Perceived glucose levels matter more than CGM-based data in predicting diabetes distress in type 1 or type 2 diabetes: a precision mental health approach using n-of-1 analyses.

AIMS/HYPOTHESIS: 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 (ß=0.32), diabetes distress (ß=0.39) and hypoglycaemia fear (ß=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: ß=-0.31; diabetes distress: ß=-0.33; hypoglycaemia fear: ß=-0.27; all p<0.001) but also higher HbA1c (ß=0.12; p<0.05). CONCLUSIONS/INTERPRETATION: 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.

The next generation of developmental psychopathology research: Including broader perspectives and becoming more precise.

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.

Exploring biologically oriented precision mental health initiatives for the care of patients with eating disorders: A narrative review.

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.

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example.

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.

Research Review: Brain network connectivity and the heterogeneity of depression in adolescence - a precision mental health perspective.

BACKGROUND: Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC). METHODS: In this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence. RESULTS: Nascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping. CONCLUSIONS: Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.

Working Towards the Development and Implementation of Precision Mental Healthcare: An Example.

Leonard Bickman's (2020) Festschrift paper in the special issue "The Future of Children's Mental Health Services" on improving mental health services is an impressive reflection of his career, highlighting his major insights and the development of mental health services research as a whole. Five major difficulties in this field's current research and practice are attentively delineated: poor diagnostics, measurement problems, disadvantages of randomized controlled trials (RCTs), lack of feedback and personalized treatments. Dr. Bickman recommends possible solutions based on his extensive experience and empirical findings. We agree with his thoughts and illustrate how we, challenged with the same problems, have attempted to develop clinically informed research and evidence-based clinical practice. A comprehensive feedback system that deals with the aforementioned problems is briefly described. It includes pre-treatment recommendations for treatment strategies and an empirically informed dropout prediction based on a variety of data sources. In addition to treatment recommendations, continuous feedback as well as individualized treatment adaptation tools are provided during ongoing therapy. New projects are being implemented to further improve the system by including new data assessment strategies and sources, e.g., ecological momentary assessment (EMA) and automated video analysis.

Festschrift for Leonard Bickman: Introduction to The Future of Children's Mental Health Services Special Issue.

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.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health.

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.

Randomized controlled trial to evaluate the effects of personalized prediction and adaptation tools on treatment outcome in outpatient psychotherapy: study protocol.

BACKGROUND: Psychotherapy is successful for the majority of patients, but not for every patient. Hence, further knowledge is needed on how treatments should be adapted for those who do not profit or deteriorate. In the last years prediction tools as well as feedback interventions were part of a trend to more personalized approaches in psychotherapy. Research on psychometric prediction and feedback into ongoing treatment has the potential to enhance treatment outcomes, especially for patients with an increased risk of treatment failure or drop-out. METHODS/DESIGN: The research project investigates in a randomized controlled trial the effectiveness as well as moderating and mediating factors of psychometric feedback to therapists. In the intended study a total of 423 patients, who applied for a cognitive-behavioral therapy at the psychotherapy clinic of the University Trier and suffer from a depressive and/or an anxiety disorder (SCID interviews), will be included. The patients will be randomly assigned either to one therapist as well as to one of two intervention groups (CG, IG2). An additional intervention group (IG1) will be generated from an existing archival data set via propensity score matching. Patients of the control group (CG; n = 85) will be monitored concerning psychological impairment but therapists will not be provided with any feedback about the patients assessments. In both intervention groups (IG1: n = 169; IG2: n = 169) the therapists are provided with feedback about the patients self-evaluation in a computerized feedback portal. Therapists of the IG2 will additionally be provided with clinical support tools, which will be developed in this project, on the basis of existing systems. Therapists will also be provided with a personalized treatment recommendation based on similar patients (Nearest Neighbors) at the beginning of treatment. Besides the general effectiveness of feedback and the clinical support tools for negatively developing patients, further mediating and moderating variables on this feedback effect should be examined: treatment length, frequency of feedback use, therapist effects, therapist's experience, attitude towards feedback as well as congruence of therapist's and patient's evaluation concerning the progress. Additional procedures will be implemented to assess treatment adherence as well as the reliability of diagnosis and to include it into the analyses. DISCUSSION: The current trial tests a comprehensive feedback system which combines precision mental health predictions with routine outcome monitoring and feedback tools in routine outpatient psychotherapy. It also adds to previous feedback research a stricter design by investigating another repeated measurement CG as well as a stricter control of treatment integrity. It also includes a structured clinical interview (SCID) and controls for comorbidity (within depression and anxiety). This study also investigates moderators (attitudes towards, use of the feedback system, diagnoses) and mediators (therapists' awareness of negative change and treatment length) in one study. TRIAL REGISTRATION: Current Controlled Trials NCT03107845 . Registered 30 March 2017.

A Precision Mental Health Model for Predicting Stability of 4-year-olds' Attention Deficit/Hyperactivity Disorder Symptoms to Age 6 Diagnostic Status.

OBJECTIVE: 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. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

From Theory to Practice: A Transtheoretical Treatment and Training Model (4TM).

Background: In this paper, we present the conceptual background and clinical implications of a research-based transtheoretical treatment and training model (4TM). Method: The model implements findings from psychotherapy outcome, process, and feedback research into a clinical and training framework that is open to future research. Results: 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). Conclusion: 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.

Digital twins and the future of precision mental health.

Science faces challenges in developing much-needed precision mental health treatments to accurately identify and diagnose mental health problems and the optimal treatment for each individual. Digital twins (DTs) promise to revolutionize the field of mental health, as they are doing in other fields of science, including oncology and cardiology, where they have been successfully deployed. The use of DTs in mental health is yet to be explored. In this Perspective, we lay the conceptual foundations for mental health DTs (MHDT). An MHDT is a virtual representation of an individual's mental states and processes. It is continually updated from data collected over the lifespan of the individual, and guides mental health professionals in diagnosing and treating patients based on mechanistic models and statistical and machine learning tools. The merits of MHDT are demonstrated through the example of the working alliance between the therapist and the patient, which is one of the most consistent mechanisms predicting treatment outcome.

Mapping Neural Circuit Biotypes to Symptoms and Behavioral Dimensions of Depression and Anxiety.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments.

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.

"Third-wave" cognitive and behavioral therapies and the emergence of a process-based approach to intervention in psychiatry.

For decades, cognitive and behavioral therapies (CBTs) have been tested in randomized controlled trials for specific psychiatric syndromes that were assumed to represent expressions of latent diseases. Although these protocols were more effective as compared to psychological control conditions, placebo treatments, and even active pharmacotherapies, further advancement in efficacy and dissemination has been inhibited by a failure to focus on processes of change. This picture appears now to be evolving, due both to a collapse of the idea that mental disorders can be classified into distinct, discrete categories, and to the more central attention given to processes of change in newer, so-called "third-wave" CBTs. Here we review the context for this historic progress and evaluate the impact of these newer methods and models, not as protocols for treating syndromes, but as ways of targeting an expanded range of processes of change. Five key features of "third-wave" therapies are underlined: a focus on context and function; the view that new models and methods should build on other strands of CBT; a focus on broad and flexible repertoires vs. an approach to signs and symptoms; applying processes to the clinician, not just the client; and expanding into more complex issues historically more characteristic of humanistic, existential, analytic, or system-oriented approaches. We argue that these newer methods can be considered in the context of an idiographic approach to process-based functional analysis. Psychological processes of change can be organized into six dimensions: cognition, affect, attention, self, motivation and overt behavior. Several important processes of change combine two or more of these dimensions. Tailoring intervention strategies to target the appropriate processes in a given individual would be a major advance in psychiatry and an important step toward precision mental health care.

Using Korean Dramas as a Precision Mental Health Education Tool for Asian Americans: A Pilot Study.

Precision mental health (MH) holds great potential for revolutionizing MH care and reducing the burden of mental illness. Efforts to engage Asian Americans in precision MH research is necessary to help reduce MH disparities. Korean drama ("K-drama") television shows may be an effective educational tool to increase precision MH knowledge, attitudes, and behaviors (KAB) among Asian Americans. This study determined whether KAB improved after participating in a K-drama precision MH workshop, and examined the participants' perspectives about K-dramas' utility as an educational tool. A K-drama precision MH workshop in English/Vietnamese/Korean was conducted with a convenience sample (n = 122). Pre-/post-tests on precision MH KAB (genetics and genetic testing, and MH and help-seeking) and a survey on K-dramas' utility as an educational tool were administered. Findings revealed a significant difference in the pre- and post-test KAB scores overall, by genetics and genetic testing, and by MH and help-seeking. There were also significant increases in the overall post-test KAB scores by workshop (language) participation. Overall, participants responded positively on the utility of K-dramas as a precision MH educational tool. This study demonstrates the feasibility of K-drama as an innovative and widely available health education tool to educate communities about precision MH.

Towards integrating personalized feedback research into clinical practice: Development of the Trier Treatment Navigator (TTN).

In this study, a computer-based feedback, decision and clinical problem-solving system for clinical practice will be described - the Trier Treatment Navigator (TTN). The paper deals with the underlying research concepts related to personalized pre-treatment recommendations for drop-out risk and optimal treatment strategy selection as well as personalized adaptive recommendations during treatment. The development sample consisted of 1234 patients treated with cognitive behavioral therapy (CBT). Modern statistical machine learning techniques were used to develop personalized recommendations. Drop-out analyses resulted in seven significant predictors explaining 12.0% of variance. The prediction of optimal treatment strategies resulted in differential prediction models substantially improving effect sizes and reliable improvement rates. The dynamic failure boundary reliably identified patients with a higher risk for no improvement or deterioration and indicated the usage of clinical problem-solving tools in risk areas. The probability to be reliably improved for patients identified as at risk for treatment failure was about half of the probability for other patients (35% vs. 62.15%; χ2df=1 = 82.77, p < .001). Results related to the computer-based feedback system are discussed with regard to the implication for clinical applications as well as clinical training and future research possibilities.