure their needs, goals, and 
motives and which motivational patterns they use in the process. 
As a potential moderator of outcome, therapeutic alliance is assessed by patients 
and therapists via the 11-item Helping Alliance Questionnaire (HAQ-11) [30]. 
 
The diagnosis will be based on the International Classification of Disease-10 (ICD-
10) and made by the therapists. At posttreatment, in addition to feasibility ratings for 
clinical utility and acceptance (see above), a checklist of interventions for therapists is 
implemented to verify adherence. The Process-based Decision-making 
Questionnaire (PBDMQ) [27] comprises 24 items designed to evaluate adherence to 
process-based therapy (PBT). The "Clinical Global Impression Scale" (CGI) [28] 
assesses the clinician's overall evaluation of the severity of a mental disorder and is 
utilized to monitor changes in symptom severity over time. It has 3 items that are 
answered by therapists. 
EMA 
The Status-PBT (Vacay ©, 2024) is a mobile application for the EMA during the 
baseline phase that uses personalized questions and captures six dimensions 
(thought, emotion, body sensation, behavior, cognitive processing, motivational 
schema) on bipolar continuous scales ranging from -100 to +100. The similarity of the 
context with typical problem situations as defined in the hypothetical network model is 
rated on a continuous scale ranging from 0 to +100. 
A checklist of interventions for therapists is used after posttreatment assessment to 
check adherence based on literature reviews [31,32] and meta-analyses [33-36]. 
 
Settings 
Therapists participating in the trial will be psychotherapists who are licensed for 
cognitive‒behavioral therapy or psychodynamic psychotherapy for adult patients. 
Therapists will be recruited in Germany. All the therapists receive 20 hrs. training 
focusing on personalization of treatment by 
1) deriving a hypothetical network model of the individual problem, 
2) guiding patients through the collection of EMA data, 
3) interpreting dynamic network models, 
4) drawing network-related treatment decisions, and 
5) applying interventions based on mechanisms related to the central knot of the 
network. 
All therapists receive a manual [37] containing essential procedures in network-based 
assessment and treatment. 
Participants 
We aim to include 40 patients who meet the following inclusion criteria: (1) a primary 
DSM-5 diagnosis of a depressive or anxiety disorder, (2) age 18-65 years, (4) 

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sufficient knowledge of the German language. Participating patients are not required 
to discontinue medication but rather to keep medication constant throughout the 
treatment period. Patients will be excluded in case of (1) increased suicidality, (2) 
substance abuse or dependency, (3) diagnosis of a cluster A or B (DSM-5) 
personality disorder, (4) pervasive developmental disorder, psychotic disorder, eating 
disorder, bipolar disorder, or severe physical illness. Inclusion and exclusion criteria 
are assessed by the psychotherapists who will determine the diagnosis according to 
ICD-10. 
Recruitment and randomization procedure 
Patients are evaluated by the therapists to be eligible for participation in the study 
based on the inclusion and exclusion criteria described previously. If patients are 
eligible for treatment, they will be provided with information about the project. 
Patients who consent to participate in the project will be included in the pretreatment 
assessment (see Table 1). Patients’ self-ratings are collected electronically with the 
guidance of the trial management staff. Patients who cannot or will not participate in 
the study will be offered treatment as usual by the therapist. 
If eligibility for the study is confirmed and informed consent for randomization is 
given, the patient will be randomized to one of two conditions (PBT or R-PT) provided 
by the same therapist. To allocate study participants to treatment conditions, a 
randomization list is created by the data management staff via the statistical software 
R. The group allocations are printed individually and placed in sealed envelopes. For 
each included participant, a member of the trial management staff draws an envelope 
and reads off the group allocation. 
 
 
Treatments 
Both treatments comprise 24 weekly sessions, including a 5-week baseline phase in 
PBT and a following tracking phase. 
In the baseline phase of PBT, a hypothetical network model of the problem is 
developed. EMA is then conducted based on the model's key components: 
situational context, cognition, emotion, bodily symptoms, behavior, cognitive 
processing, and motivational schema. In addition to the definition of maladaptive 
responses, the adaptive counterparts of the variables, representing the desired 
outcomes to be targeted in treatment, are also defined. 
Participants of PBT are instructed to use the mobile app Status (Vacay ©), which 
prompts them to assess the seven dimensions from the hypothetical network model 
on a bipolar scale from -100 (maladaptive) to +100 (adaptive). Data are collected in 
the context of situations related to the problem. Therapists guide the adaptation of 
the items during weekly sessions and assist patients in recognizing situations and 
recording their judgments of the model components. EMA is completed when 100 
measurements are collected. 
Based on the EMA data, a dynamic network analysis is performed to assess 
autoregressive and cross-lagged effects of the variables [38]. The interactive effects 
of the variables are estimated and visualized in a network where variables are 
represented as 'nodes', effects between them as directed arrows ('edges'), and 
autoregressive effects as 'self-loops'. 
In the 5 initial sessions of r-PT corresponding to the baseline in PBT, no specific 
goals are prescribed. Usually, psychotherapists explore either current symptoms or 

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major biographic events to gain insight into the psychological determinants of the 
problem. 
After 5 sessions, the intermediate assessment is conducted, followed by 19 treatment 
sessions in both conditions. In PBT, treatment begins with a collaborative 
interpretation of the dynamic network model, which is based on EMA data collected 
during the baseline phase. Therapists identify the central node, significant edges, 
self-loops, and positive or negative feedback loops between the nodes [39]. Using 
the outcomes from the dynamic network model, interventions are selected based on 
empirical evidence for mechanisms of change that correspond to the individual 
patient's central node, as well as the feedback loops and self-loops, which are key in 
maintaining maladaptive patterns [10]. 
These interventions are framed within an evolutionary framework as the variation, 
selection, and retention of an adaptive mode of the central node in relation to the 
specific context of the problem [40]. The change in this key variable is monitored 
through daily judgments based on EMA.Treatment also focuses on additional targets 
to establish adaptive modes of the dimensions as defined in the positive network 
model. 
 
In r-PT, as opposed to PBT, a naturalistic setting is retained for treatment decisions. 
Treatment planning follows traditional theories about the factors maintaining the 
disorder and interventions to change it, e.g., avoidance and exposure in anxiety 
disorders or reduced reinforcement of activities and behavioral activation in 
depression [41]. Interventions are selected based on common treatment manuals 
related to diagnoses, e.g., CBT for depression. Individual data from the behavioral 
analysis are used to tailor the techniques to the individual problems of the patients. 
The treatment process is largely structured by the personal preferences of the 
therapist due to the experience, knowledge, or recommendations of the National 
Guidelines for Mental Health problems. 
 
Treatment fidelity 
Adherence to PBT vs. r-PT treatment will be checked via a questionnaire assessing 
process-based and naturalistic decision-making styles [26]. The questionnaire, 
validated by PBT experts, contains 24 items. Therapists in both conditions will 
complete the questionnaire after every four sessions in each treatment. Additionally, 
after each treatment session, an intervention checklist covering frequently used 
interventions for depression and anxiety disorders will be completed. 
Sample size justification 
Power analysis with a formal sample size calculation is not considered to be 
adequate in feasibility studies [42]. To achieve a sufficient variance in patients’ and 
therapists’ responses, we intend to recruit, in line with recommendations reported in 
literature [43], a sample size of 40 patients. This sample size is considered to be 
sufficient to provide initial references for a feasibility trial. 
Statistical analyses 
Main outcome criteria for feasibility refer to descriptive statistics based on the criteria 
for utility and acceptance as defined in the section on feasibility measures. In 
addition, statistical analysis of measures will be based on completed assessments 

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but will not include intention-to-treat analysis. For the statistical analysis of the cross-
sectional feasibility data at pre- and post-treatment, Mann-Whintey-U-Tests will be 
applied. Clinical outcome data will be analyzed using a repeated measures ANOVA 
with treatment condition as the between-subjects factor and time (pre-treatment and 
post-treatment) as the within-subjects factor. Secondary analyses of the data set 
generated in this study, such as exploratory moderator analyses and the analysis of 
secondary outcomes will be reserved. Any secondary analysis will be preregistered 
seperately from this study protocol. Dynamic network analysis of EMA data at 
baseline and posttreatment will be computed using the R package "graphical VAR" 
[38]. For all other statistical analyses, the computer program R will also be used. 
 
Ethics and governance 
The protocol has been approved by the Ethics Committee at the Department of 
Psychology (Registration number: NCT06530888). Protocol amendments will be 
communicated at https://clinicaltrials.gov and detailed in publications. 
Adverse events, particularly suicidal behavior/ideation, will be monitored. Withdrawal 
from the study will be considered in cases of increased suicidal ideation or acute 
suicidality. 
The first author, U.S., is the principal investigator (PI) and initiated the project 
together with the senior author, S.G.H. The study does not have a data monitoring 
committee. 

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Conclusions 
This study is the first to investigate the implementation of PBT in a natural setting. 
Although the interventions used in PBT are not different from established 
psychotherapeutic methods, there are innovative elements that might be associated 
with barriers to implementation in practice. 
First, the use of smartphone-based EMA implies that a hypothetical network model of 
the individual problem must be derived from exploration and then defined as 
dimensions that can be assessed in EMA. However, as some studies have noted, 
practitioners are skeptical of the application of digital assessment methods [16, 44, 
45]. The technical requirements also must be met [46], which might limit its 
application. 
Second, the outcome of dynamic network analyses of EMA data is dependent on the 
quality of the information covered in the smartphone-based assessments. Thus, the 
identification of relevant dimensions in exploration is an important challenge in the 
initial sessions before EMA is established at baseline. During the EMA phase, users 
must understand when and what is to be assessed, depending on whether time 
sampling or experience sampling is carried out. Furthermore, interpretation of 
empirical dynamic network models is an important initial step in organizing treatment. 
The identification of parameters such as central nodes, positive and negative 
feedback circles, and self-loops requires insight into the methodological background 
of these parameters, which is not part of the current academic and clinical training. 
Furthermore, the communication of the results must be fitted to the patients’ 
understanding, associated with obstacles concerning complexity, clarity, and utility for 
the treatment process [47]. 
Third, interventions are selected based on underlying mechanisms related to central 
nodes of the individual network rather than treatment packages or traditional 
treatment schemata. However, dynamic network models represent a challenge of the 
established practice, which is characterized by a strong link from syndromes to 
protocols or treatment packages [8]. The latent disease model claims that the 
problems of a person are caused by a latent dysfunction and is the target of the 
treatment. This model has determined scientific and clinical training and is deeply 
rooted in the clinical knowledge of many practitioners. The dynamic network 
approach, however, argues that psychopathological patterns represent an individual 
system of interacting elements of psychological symptoms [48]. Therefore, the 
selection of personalized interventions based on information about the dynamic 
network model has not yet been established in clinical practice. Thus, the 
implementation of PBT requires “new” models, specific knowledge, and more 
complex decision processes that also consider the individual competencies of a 
therapist, which might limit its feasibility and effectiveness in practice. 
From the patients’ perspective, the implementation of EMA is associated with 
increased efforts. The collection of data in everyday life may not only be associated 
with digital distress but also require the interruption of automatic habits and a focus 
on psychological dimensions, as worked out in therapy. Thus, patients must refrain 
from avoiding negative emotions, face problems and judge their reactions. Thus, the 
capacity to accept distressing internal experiences is necessary to overcome 
experiential avoidance [49]. On the other hand, using bipolar ratings including 
positive dimensions may also motivate patients to engage in change processes even 
before therapeutic strategies are applied [50]. 

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This trial will provide valuable insights into barriers to implementing PBT, which will 
help improve the conceptualization of decision processes in this new approach, as 
well as the training and supervision of therapists. However, with respect to 
effectiveness, the design and setting of our study are associated with significant 
limitations in internal validity. First, since therapists will implement both treatment 
conditions, their therapeutic skills and attitudes will have profound effects on the 
delivery of PBT as well as on routine psychotherapy. Therapists’ competence in 
applying specific decision-making strategies in PBT, their adherence to network-
based interventions, and their allegiance with PBT will largely determine whether 
significant differences in effectiveness will be detectable. Although measures for 
adherence and allegiance can be applied to control for these factors, it is difficult to 
estimate whether statistically significant differences can be achieved. Second, 
independent clinical judgments of the clinical outcomes are not included since 
organizing the timing with practitioners might create problems. Thus, there is a lack of 
objective information about treatment effects. Third, the inclusion criteria are broadly 
defined and allow for a heterogeneous composition of the sample with respect to 
diagnoses and severity. Although patients are randomized to both treatment 
conditions within therapists, the equivalence of subsamples with respect to clinical 
characteristics may be impaired. 
In addition to these limitations, implementation in a natural setting of practicing 
psychotherapists with low interference with the therapeutic procedures ensures the 
generalizability of the outcome to the broader target of mental health care. 
Furthermore, in addition to providing information on the outcome, the practical 
experiences of psychotherapists will stimulate new ideas for improving the 
conceptualization and implementation of PBT. We have included quantitative criteria 
for determining the feasibility of the new treatment which focus on clinical utility and 
acceptance by therapists and patients. Feasibility will be further assessed by 
measures for patient’s expectations and of therapists’ adherence with the treatment 
principles. In addition, we also include ratings of quality of life, psychological 
symptoms and measures referring potential mediators of treatment effects. Although 
the design of this study does not allow for valid conclusions about the effectiveness 
of the treatment, these measures may provide additional information about the 
potential effects of the treatment. If the criteria for feasibility are met, randomized-
controlled trials with adequate sample sizes are needed to test the efficacy of the 
treatment. 
 
 

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Declarations 
Ethics approval and consent to participate 
The protocol has been approved by the Ethics Committee at the Department of 
Psychology (Registration number: 2026-00B as a supplement to 2023-47). Protocol 
amendments will be communicated at https://clinicaltrials.gov and detailed in 
publications.  
Consent for publication 
Not applicable. 
Availability of data and material 
The study protocol is made publicly available through this publication. The main 
results are intended to be published in a high-impact peer reviewed journal within 6 
months after the trial end date (approximately 2026/27). Individual participant data 
will be available for investigators whose proposed use of the data has been approved 
by an independent review committee identified for this purpose. Data will be available 
beginning 6 months and ending 36 months following article publication.  
Competing interests 
All authors declare that they have no conflicts of interest. 
Funding 
This work is funded by the LOEWE Top Professorship for Stefan G. Hofmann, and 
supported by the DYNAMIC center, which is funded by the LOEWE program of the 
Hessian Ministry of Science and Arts (Grant Number: 
LOEWE1/16/519/03/09.001(0009)/98). 
Authors' contributions 
US and SH conceived the trial, drafted the study protocol and treatment manual, 
trained therapists and diagnosticians, and supervised implementation and 
coordination of the study. BH contributed to the trial design and protocol development 
and obtained ethical approval. ME and AN developed the EMA assessment and 
dynamic network analyses. LP, VK and DB conduct recruitment and data collection. 
US drafted the article and all authors read, edited, and approved the final manuscript. 
Acknowledgements 
We would like to thank Steve Hayes from the University of Nevada for supporting the 
development of our treatment manual and for contributing valuable ideas to the 
implementation of EMA into practice.  
 
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