Wolfgang Lutz

Objective: Motivational incongruence refers to insufficient satisfaction of individual motives in interaction with the environment and is assumed to contribute to psychological distress and psychopathology when prolonged. This study examined reduced motivational incongruence as a potential change mechanism in psychotherapy by testing whether within-person decreases in motivational incongruence predict subsequent decreases in general psychological distress. Method: Analyses were based on three psychotherapy samples from two randomized controlled trials and one observational dataset collected in routine care, comprising 1,656 patients in total. In each sample, motivational incongruence and distress were assessed at four to six occasions during treatment using short forms of the Incongruence Questionnaire and the Symptom Checklist-90-R, respectively. Random-intercept cross-lagged panel models (RI-CLPMs) tested prospective within-person effects between motivational incongruence and distress. Latent curve models with structured residuals (LCM-SRs) served as exploratory robustness analyses. Results: Across three samples, the RI-CLPMs yielded significant and large prospective effects of motivational incongruence on subsequent distress. Reverse cross-lagged effects, from distress to subsequent motivational incongruence, were significant in two of the three samples. The exploratory LCM-SR supported the primary motivational incongruence-to-distress effect in one sample but yielded no admissible solutions in the other two. Conclusions: Across three independent psychotherapy samples, within-person reductions in motivational incongruence preceded reductions in distress, consistent with motivational incongruence as a potential transtheoretical change mechanism in psychotherapy. Motivational incongruence may therefore represent a candidate treatment target. Future research should test the proposed causal relationship experimentally and examine which dimensions of motivational incongruence are most relevant for individual patients.

Background: The Personalized Advantage Index (PAI) has evolved into a standard framework to identify the individually optimal treatment from available alternatives in mental health and beyond. The traditional PAI compares a patient’s predicted post-treatment symptom scores across treatment alternatives to identify the treatment with the most favorable predicted score. However, clinicians may wish to select the treatment that maximizes that patient’s predicted probability of achieving a clinically defined outcome, such as remission. This study presents an Alternative PAI (AltPAI) optimized for this purpose and evaluates its performance. Methods: We used routine care data (n = 1,264) to implement the traditional PAI and AltPAI using random forests. The indices were compared regarding the benefit for patients in terms of dimensional improvement and remission. Exploratively, the PAI and AltPAI were combined into an ensemble (EnsPAI). Results: The AltPAI outperformed the PAI descriptively but not significantly, yielding higher symptom change (cohens d = 0.09) and remission probability (OR = 1.32) for optimally treated patients with higher index values. Regarding symptom improvement, the PAI performed worse than chance. The EnsPAI was the only algorithm to perform significantly better than chance in terms of symptom remission. Conclusions: The AltPAI and EnsPAI demonstrated performance that was generally descriptively superior to the PAI. The approaches should therefore be investigated in combination with the PAI in future treatment stratification analyses. Additionally, more advanced implementations of the EnsPAI are discussed that would allow the ensemble to be calibrated on the error of the base indices, potentially increasing prediction performance.

Objective: Suicidal ideation (SI) is a critical clinical concern, yet most predictive approaches rely on static or aggregate measures that overlook its session-to-session dynamics. This study aimed to improve next-session SI prediction by integrating dynamic item-level data and identifying the clinical features that contribute most strongly throughout treatment. Methods: Data from 2,045 outpatients were analyzed across the first 10 sessions. A transformer-based time-series model was trained to predict next-session SI using prior-session item-level scores and was compared against a static elastic-net baseline. SHAP values were calculated to quantify feature contributions at both group and individual levels. Results: The dynamic model significantly outperformed the static baseline (average R² = .95 vs. .12), and performance remained improved even without autoregressive inputs (average R² = .24). While prior-session SI was consistently the strongest predictor, depressive affect, hopelessness, meaning in life, and anxiety-related symptoms also provided significant predictive value. SHAP analyses further revealed temporal fluctuations and patient-specific patterns in influential features. Conclusion: Dynamic, interpretable models using item-level data substantially enhance short-term SI prediction. These findings provide clinical insights into which symptoms are most relevant for specific patients at different times, supporting more responsive and personalized suicide risk monitoring.

Clinicians often struggle to accurately identify patients at risk of treatment failure. Feedback-informed treatment offers real-time outcome monitoring to help detect and address potential treatment failures early. This study examined patterns of change among not-on-track (NOT) patients during feedback-informed treatment, explored how patients' characteristics related to these patterns, and assessed the feasibility of predicting them. Data from 573 NOT patients in a university outpatient psychotherapy clinic were analyzed. Growth mixture modeling identified distinct change trajectories, omnibus tests compared patients' characteristics across patterns, and multinomial logistic regression evaluated the predictive value of these characteristics. Three distinct patterns of change emerged, differing in symptom severity at the point of being flagged as NOT and in subsequent treatment response: C1: severe-modest responders, C2: severe-fast improvers, and C3: moderate-improvers. The patterns were differentiated by key intake variables, including symptom severity, personality disorder traits, interpersonal problems, and emotion regulation difficulties. Two of the patterns were predicted with acceptable accuracy. All patient subgroups demonstrated improvement. Nevertheless, they became NOT for different reasons, and their responses were associated with different clinical characteristics. These findings suggest that a NOT alert should not be interpreted as a homogeneous signal, and patients may require different adaptations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The definition of psychotherapy has wide-ranging implications for research and clinical care as well as for legal issues, as set out in the German psychotherapy guidelines. It can determine which services are covered by health insurance and who is authorized to provide the services. In light of the ongoing debate regarding the evidence base of psychotherapy, a group of experts from various professional disciplines with different theoretical and methodological backgrounds and focusing on different age groups, has worked together with individuals with personal experience of psychotherapy and young clinicians to engage in a discussion process aimed at arriving at a contemporary definition of psychotherapy. This incorporates the current body of empirical evidence, enables innovative developments towards a transtheoretical, transdiagnostic and personalized psychotherapy and places the needs of patients at the center. Our literature review leads to a definition of psychotherapy comprising four aspects: treatment modality or "tools" (treatment modality)? How does the treatment work (proposed mechanisms of action, assumptions about causal and evidence-based processes)? Who receives psychotherapy (target group)? Who provides psychotherapy (provider)? Our proposed definition is not bound to specific treatment theories and orientations but aims to promote the evidence-based provision of psychotherapy within the German healthcare system and in accordance with the legal framework.

Digital therapeutics (DTx) have become increasingly prominent in mental health care, offering scalable, evidence-based interventions. A common assumption underlying their design and evaluation is that greater usage time leads to superior therapeutic outcomes, reflecting an implicit linear dose-response model. However, accumulating evidence challenges this simplified perspective and suggests that the relationship between usage time and clinical benefit is substantially more complex. In this viewpoint article, we critically examine the assumption that higher usage time is a necessary prerequisite for therapeutic success in digital mental health interventions. Drawing on psychotherapy dose-effect research, findings from digital intervention trials, and our own empirical work, we highlight several factors that complicate linear exposure models. These include rapid early response and plateau effects, substantial heterogeneity in user trajectories, motivational and contextual determinants of use, episodic patterns of engagement, and the distinction between on-platform activity and off-platform skill application. The linear dose metaphor, implicitly inherited from pharmacotherapy models, appears conceptually mismatched with learning-based digital interventions. Importantly, equating greater use with greater effectiveness risks conflating exposure with therapeutic mechanism, and may inadvertently promote evaluation frameworks that prioritize duration over meaningful change. Together, these insights suggest that usage time is an imperfect proxy for therapeutic engagement, and that high or continuous use should not be treated as a universal indicator of intervention quality. We argue for a shift toward mechanism-informed and individualized evaluation frameworks that prioritize the quality, timing, and functional impact of intervention use over cumulative exposure.

Clinical or methodological significance of this article: Premature treatment termination in inpatient psychotherapy can have severe negative consequences for the individual patient and the mental h...

Importance: Psychological interventions are a mainstay of treatment for substance use disorders (SUDs), but their ingredients remain unknown.Objective: Identify components of evidence-based psychological interventions for a SUD class and develop a transtheoretical taxonomy.Data sources: The National Library of Medicine via PubMed, searched on 11/01/2023 (update 04/01/2025) for network meta-analyses (NMAs) published since 11/01/2018, using keywords related to substance types, psychotherapy and NMAs.Study selection: Evidence-based psychological interventions were identified from randomized controlled trials (RCTs) included in one large, recent NMA of stand-alone interventions targeting stimulant use disorders. This NMA comprised 50 RCTs comparing any structured psychosocial intervention to active control or treatment as usual for stimulant (cocaine and/or amphetamine) use disorder in adults, with 10 more RCTs identified in the search update. Two independent researchers identified all intervention arms containing structured psychological components.Data extraction and synthesis: PRISMA 2020 reporting guidelines were followed. Two researchers retrieved complete intervention descriptions (e.g., protocols, manuals) and independently and iteratively extracted components, with multiple consensus meetings for groups of 3-5 RCTs to standardize extraction and coding decisions. Components were organized into higher-order categories with shared functional characteristics and unified definitions. The taxonomy was discussed and amended in a 2.5-days expert meeting. Inter-rater reliability (Cohen’s kappa) was estimated for a random subset of 35% of intervention arms, assessed independently by two new researchers.Main outcome(s) and measure(s): Transtheoretical components and unique intervention profiles, defined as distinct combinations of these. Results: 109 psychological intervention arms yielded 1592 elements, subsequently consolidated into 25 distinct transtheoretical components. Based on this taxonomy, 109 psychological arms were condensed in 40 unique profiles. The most frequently employed components were relapse prevention (34 profiles), functional analysis (32), skill generalization (29), and therapeutic alliance (29). The least frequent components were mindfulness & meditation (3) and subpopulation issues (4). Cohen’s kappa was fair (0.2-0.40) for two components, moderate (0.41-0.6) for four, and substantial (over 0.60) for eleven (remaining components had low prevalence). Conclusions and relevance: Recasting psychological interventions as unique combinations of transtheoretical components instead of trademarked protocols could revolutionize treatment evaluation and delivery and accelerate mechanisms of change identification.

Background: The definition of psychological treatments and psychotherapy has various implications for communication within research and clinical care, as well as for legal issues — particularly in countries with psychotherapy acts. It can define what should be covered by public healthcare systems and who should be permitted to provide these services. Method: We assembled a group of psychotherapists in Germany to develop an inclusive, scientific definition of psychotherapy. This definition should serve as an umbrella that is not limited to traditional treatment schools and is suitable for science, clinical care and legal acts. The group comprised people with different therapeutic backgrounds (e.g., CBT, psychodynamic and systemic therapy), experts from various disciplines (e.g., psychology, psychiatry and psychosomatic medicine), people focusing on different age groups (e.g., adults and youth), individuals with lived experience of psychotherapy and early career clinicians. Results: A literature review led us to consider four aspects of a definition. What are the treatment means or 'tools'? (modality of treatment); how does the treatment work? (Proposed change mechanisms, assumptions about causal and evidence-based processes and concepts); who receives psychotherapy? (target group); and who provides psychotherapy? (Provider). Based on these aspects, we offer a definition of psychological therapy. Discussion: Our suggested definition is not bound to specific treatment orientations, but is intended to encompass both traditional approaches and innovative developments. Our aim is to promote the evidence-based provision of psychological therapy within healthcare systems and legal frameworks.