UNASSIGNED: In this paper, we present the conceptual background and clinical implications of a research-based transtheoretical treatment and training model (4TM).
UNASSIGNED: The model implements findings from psychotherapy outcome, process, and feedback research into a clinical and training framework that is open to future research.
UNASSIGNED: The framework is based on interventions targeting patient processes on a behavioral, cognitive, emotional, motivational, interpersonal, and systemic/socio-cultural level. The 4TM also includes a data-based decision support and feedback system called the Trier Treatment Navigator (TTN).
UNASSIGNED: We discuss important problems associated with clinical orientations solely based on one school of thought. We then contrast these concerns with a clinical and training framework that embraces ongoing research, serving as a guiding structure for process-based transtheoretical interventions. Such research-based psychological therapy can take both traditional and novel clinical developments as well as findings from psychotherapy research into account and be adaptively disseminated to a variety of patient populations.

OBJECTIVE: Diabetes distress is one of the most frequent mental health issues identified in people with type 1 and type 2 diabetes. Little is known about the role of glucose control as a potential contributor to diabetes distress and whether the subjective perception of glucose control or the objective glycaemic parameters are more important for the experience. With the emergence of continuous glucose monitoring (CGM), this is a relevant question as glucose values are now visible in real-time. We employed a precision monitoring approach to analyse the independent associations of perceived and measured glucose control with diabetes distress on a daily basis. By using n-of-1 analyses, we aimed to identify individual contributors to diabetes distress per person and analyse the associations of these individual contributors with mental health at a 3 month follow-up.
METHODS: In this prospective, observational study, perceived (hypoglycaemia/hyperglycaemia/glucose variability burden) and measured glucose control (time in hypoglycaemia and hyperglycaemia, CV) were assessed daily for 17 days using an ecological momentary assessment (EMA) approach with a special EMA app and CGM, respectively. Mixed-effect regression analysis was performed, with daily diabetes distress as the dependent variable and daily perceived and CGM-measured metrics of glucose control as random factors. Individual regression coefficients of daily distress with perceived and CGM-measured metrics were correlated with levels of psychosocial well-being at a 3 month follow-up.
RESULTS: Data from 379 participants were analysed (50.9% type 1 diabetes; 49.6% female). Perceived glucose variability (t=14.360; p<0.0001) and perceived hyperglycaemia (t=13.637; p<0.0001) were the strongest predictors of daily diabetes distress, while CGM-based glucose variability was not significantly associated (t=1.070; p=0.285). There was great heterogeneity between individuals in the associations of perceived and measured glucose parameters with diabetes distress. Individuals with a stronger association between perceived glucose control and daily distress had more depressive symptoms (β=0.32), diabetes distress (β=0.39) and hypoglycaemia fear (β=0.34) at follow-up (all p<0.001). Individuals with a stronger association between CGM-measured glucose control and daily distress had higher levels of psychosocial well-being at follow-up (depressive symptoms: β=-0.31; diabetes distress: β=-0.33; hypoglycaemia fear: β=-0.27; all p<0.001) but also higher HbA1c (β=0.12; p<0.05).
CONCLUSIONS: Overall, subjective perceptions of glucose seem to be more influential on diabetes distress than objective CGM parameters of glycaemic control. N-of-1 analyses showed that CGM-measured and perceived glucose control had differential associations with diabetes distress and psychosocial well-being 3 months later. The results highlight the need to understand the individual drivers of diabetes distress to develop personalised interventions within a precision mental health approach.

OBJECTIVE: Eating disorders (EDs) represent a major public health burden. Increasingly, studies suggest mental health (MH) fields are failing to improve the effectiveness of treatments and that alternative models of care must be considered. Precision mental health (PMH) seeks to tailor treatment to individual needs and relies on a comprehensive understanding of the neurobiological and physiological underpinnings of mental illness.
METHODS: In this narrative review, published literature with focus on biological application of PMH strategies for EDs is reviewed and summarised.
RESULTS: A total of 39 articles were retained for the review covering a variety of themes with relevance to PMH. Many studies of biological markers with PMH applicability focused on anorexia nervosa. Although a variety of potential PMH research applications were identified, the review failed to identify any evidence of implementation into routine ED practice.
CONCLUSIONS: Despite the theoretical merit of biological application of PMH in ED treatment, clinical applications for standard practice are lacking. There is a need to invest further in studies that seek to identify biological markers and investigate neurobiological underpinnings of disease in hopes of targeting and developing treatments that can be better tailored to the individualised needs of patients.

The current Special Issue marks a major milestone in the history of developmental psychopathology; as the final issue edited by Cicchetti, we have an opportunity to reflect on the remarkable progress of the discipline across the last four decades, as well as challenges and future directions for the field. With contemporary issues in mind, including rising rates of psychopathology, health disparities, and international conflict, as well as rapid growth and accessibility of digital and mobile technologies, the discipline of developmental psychopathology is poised to advance multidisciplinary, developmentally- and contextually- informed research, and to make substantial progress in supporting the healthy development of individuals around the world. We highlight key future directions and challenges for the next generation of developmental psychopathology research including further investigation of culture at multiple levels of analysis, incorporation of macro-level influences into developmental psychopathology research, methods advances to address heterogeneity in translational research, precision mental health, and the extension of developmental psychopathology research across the lifespan.

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient\'s response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Estimates of the stability of a preschooler\'s diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) into early elementary school vary greatly. Identified factors associated with diagnostic instability provide little guidance about the likelihood a particular child will have ADHD in elementary school. This study examined an approach to predicting age 6 ADHD-any subtype (ADHD-any) from preschoolers\' demographics and ADHD symptoms.
Participants were 796 preschool children (Mage = 4.44; 51% boys; 54% White, non-Hispanic) recruited from primary pediatric care and school settings. Parents completed ADHD Rating Scales at child ages 4 and 5 years, and a structured diagnostic interview (DISC-YC) at ages 4 and 6. Classification tree analyses (CTAs) examined the predictive utility of demographic and symptom variables at ages 4 and 5 years for age 6 ADHD.
Over half (52.05%) of preschoolers meeting diagnostic criteria for ADHD-any at age 4 did not meet those criteria at age 6; more than half (52.05%) meeting criteria for ADHD-any at age 6 had not met those criteria at age 4. A CTA conducted at age 4 predicted age 6 ADHD-any diagnosis 65.82% better than chance; an age 5 CTA predicted age 6 ADHD-any 70.60% better than chance. At age 4, likelihood of age 6 ADHD-any diagnosis varied from <5% to >40% across CTA tree branches and from <5% to >78% at age 5.
Parent-reported patterns of preschool-age symptoms may differentially predict ADHD-any at age 6. Psychoeducation regarding these patterns may aid in decision about pursuing multidisciplinary evaluations or initiating treatment.

Despite tremendous advances in characterizing human neural circuits that govern emotional and cognitive functions impaired in depression and anxiety, we lack a circuit-based taxonomy for depression and anxiety that captures transdiagnostic heterogeneity and informs clinical decision making.
We developed and tested a novel system for quantifying 6 brain circuits reproducibly and at the individual patient level. We implemented standardized circuit definitions relative to a healthy reference sample and algorithms to generate circuit clinical scores for the overall circuit and its constituent regions.
In new data from primary and generalizability samples of depression and anxiety (N = 250), we demonstrated that overall disconnections within task-free salience and default mode circuits map onto symptoms of anxious avoidance, loss of pleasure, threat dysregulation, and negative emotional biases-core characteristics that transcend diagnoses-and poorer daily function. Regional dysfunctions within task-evoked cognitive control and affective circuits may implicate symptoms of cognitive and valence-congruent emotional functions. Circuit dysfunction scores also distinguished response to antidepressant and behavioral intervention treatments in an independent sample (n = 205).
Our findings articulate circuit dimensions that relate to transdiagnostic symptoms across mood and anxiety disorders. Our novel system offers a foundation for deploying standardized circuit assessments across research groups, trials, and clinics to advance more precise classifications and treatment targets for psychiatry.

Modern research has proven that the \"typical patient\" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients\' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.

This introductory article describes the genesis of the Festschrift for Leonard Bickman and of this Festschrift special issue entitled, The Future of Children\'s Mental Health Services. The special issue includes a collection of 11 original children\'s mental health services research articles, broadly organized in accordance with three themes (i.e., Improving Precision and Use of Service Data to Guide Policy and Practice, Implementation and Dissemination, and Preparing for Innovation), followed by an interview-style article with Bickman. Then follows a featured manuscript by Bickman himself, three invited commentaries, and a compilation of letters and notes in which colleagues reflect on his career and on their experiences of him. The introduction concludes with a few thoughts about the future of children\'s mental health services portended by the extraordinary scholarly contributions of Bickman and those who have been inspired by him.

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.