Bipolar disorder is a recurrent, heterogeneous condition that often begins in adolescence and typically requires lifelong, multimodal management. Advances in evidence-based assessment (EBA) offer structured frameworks for prediction, prescription, and progress monitoring, and pharmacological and psychosocial interventions supported by recent reviews and the Canadian Network for Mood and Anxiety Treatments (CANMAT)/International Society for Bipolar Disorders (ISBD) guidelines provide effective options across phases of illness. Despite these advances, the impact of evidence-based approaches remains blunted in practice: Diagnosis is often delayed, pharmacotherapy is inconsistently prescribed or monitored, psychosocial interventions are underused, and relapse prevention strategies are rarely sustained. Therefore, the field must embed prediction, treatment, and monitoring within community treatment settings—primary care, schools, digital platforms, and family systems—where risk can be identified early, preventive strategies can be delivered, and long-term maintenance can be supported. Framing EBA as a dynamic, community-anchored cycle offers the best chance of translating evidence into improved outcomes, bridging the gap between research efficacy and real-world effectiveness in the care of bipolar disorder.
Mounting evidence supports the efficacy of nonpharmacologic or psychosocial interventions for reducing behavioral and psychological symptoms of dementia (BPSD), identifies the comparative risks and benefits of psychotropic medication, and underscores the importance of deprescribing psychotropic medications. However, evidence from clinical settings indicates that uptake of nonpharmacologic interventions and potential overuse of medications remain problematic. We begin by discussing the importance of exploring potential contributors to BPSD, using person-centered language to describe BPSD, implementing holistic and person-centered care plans, measuring clinically important changes in BPSD, and considering social determinants of health when assessing and managing BPSD; we also discuss the historical context informing how clinicians manage BPSD. Next, we compare and contrast nonpharmacologic and pharmacologic approaches to managing BPSD, evaluate their individual and comparative efficacy, and describe recommendations for intervention deprescribing or deimplementation. Lastly, we discuss strengths and limitations of the current evidence supporting BPSD management as well as recommendations for future research.
Clinical psychology is a discipline reliant on self-reports but uniquely susceptible to specific biases associated therewith. Here we provide a prototype for objective behavioral assessment drawn from the field of alcohol science, describing an emerging class of wearable transdermal biosensor. We note the challenges of transdermal alcohol assessment and describe recent performance gains from updated devices and machine learning analytic tools. We indicate unanswered questions for transdermal technology, including device longevity and the accuracy of devices for producing fine-grained estimates of drinking quantity. We identify factors that can impede development of transdermal sensors and other new objective measures, including the tendency to judge new tools against an implicit ideal, and consider scientific findings divorced from methodology. Finally, in evaluating novel objective measurement tools, we argue for careful consideration of not only error magnitude but also error type (i.e., random versus systematic), identifying measurement diversification as a priority for clinical psychology moving forward.
Psychological processes are highly heterogeneous, even among individuals with the same diagnosis. This variability poses challenges for nomothetic approaches that assume everyone is guided by the same broad psychological principles. In contrast, idiographic approaches focus on within-person variability but are often prone to noise and spurious relations and may not translate easily to clinical use due to limited generalizability. These constraints have motivated integrative approaches designed to model person-specific dynamics while still drawing on patterns that generalize across people. In this article, we review group iterative multiple model estimation (GIMME), one of the most widely used integrative approaches for modeling intensive longitudinal data (ILD) in clinical research. GIMME estimates person-specific dynamics using majority-shared paths as the backbone of individual models. We begin by introducing GIMME's core algorithm and its major extensions. We then review simulation studies evaluating its performance, survey empirical applications in clinical psychology, and outline alternative ILD methods. Finally, we discuss current limitations of GIMME and propose directions for its continued refinement.
Early life adversity is associated with many negative health, behavioral, and cognitive outcomes. However, the causal mechanisms underlying these associations remain obscure, making it difficult to design targeted interventions for individuals most at risk. Current approaches focus almost exclusively on event exposures, but given that data using these approaches have been inconsistent, there is a need for new models for characterizing adversity. We propose the Topological Approach to Adversity and Development, which, rather than focusing on the types of events children encounter, prioritizes understanding how children perceive, interpret, and make meaning of their circumstances. A focus on the developmental dimensions that contribute to how individuals interpret and learn to respond to potentially challenging life circumstances could renew and advance mechanistic accounts of childhood adversity. Such an approach has implications for the basic science of human stress responding as well as practical implications for improvements to individual well-being and health.
Emotion regulation is a multifaceted process essential to mental health. This review synthesizes established theoretical models within an integrative framework of emotion regulation that links individual regulatory abilities with the dynamic processes through which emotions are modulated. It outlines how these abilities develop through the interplay of biological predispositions and environmental factors, learning processes, and beliefs about emotion and highlights how regulatory abilities and process-level dynamics interact within a feedback-sensitive system. Repeated failures in this system, in turn, can contribute to psychopathology and behavioral problems. The review then examines how psychological interventions—from traditional cognitive and behavioral therapies to approaches that explicitly target emotion regulation—engage with these components. Special attention is given to emerging digital interventions. Findings on emotion regulation as a potential treatment mechanism are reviewed; these findings indicate that improvements in regulatory ability and reductions in maladaptive strategies frequently mediate treatment outcomes. The review concludes by identifying conceptual and methodological challenges and outlining future directions.
Mental health disorders are some of the greatest contributors to the global disease burden, and healthcare systems are struggling to provide scalable care of high quality. Artificial intelligence (AI)-based tools and applications are some of the most significant technological advances in the mental healthcare field. Researchers have spent decades to build, evaluate, and refine AI models to conduct tasks such as identifying the treatment intervention and rating the quality of treatment. These models have been utilized to monitor treatment quality, enhance training, support clinical documentation, and supplement client treatment support. However, significant limitations include the potential for algorithmic biases and ethical concerns regarding patient data privacy. While AI shows promise in addressing the mental health workforce shortage and improving the quality of care, successful implementation requires thoughtful integration. This review examines research in which AI is a complement to, not replacement for, human providers in mental health treatment.
This review provides an overview of biological processes that contribute to obsessive-compulsive disorder (OCD). It encourages nonreductionist integration of biological findings with psychological and social constructs. OCD runs in families; studies are beginning to identify genetic variants that contribute to risk, though these findings are not yet clinically actionable. A robust body of neuroimaging research implicates hyperactivity in certain brain circuits in the pathophysiology of OCD, which often normalizes following successful treatment. The efficacy of serotonin reuptake inhibitors is well-established; however, evidence does not support a simple serotonin deficit model. The role of the neurotransmitter glutamate in pathophysiology and treatment is under investigation. Emerging research is exploring the contributions of immune system dysregulation and of hormones to pathophysiology. Pharmacological treatment strategies are reviewed, as are anatomically targeted interventions for refractory cases. Future treatments will likely synergistically deploy somatic and psychotherapeutic interventions, leveraging biological tools to enhance mechanisms of psychological change.
Hormone sensitivity is a heterogeneous phenomenon involving multiple dimensions of sensitivity to estrogen and progesterone fluctuations across the menstrual cycle. While the majority of menstruating individuals do not experience any significant impact from these hormone changes on mood or behavior, rates of hormone sensitivity in clinical populations, particularly affective disorders, are substantially elevated, suggesting potential shared psychosocial or physiological mechanisms or moderators of risk. In this review, we provide an overview of menstrually related mood disorders and dimensions of hormone sensitivity across the cycle contributing to these presentations. We discuss how hormone sensitivity during the menstrual cycle corresponds with affective problems during other reproductive life events (puberty, perimenopause, pregnancy/postpartum) and review the evidence for environmental, neurobiological, and cognitive/affective factors associated with hormone sensitivity. Methodological considerations and directions for further research are highlighted throughout.
Adults who provide care to family members living with dementia experience substantial impacts to their well-being. Dementia family caregivers are the backbone of health and long-term care services in the United States, yet they typically do not access evidence-based caregiver interventions. This is especially the case for racial and ethnic minoritized populations, who experience higher rates of dementia yet lower access to diagnostic and specialty care services and evidence-based interventions. This review appraises the peer-reviewed literature on randomized clinical trials to test the effectiveness of caregiver interventions, the extent of cultural adaptations, and their impact on psychological outcomes, including mastery. We find that few evidence-based interventions incorporate cultural and linguistic adaptations, and when they do, most fall short of following formal adaptation frameworks and documenting treatment effects on psychological outcomes by racial and ethnic group. Research must address these shortcomings to increase the equitable distribution of caregiver interventions for all Americans.
Adolescent romantic competence is broadly understood as a person's capacity to adaptively approach, form, and maintain healthy romantic relationships and has important implications for relationship functioning and mental health. In this review, we first provide a history of the development of the construct of romantic competence and give an overview of different conceptualizations and assessments of adolescent romantic competence. Next, we summarize the research findings on the associations between adolescent romantic competence and relationship experience, relationship functioning, and mental health. Special challenges are addressed for romantic competence and adolescent relationships that may arise from social media. We then outline relationship education programs created out of the romantic competence literature, and we conclude by identifying future directions and remaining questions in the field.
Misophonia is characterized by unusually distressing reactions to certain repetitive audiovisual stimuli produced by others, most commonly oral (e.g., eating, throat clearing, gum popping) or nasal (e.g., sniffing, heavy breathing) sounds. Using the acronym BASIC, we review shared features between misophonia and anxiety disorders across behavioral, attentional, somatic, interpersonal, and cognitive domains of functioning. This article explores whether misophonia should be classified as an anxiety disorder, with emphasis on ways in which misophonia can be distinguished from the defining characteristics of anxiety disorders. Chief among these distinctions is the accumulating research indicating that anger (and related affective states such as irritation and resentment) is a central emotion more common than fear or anxiety in misophonia. With mounting data indicating that anxiety is not the primary core feature, scientific evidence does not justify classifying misophonia as an anxiety disorder.
Cyberbullying is a growing public health concern given its increasing prevalence, connection to mental health problems, and broader concerns about youth social media use. In this review, we define cyberbullying and its forms and provide information on prevalence and trends. We then contextualize cyberbullying within the larger research literature on digital technology use and mental health, detailing how this relationship varies depending on individual characteristics and how the technologies are being used. We then summarize the research on concurrent and long-term impacts of cyberbullying victimization and perpetration on internalizing and externalizing symptoms as well as the impacts on youth well-being. Mediating and moderating mechanisms that exacerbate risk and protect youth from adverse mental health impacts of cyberbullying are then explored. The evidence supporting cyberbullying prevention in school-based contexts and involving families is then presented. Finally, we discuss challenges in existing research, areas in need of further empirical investigation, and implications for practice and policy.
Fear extinction is foundational to exposure therapy; therefore, the study of strategies to optimize fear extinction is relevant for clinical practice. This article provides a critical review of translational research on pharmacological enhancement of fear extinction, concentrating mostly on d -cycloserine, the agent with the most extensive evidence base across levels of analyses. Despite early promise, results across preclinical, human laboratory, and clinical trials have been mixed. We identify factors that may account for these inconsistent findings, including differences in study design, selection of subjects, sample size, and measurement approaches. We emphasize the need for a rigorous mechanistic research agenda that both assesses extinction processes—acquisition, consolidation, and retrieval—as distinct mechanistic targets and examines the relation between changes in these fear extinction processes and clinical outcomes. Finally, we discuss opportunities to advance translational research in this area by leveraging extant collaborative infrastructures to improve the quality, the efficiency, and ultimately the availability of effective clinical strategies.
Although psychological science has contributed enormously to our understanding of the development of psychopathology and to interventions to treat psychiatric disorders, it has not successfully reduced the burden of mental illness in American communities. Factors include the lack of a mandate of population impact, stigma attached to mental health intervention, difficulty scaling up interventions, and problems in financing prevention. The concept of primary mental health care is introduced as a possible solution; components could include universal reach, universal brief interventions, screening and referral, ongoing support to all individuals, an infrastructure of specialized services, and an integrated data system. Promising examples of the primary mental health care approach are described, including PROSPER, Communities That Care, Triple P, Family Check-Up, HealthySteps, Family Connects, and Community Navigation. For these efforts to succeed, challenges must be overcome in labor force participation, reaching the full population, novel approaches to delivery of interventions, cultural adaptation, community-level interventions, financing, and scientific inquiry.
In this career-review-as-memoir, I interweave the deeply personal/familial roots of my abiding interests in developmental psychopathology, clinical trials, and reduction of mental illness stigma and discrimination with an overview of my variegated research on youth and young adults. I also discuss mentoring, teaching, lab-building, leadership, collaboration, synthesis, and serendipitous ideas. Lived experience and personal interest can and should inform discovery phases of scientific efforts, whereas objectivity and disinterest are essential for justification aspects. My long-term aim has been to bridge science and humanization. Although progress in clinical psychology is apparent, even passing recognition of current mental health challenges-especially for adolescents/young adults-provides an urgent call for (a) integration of genetic/biological risk with contextual and cultural factors; (b) provision of supportive settings, plus evidence-based treatments, for individuals and families in need; and (c) recruiting and mentoring new generations of scholar-clinicians who will continue these essential efforts.
Clinical psychological assessment often relies on self-report, interviews, and behavioral observation, methods that pose challenges for reliability, validity, and scalability. Computational approaches offer new opportunities to analyze expressive behavior (e.g., facial expressions, vocal prosody, language use) with greater precision and efficiency. This review provides an accessible conceptual framework for understanding how methods from computer vision, speech signal processing, and natural language processing can enhance clinical assessment. We outline the goals, frameworks, and methods of both clinical and computational approaches and present an illustrative review of interdisciplinary research applying these techniques across a range of mental health conditions. We also examine key challenges related to data quality, measurement, interdisciplinarity, and ethics. Finally, we highlight future directions for building systems that are robust, interpretable, and clinically meaningful. This review is intended to support dialogue between clinical and computational communities and to guide ongoing research and development at their intersection.
Schizophrenia-spectrum disorders (SSD) are characterized by interruptions in one's sense of self, cognitive dysfunction, disorganized thinking, belief inflexibility, and unusual experiences such as hallucinations and delusions. Disruptions in metacognition—skill in reflecting upon one's own and other's thought processes—have increasingly been viewed as a core foundation of these features of SSD. We focus this review on Metacognitive Reflection and Insight Therapy (MERIT) and Metacognitive Training (MCT), two metacognitive therapies for SSD designed to improve integration in perceptions of self and others, and to gain greater awareness of biases and the fallibility of cognitions, respectively. We explicate their theoretical underpinnings, treatment targets, and commonly used techniques and summarize their evidence base. We also provide a brief overview of two related therapies, cognitive remediation and cognitive behavioral therapy, with a focus on identifying their metacognitive components, and we compare mechanisms of action, efficacy, and evidence base across these different approaches.
Almost all countries in the world have witnessed a rapid increase in levels of economic inequality, a measure of the distribution of income and wealth across the population, since the advent of neoliberal economic policies in the 1970s. In this review, we conceptualize inequality as an ecological construct and discuss why it matters for the mental health of populations and for individual clinical outcomes. We then discuss some of the key mechanisms through which economic inequality influences mental health beyond poverty itself: social comparison and social capital. We also consider how the effect might vary across specific vulnerable groups in the population, such as young people and minoritized communities. Finally, we discuss methodological challenges in studying the relationship between inequality and mental health and conclude by outlining future research directions and possible interventions at the governmental, community, and individual levels to mitigate the negative mental health consequences of economic inequality.
A global mental health crisis is threatening a generation of young people with a lifetime of symptoms that do not fit neatly into diagnostic systems. Optimal decisions regarding treatments, services, research, and policies are critically needed, yet such decisions are based on idiosyncratic categorization of clinical courses. This review suggests clinical staging approaches may unite mental health stakeholders around shared targets to reduce mental illness. It first presents key approaches to clinical staging and then outlines how clinical knowledge has been translated into a unified transdiagnostic staging heuristic and clinical service structure over the past 30 years. Directions for short-, medium-, and long-term action are recommended with global community engagement. With investment from the mental health community, staging could reduce suffering through the use of an ethical, organized, and targeted system of communication.
