Personalized Models of Psychopathology

The Alternative Model of Personality Disorders: Assessment, Convergent and Discriminant Validity, and a Look to the Future

The Alternative Model of Personality Disorders (AMPD) is a dimensional, empirically based diagnostic system developed to overcome the serious limitations of traditional categories. We review the mounting evidence on its convergent and discriminant validity, with an incursion into the less-studied ICD-11 system. In the literature, the AMPD's Pathological Trait Model (Criterion B) shows excellent convergence with normal personality traits, and it could be useful as an organizing framework for mental disorders. In contrast, Personality Functioning (Criterion A) cannot be distinguished from personality traits, lacks both discriminant and incremental validity, and has a shaky theoretical background. We offer some suggestions with a view to the future. These include removing Criterion A, using the real-life consequences of traits as indicators of severity, delving into the dynamic mechanisms underlying traits, and furthering the integration of currently disengaged psychological paradigms that can shape a sounder clinical science.

Symptoms of a feather flock together? An exploratory secondary dynamic time warp analysis of 11 single case time series of suicidal ideation and related symptoms

Suicidal ideation fluctuates over time, as does its related risk factors. Little is known about the difference or similarities of the temporal patterns. The current exploratory secondary analysis examines which risk symptoms have similar time dynamics using a mathematical algorithm called dynamic time warping (DTW). Ecological momentary assessment data was used of 11 depressed psychiatric outpatients with suicidal ideation who answered three daytime surveys at semi-random sampling points for a period of three to six months. Patients with 45 assessments or more were included. Results revealed significant inter-individual variability in symptom dynamics and clustering, with certain symptoms often clustering due to similar temporal patterns, notably feeling sad, hopelessness, feeling stuck, and worrying. The directed network analyses shed light on the temporal order, highlighting entrapment and worrying as symptoms strongly related to suicide ideation. Still, all patients also showed unique directed networks. While for some patients changes in entrapment directly preceded change in suicide ideation, the reverse temporal ordering was also found. Relatedly, within some patients, perceived burdensomeness played a pivotal role, whereas in others it was unconnected to other symptoms. The study underscores the individualized nature of symptom dynamics and challenges linear models of progression, advocating for personalized treatment strategies.

Examining missingness at the momentary level in clinical research using ecological momentary assessment: Implications for suicide research

The use of intensive time sampling methods, such as ecological momentary assessment (EMA), has increased in clinical, and specifically suicide, research during the past decade. While EMA can capture dynamic intraindividual processes, repeated assessments increase participant burden, potentially resulting in low compliance. This study aimed to shed light on study-level and psychological variables, including suicidal ideation (SI), that may predict momentary prompt (i.e., prompt-to-prompt) completion. We combined data from three EMA studies examining mental health difficulties (N = 103; 10,656 prompts; 7144 completed), using multilevel models and machine learning to determine how well we can predict prompt-to-prompt completion and which variables are most important. The two most important variables in prompt-to-prompt completion were hours since the last prompt and time in study. Psychological variables added little predictive validity; similarly, trait-level SI demonstrated a small effect on prompt-to-prompt completion. Our study showed how study-level characteristics can be used to explain prompt-to-prompt compliance rates in EMA research, highlighting the potential for developing adaptive assessment schedules to improve compliance.

Novel Approaches Toward Studying Change: Implications for Understanding and Treating Psychopathology

In this article, the authors critically evaluate contemporary models of psychopathology and therapies, underscoring the limitations of traditional symptom-based classification approaches in mental health. The authors introduce a paradigm shift in the field, toward a process-oriented and dynamic systems approach to psychotherapy that offers deeper insights into the complex interplay of symptoms and individual experiences in psychopathology. These approaches offer a more personalized and effective understanding and treatment of mental health issues, moving beyond static and 1-dimensional views. The authors discuss the implications for clinical practice, emphasizing improved assessment, diagnosis, and tailored treatment strategies.

Heterogeneity in suicide risk: Evidence from personalized dynamic models

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

Personality Functioning, Problems in Living, and Personality Traits

The publication of the Alternative Model of Personality Disorder (AMPD) was a signpost achievement in the personality assessment. However, research on the AMPD has generally not led to either a deeper understanding of personality disorder or personality assessment or new ideas about how to provide better care for people with personality disorder diagnoses. A significant portion of research has focused on narrow issues and appears to be driven in part by ideological differences between scholars who prefer Criterion A (personality functioning) or Criterion B (maladaptive traits). I trace these issues to ambiguity about the concept of personality functioning as defined in the AMPD and its conceptual distinction from personality traits and problems in living. In this paper, I reground these concepts in coherent and distinct definitions, elaborate upon the implications of their differences, and show how these differences can help clarify and reorient AMPD research to focus on generating clinically useful models for personality pathology and personality assessment.

Psychosis superspectrum II: neurobiology, treatment, and implications

Alternatives to traditional categorical diagnoses have been proposed to improve the validity and utility of psychiatric nosology. This paper continues the companion review of an alternative model, the psychosis superspectrum of the Hierarchical Taxonomy of Psychopathology (HiTOP). The superspectrum model aims to describe psychosis-related psychopathology according to data on distributions and associations among signs and symptoms. The superspectrum includes psychoticism and detachment spectra as well as narrow subdimensions within them. Auxiliary domains of cognitive deficit and functional impairment complete the psychopathology profile. The current paper reviews evidence on this model from neurobiology, treatment response, clinical utility, and measure development. Neurobiology research suggests that psychopathology included in the superspectrum shows similar patterns of neural alterations. Treatment response often mirrors the hierarchy of the superspectrum with some treatments being efficacious for psychoticism, others for detachment, and others for a specific subdimension. Compared to traditional diagnostic systems, the quantitative nosology shows an approximately 2-fold increase in reliability, explanatory power, and prognostic accuracy. Clinicians consistently report that the quantitative nosology has more utility than traditional diagnoses, but studies of patients with frank psychosis are currently lacking. Validated measures are available to implement the superspectrum model in practice. The dimensional conceptualization of psychosis-related psychopathology has implications for research, clinical practice, and public health programs. For example, it encourages use of the cohort study design (rather than case-control), transdiagnostic treatment strategies, and selective prevention based on subclinical symptoms. These approaches are already used in the field, and the superspectrum provides further impetus and guidance for their implementation. Existing knowledge on this model is substantial, but significant gaps remain. We identify outstanding questions and propose testable hypotheses to guide further research. Overall, we predict that the more informative, reliable, and valid characterization of psychopathology offered by the superspectrum model will facilitate progress in research and clinical care.

Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study

BACKGROUND

Heightened stress and insufficient sleep are common in the transition to college, often co-occur, and have both been linked to negative health outcomes. A challenge concerns disentangling whether perceived stress precedes or succeeds changes in sleep. These day-to-day associations may vary across individuals, but short study periods and group-level analyses in prior research may have obscured person-specific phenotypes.

OBJECTIVE

This study aims to obtain stable estimates of lead-lag associations between perceived stress and objective sleep duration in the individual, unbiased by the group, by developing an individual-level linear model that can leverage intensive longitudinal data while remaining parsimonious.

METHODS

In total, 55 college students (n=6, 11% second-year students and n=49, 89% first-year students) volunteered to provide daily self-reports of perceived stress via a smartphone app and wore an actigraphy wristband for the estimation of daily sleep duration continuously throughout the academic year (median usable daily observations per participant: 178, IQR 65.5). The individual-level linear model, developed in a Bayesian framework, included the predictor and outcome of interest and a covariate for the day of the week to account for weekly patterns. We validated the model on the cohort of second-year students (n=6, used as a pilot sample) by applying it to variables expected to correlate positively within individuals: objective sleep duration and self-reported sleep quality. The model was then applied to the fully independent target sample of first-year students (n=49) for the examination of bidirectional associations between daily stress levels and sleep duration.

RESULTS

Proof-of-concept analyses captured expected associations between objective sleep duration and subjective sleep quality in every pilot participant. Target analyses revealed negative associations between sleep duration and perceived stress in most of the participants (45/49, 92%), but their temporal association varied. Of the 49 participants, 19 (39%) showed a significant association (probability of direction>0.975): 8 (16%) showed elevated stress in the day associated with shorter sleep later that night, 5 (10%) showed shorter sleep associated with elevated stress the next day, and 6 (12%) showed both directions of association. Of note, when analyzed using a group-based multilevel model, individual estimates were systematically attenuated, and some even reversed sign.

CONCLUSIONS

The dynamic interplay of stress and sleep in daily life is likely person specific. Paired with intensive longitudinal data, our individual-level linear model provides a precision framework for the estimation of stable real-world behavioral and psychological dynamics and may support the personalized prioritization of intervention targets for health and well-being.

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

BACKGROUND

As depression is highly heterogenous, an increasing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time invariant, which may lead to important temporal features of the disorder being missed.

OBJECTIVE

To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time.

METHODS

Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models.

RESULTS

The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

CONCLUSIONS

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.

Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review

BACKGROUND

There has been an increased interest in understanding social anxiety (SA) and SA disorder (SAD) antecedents and consequences as they occur in real time, resulting in a proliferation of studies using ambulatory assessment (AA). Despite the exponential growth of research in this area, these studies have not been synthesized yet.

OBJECTIVE

This review aimed to identify and describe the latest advances in the understanding of SA and SAD through the use of AA.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic literature search was conducted in Scopus, PubMed, and Web of Science.

RESULTS

A total of 70 articles met the inclusion criteria. The qualitative synthesis of these studies showed that AA permitted the exploration of the emotional, cognitive, and behavioral dynamics associated with the experience of SA and SAD. In line with the available models of SA and SAD, emotion regulation, perseverative cognition, cognitive factors, substance use, and interactional patterns were the principal topics of the included studies. In addition, the incorporation of AA to study psychological interventions, multimodal assessment using sensors and biosensors, and transcultural differences were some of the identified emerging topics.

CONCLUSIONS

AA constitutes a very powerful methodology to grasp SA from a complementary perspective to laboratory experiments and usual self-report measures, shedding light on the cognitive, emotional, and behavioral antecedents and consequences of SA and the development and maintenance of SAD as a mental disorder.

Psychosis superspectrum I: Nosology, etiology, and lifespan development

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis: Comparative Case Analysis

BACKGROUND

Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.

OBJECTIVE

This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.

METHODS

To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one's phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).

RESULTS

Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.

CONCLUSIONS

Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual's psychological processes in the time leading up to a suicidal crisis.

Leveraging Single-Case Experimental Designs to Promote Personalized Psychological Treatment: Step-by-Step Implementation Protocol with Stakeholder Involvement of an Outpatient Clinic for Personalized Psychotherapy

Our objective is to implement a single-case experimental design (SCED) infrastructure in combination with experience-sampling methods (ESM) into the standard diagnostic procedure of a German outpatient research and training clinic. Building on the idea of routine outcome monitoring, the SCED infrastructure introduces intensive longitudinal data collection, individual effectiveness measures, and the opportunity for systematic manipulation to push personalization efforts further. It aims to empower psychotherapists and patients to evaluate their own treatment (idiographic perspective) and to enable researchers to analyze open questions of personalized psychotherapy (nomothetic perspective). Organized around the principles of agile research, we plan to develop, implement, and evaluate the SCED infrastructure in six successive studies with continuous stakeholder involvement: In the project development phase, the business model for the SCED infrastructure is developed that describes its vision in consideration of the context (Study 1). Also, the infrastructure's prototype is specified, encompassing the SCED procedure, ESM protocol, and ESM survey (Study 2 and 3). During the optimization phase, feasibility and acceptability are tested and the infrastructure is adapted accordingly (Study 4). The evaluation phase includes a pilot implementation study to assess implementation outcomes (Study 5), followed by actual implementation using a within-institution A-B design (Study 6). The sustainability phase involves continuous monitoring and improvement. We discuss to what extent the generated data could be used to address current questions of personalized psychotherapy research. Anticipated barriers and limitations during the implementation processes are outlined.

The future of neuroscience in developmental psychopathology

Developmental psychopathology started as an intersection of fields and is now a field itself. As we contemplate the future of this field, we consider the ways in which a newer, interdisciplinary field - human developmental neuroscience - can inform, and be informed by, developmental psychopathology. To do so, we outline principles of developmental psychopathology and how they are and/or can be implemented in developmental neuroscience. In turn, we highlight how the collaboration between these fields can lead to richer models and more impactful translation. In doing so, we describe the ways in which models from developmental psychopathology can enrich developmental neuroscience and future directions for developmental psychopathology.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

Individual-Specific Animated Profiles of Mental Health

How important is the timing of the pretreatment evaluation? If we consider mental health to be a relatively fixed condition, the specific timing (e.g., day, hour) of the evaluation is immaterial and often determined on the basis of technical considerations. Indeed, the fundamental assumption underlying the vast majority of psychotherapy research and practice is that mental health is a state that can be captured in a one-dimensional snapshot. If this fundamental assumption, underlying 80 years of empirical research and practice, is incorrect, it may help explain why for decades psychotherapy failed to rise above the 50% efficacy rate in the treatment of mental-health disorders, especially depression, a heterogeneous disorder and the leading cause of disability worldwide. Based on recent studies suggesting within-individual dynamics, this article proposes that mental health and its underlying therapeutic mechanisms have underlying intrinsic dynamics that manifest across dimensions. Computational psychotherapy is needed to develop individual-specific pretreatment animated profiles of mental health. Such individual-specific animated profiles are expected to improve the ability to select the optimal treatment for each patient, devise adequate treatment plans, and adjust them on the basis of ongoing evaluations of mental-health dynamics, creating a new understanding of therapeutic change as a transition toward a more adaptive animated profile.

Commentary: Special Issue on Interpersonal Trust

This commentary reviews the Journal of Personality Disorders special issue "Interpersonal Trust and Borderline Personality Disorder: Insights From Clinical Practice and Research," published in Volume 37, Number 5, October 2023.

The network dynamics of self-compassion components and psychological symptoms during an intervention

The change process of psychological interventions is complex and should be understood with a systems perspective. This study sought to examine the network dynamics of self-compassion components and psychological symptoms during an intervention. A total of 139 participants completed daily assessments during a 28-day intervention. Utilizing multilevel vector autoregressive (VAR) model, temporal and contemporaneous networks were generated and group differences in network dynamics were evaluated through descriptive assessment and permutation tests. The intervention group displayed a significant increase in self-compassion and decrease in psychological symptoms, with self-compassion mediating the intervention effects on symptoms. Network analysis revealed some network dynamics that might be relevant to desirable therapeutic changes in the intervention group. The intervention group demonstrated a significantly less connected contemporaneous depression network, indicating a decreased vulnerability to symptom activation. Additionally, the intervention group showed significantly more temporal connections from self-compassion to anxiety, indicating an increased influence of self-compassion on anxiety. These findings suggest that the intervention may have reshaped the interconnection pattern of symptoms and that between self-compassion components and symptoms.

A novel approach for constructing personalized networks from longitudinal perceived causal relations

Personalized networks of psychological symptoms aim to advance therapy by identifying treatment targets for specific patients. Statistical relations in such networks can be estimated from intensive longitudinal data, but their causal interpretation is limited by strong statistical assumptions. An alternative is to create networks from patient perceptions, which comes with other limitations such as retrospective bias. We introduce the Longitudinal Perceived Causal Problem Networks (L-PECAN) approach to address both these concerns. 20 participants screening positive for depression completed 4 weeks day of brief daily assessments of perceived symptom interactions. Quality criteria of this new method are introduced, answering questions such as "Which symptoms should be included in networks?", "How many datapoints need to be collected to achieve stable networks?", and "Does the network change over time?". Accordingly, about 40% of respondents achieved stable networks and only few respondents exhibited network structure that changed during the assessment period. The method was time-efficient (on average 7.4 min per day), and well received. Overall, L-PECAN addresses several of the prevailing issues found in statistical networks and therefore provides a clinically meaningful method for personalization.

Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances

Researchers and clinicians working within the Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition, Text Rev (DSM-5-TR) framework face a difficult question: what does it mean to have an evidence-based assessment of a nonevidence-based diagnostic construct? Alternative nosological approaches conceptualize psychopathology as (a) hierarchical, allowing researchers to move between levels of description and (b) dimensional, eliminating artificial dichotomies between disorders and the dichotomy between mental illness and mental well-being. In this article, we provide an overview of ongoing efforts to develop validated measures of transdiagnostic nosologies (i.e., the Hierarchical Taxonomy of Psychopathology; HiTOP) with applications for measurement-based care. However, descriptive models like HiTOP, which summarize patterns of covariation among psychopathology symptoms, do not address dynamic processes underlying the problems associated with psychopathology. Ambulatory assessment, well-suited to examine such dynamic processes, has also developed rapidly in recent decades. Thus, the goal of the current article is twofold. First, we provide a brief overview of developments in constructing valid measures of the HiTOP model as well as developments in ambulatory assessment practices. Second, we outline how these parallel developments can be integrated to advance measurement-based treatment. We end with a discussion of some major challenges for future research to address to integrate advances more fully in transdiagnostic and ambulatory assessment practices.

Assessment of Depression in Adults and Youth

This article selectively reviews the key issues and measures for the assessment of depressive disorders and symptoms in youth and adults. The first portion of the article addresses the nature and conceptualization of depression and some key issues that must be considered in its assessment. Next, the diagnostic interview and clinician- and self-administered rating scales that are most widely used to diagnose, screen for, and assess the severity of depression in adults and youth are selectively reviewed. In addition, the assessment of three transdiagnostic clinical features (anhedonia, irritability, and suicidality) that are frequently associated with both depression and other forms of psychopathology is discussed. The article concludes with some broad recommendations for assessing depression in research and clinical practice and suggestions for future research.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

The general factor of psychopathology (p): Choosing among competing models and interpreting p

Over the past 10 years, the general factor of psychopathology, p, has attracted interest and scrutiny. We review the history of the idea that all mental disorders share something in common, p; how we arrived at this idea; and how it became conflated with a statistical representation, the Bi-Factor Model. We then leverage the Environmental Risk (E-Risk) longitudinal twin study to examine the properties and nomological network of different statistical representations of p. We find that p performed similarly regardless of how it was modelled, suggesting that if the sample and content are the same the resulting p factor will be similar. We suggest that the meaning of p is not to be found by dueling over statistical models but by conducting well-specified criterion-validation studies and developing new measurement approaches. We outline new directions to refresh research efforts to uncover what all mental disorders have in common.

Understanding the complexity of individual developmental pathways: A primer on metaphors, models, and methods to study resilience in development

The modern study of resilience in development is conceptually based on a complex adaptive system ontology in which many (intersystem) factors are involved in the emergence of resilient developmental pathways. However, the methods and models developed to study complex dynamical systems have not been widely adopted, and it has recently been noted this may constitute a problem moving the field forward. In the present paper, I argue that an ontological commitment to complex adaptive systems is not only possible, but highly recommended for the study of resilience in development. Such a commitment, however, also comes with a commitment to a different causal ontology and different research methods. In the first part of the paper, I discuss the extent to which current research on resilience in development conceptually adheres to the complex systems perspective. In the second part, I introduce conceptual tools that may help researchers conceptualize causality in complex systems. The third part discusses idiographic methods that could be used in a research program that embraces the interaction dominant causal ontology and idiosyncratic nature of the dynamics of complex systems. The conclusion is that a strong ontological commitment is warranted, but will require a radical departure from nomothetic science.

Congruency of multimodal data-driven personalization with shared decision-making for StayFine: individualized app-based relapse prevention for anxiety and depression in young people

Tailoring interventions to the individual has been hypothesized to improve treatment efficacy. Personalization of target-specific underlying mechanisms might improve treatment effects as well as adherence. Data-driven personalization of treatment, however, is still in its infancy, especially concerning the integration of multiple sources of data-driven advice with shared decision-making. This study describes an innovative type of data-driven personalization in the context of StayFine, a guided app-based relapse prevention intervention for 13- to 21-year-olds in remission of anxiety or depressive disorders (n = 74). Participants receive six modules, of which three are chosen from five optional modules. Optional modules are Enhancing Positive Affect, Behavioral Activation, Exposure, Sleep, and Wellness. All participants receive Psycho-Education, Cognitive Restructuring, and a Relapse Prevention Plan. The personalization approach is based on four sources: (1) prior diagnoses (diagnostic interview), (2) transdiagnostic psychological factors (online self-report questionnaires), (3) individual symptom networks (ecological momentary assessment, based on a two-week diary with six time points per day), and subsequently, (4) patient preference based on shared decision-making with a trained expert by experience. This study details and evaluates this innovative type of personalization approach, comparing the congruency of advised modules between the data-driven sources (1-3) with one another and with the chosen modules during the shared decision-making process (4). The results show that sources of data-driven personalization provide complementary advice rather than a confirmatory one. The indications of the modules Exposure and Behavioral Activation were mostly based on the diagnostic interview, Sleep on the questionnaires, and Enhancing Positive Affect on the network model. Shared decision-making showed a preference for modules improving positive concepts rather than combating negative ones, as an addition to the data-driven advice. Future studies need to test whether treatment outcomes and dropout rates are improved through personalization.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling

Introduction

Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data.

Methods

A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation.

Results

Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values.

Conclusion

DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.

Transforming mental health systems globally: principles and policy recommendations

A burgeoning mental health crisis is emerging globally, regardless of each country's human resources or spending. We argue that effectively responding to this crisis is impeded by the dominant framing of mental ill health through the prism of diagnostic categories, leading to an excessive reliance on interventions that are delivered by specialists; a scarcity of widespread promotive, preventive, and recovery-oriented strategies; and failure to leverage diverse resources within communities. Drawing upon a series of syntheses, we identify five principles to transform current practices; namely, address harmful social environments across the life course, particularly in the early years; ensure that care is not contingent on a categorical diagnosis but aligned with the staging model of mental illness; empower diverse front-line providers to deliver psychosocial interventions; embrace a rights-based approach that seeks to provide alternatives to violence and coercion in care; and centre people with lived experience in all aspects of care. We recommend four policy actions which can transform these principles into reality: a whole of society approach to prevention and care; a redesign of the architecture of care delivery to provide a seamless continuum of care, tailored to the severity of the mental health condition; investing more in what works to enhance the impact and value of the investments; and ensuring accountability through monitoring and acting upon a set of mental health indicators. All these actions are achievable, relying-for the most part-on resources already available to every community and country. What they do require is the acceptance that business as usual will fail and the solutions to transforming mental health-care systems are already present within existing resources.

Real-time monitoring of increases in restlessness to assess idiographic risk of recurrence of depressive symptoms

BACKGROUND

This confirmatory study aimed to examine whether we can foresee recurrence of depressive symptoms using personalized modeling of rises in restlessness.

METHODS

Participants were formerly depressed patients (N = 41) in remission who (gradually) discontinued antidepressants. Participants completed five smartphone-based Ecological Momentary Assessments (EMA) a day, for a period of 4 months, yielding a total of 21 180 observations. Statistical Process Control by means of Exponentially Weighted Moving Average (EWMA) control charts was used to detect rises in the EMA item 'I feel restless', for each individual separately.

RESULTS

An increase in restlessness was detected in 68.3% of the participants with recurring depressive symptoms, and in 26.3% of those who stayed in remission (Fisher's exact test p = 0.01, sensitivity was 68.3%, specificity was 73.7%). In the participants with a recurrence and an increase in restlessness, this increase could be detected in the prodromal phase of depression in 93.3% of the cases and at least a month before the onset of the core symptoms of depression in 66.7% of the cases.

CONCLUSIONS

Restlessness is a common prodromal symptom of depression. The sensitivity and specificity of the EWMA charts was at least as good as prognostic models based on cross-sectional patient characteristics. An advantage of the current idiographic method is that the EWMA charts provide real-time personalized insight in a within-person increase in early signs of depression, which is key to alert the right patient at the right time.

Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach

BACKGROUND

Adolescence is characterized by profound change, including increases in negative emotions. Approximately 84% of American adolescents own a smartphone, which can continuously and unobtrusively track variables potentially predictive of heightened negative emotions (e.g. activity levels, location, pattern of phone usage). The extent to which built-in smartphone sensors can reliably predict states of elevated negative affect in adolescents is an open question.

METHODS

Adolescent participants (n = 22; ages 13-18) with low to high levels of depressive symptoms were followed for 15 weeks using a combination of ecological momentary assessments (EMAs) and continuously collected passive smartphone sensor data. EMAs probed negative emotional states (i.e. anger, sadness and anxiety) 2-3 times per day every other week throughout the study (total: 1145 EMA measurements). Smartphone accelerometer, location and device state data were collected to derive 14 discrete estimates of behavior, including activity level, percentage of time spent at home, sleep onset and duration, and phone usage.

RESULTS

A personalized ensemble machine learning model derived from smartphone sensor data outperformed other statistical approaches (e.g. linear mixed model) and predicted states of elevated anger and anxiety with acceptable discrimination ability (area under the curve (AUC) = 74% and 71%, respectively), but demonstrated more modest discrimination ability for predicting states of high sadness (AUC = 66%).

CONCLUSIONS

To the extent that smartphone data could provide reasonably accurate real-time predictions of states of high negative affect in teens, brief 'just-in-time' interventions could be immediately deployed via smartphone notifications or mental health apps to alleviate these states.

Prospective network analysis of proinflammatory proteins, lipid markers, and depression components in midlife community women

BACKGROUND

Vulnerability theories propose that suboptimal levels of lipid markers and proinflammatory proteins predict future heightened depression. Scar models posit the reverse association. However, most studies that tested relationships between non-specific immune/endocrine markers and depression did not separate temporal inferences between people and within-person and how different immunometabolism markers related to unique depression symptoms. We thus used cross-lagged prospective network analyses (CLPN) to investigate this topic.

METHODS

Community midlife women (n = 2224) completed the Center for Epidemiologic Studies-Depression scale and provided biomarker samples across five time-points spanning 9 years. CLPN identified significant relations (edges) among components (nodes) of depression (depressed mood, somatic symptoms, interpersonal issues), lipid markers [insulin, fasting glucose, triglycerides, low-density lipoprotein-cholesterol (LDL), high-density lipoprotein-cholesterol (HDL)], and proinflammatory proteins [C-reactive protein (CRP), fibrinogen], within and across time-points. All models adjusted for age, estradiol, follicle-stimulating hormone, and menopausal status.

RESULTS

In within-person temporal networks, higher CRP and HDL predicted all three depression components (d = 0.131-2.112). Increased LDL preceded higher depressed mood and interpersonal issues (v. somatic symptoms) (d = 0.251-0.327). Elevated triglycerides predicted more somatic symptoms (v. depressed mood and interpersonal problems) (d = 0.131). More interpersonal problems forecasted elevated fibrinogen and LDL levels (d = 0.129-0.331), and stronger somatic symptoms preceded higher fibrinogen levels (d = 0.188).

CONCLUSIONS

Results supported both vulnerability and scar models. Long-term dysregulated immunometabolism systems, social disengagement, and related patterns are possible mechanistic accounts. Cognitive-behavioral therapies that optimize nutrition and physical activity may effectively target depression.

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

Network analysis of anxiety and depressive symptoms one year after traumatic brain injury

We used network analysis to explore interrelationships between anxiety and depressive symptoms after traumatic brain injury (TBI). At one year post-injury, 882 adult civilians who received inpatient rehabilitation for moderate-severe TBI self-reported anxiety and depressive symptoms (Hospital Anxiety and Depression Scale). The severity of TBI was characterized acutely by the duration of post-traumatic amnesia (PTA), and TBI-related functional disability was rated by an examiner at one year post-injury using a structured interview (Glasgow Outcome Scale - Extended). We estimated two cross-sectional, partial correlation networks. In the first network, anxiety and depressive symptoms were densely interconnected yet formed three distinct, data-driven communities: Hyperarousal, Depression, and General Distress. Worrying thoughts and having difficulty relaxing were amongst the most central symptoms, showing strong connections with other symptoms within and between communities. In the second network, TBI severity was directly negatively associated with hyperarousal symptoms but indirectly positively associated with depressive symptoms via greater functional disability. The results highlight the potential utility of simultaneous, transdiagnostic assessment and treatment of anxiety and depressive symptoms after moderate-severe TBI. Worrying thoughts, having difficulty relaxing, and the experience of disability may be important targets for treatment, although future studies examining symptom dynamics within individuals and over time are required.

The Detection of Acute Risk of Self-injury Project: Protocol for an Ecological Momentary Assessment Study Among Individuals Seeking Treatment

BACKGROUND

Nonsuicidal self-injury (NSSI) is a major mental health concern. Despite increased research efforts on establishing the prevalence and correlates of the presence and severity of NSSI, we still lack basic knowledge of the course, predictors, and relationship of NSSI with other self-damaging behaviors in daily life. Such information will be helpful for better informing mental health professionals and allocating treatment resources. The DAILY (Detection of Acute rIsk of seLf-injurY) project will address these gaps among individuals seeking treatment.

OBJECTIVE

This protocol paper presents the DAILY project's aims, design, and materials used. The primary objectives are to advance understanding of (1) the short-term course and contexts of elevated risk for NSSI thoughts, urges, and behavior; (2) the transition from NSSI thoughts and urges to NSSI behavior; and (3) the association of NSSI with disordered eating, substance use, and suicidal thoughts and behaviors. A secondary aim is to evaluate the perspectives of individuals seeking treatment and mental health professionals regarding the feasibility, scope, and utility of digital self-monitoring and interventions that target NSSI in daily life.

METHODS

The DAILY project is funded by the Research Foundation Flanders (Belgium). Data collection involves 3 phases: a baseline assessment (phase 1), 28 days of ecological momentary assessment (EMA) followed by a clinical session and feedback survey (phase 2), and 2 follow-up surveys and an optional interview (phase 3). The EMA protocol consists of regular EMA surveys (6 times per day), additional burst EMA surveys spaced at a higher frequency when experiencing intense NSSI urges (3 surveys within 30 minutes), and event registrations of NSSI behavior. The primary outcomes are NSSI thoughts, NSSI urges, self-efficacy to resist NSSI, and NSSI behavior, with disordered eating (restrictive eating, binge eating, and purging), substance use (binge drinking and smoking cannabis), and suicidal thoughts and behaviors surveyed as secondary outcomes. The assessed predictors include emotions, cognitions, contextual information, and social appraisals.

RESULTS

We will recruit approximately 120 individuals seeking treatment aged 15 to 39 years from mental health services across the Flanders region of Belgium. Recruitment began in June 2021 and data collection is anticipated to conclude in August 2023.

CONCLUSIONS

The findings of the DAILY project will provide a detailed characterization of the short-term course and patterns of risk for NSSI and advance understanding of how, why, and when NSSI and other self-damaging behaviors unfold among individuals seeking treatment. This will inform clinical practice and provide the scientific building blocks for novel intervention approaches outside of the therapy room that support people who self-injure in real time.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/46244.

The use of advanced technology and statistical methods to predict and prevent suicide

In the past decade, two themes have emerged across suicide research. First, according to meta-analyses, the ability to predict and prevent suicidal thoughts and behaviours is weaker than would be expected for the size of the field. Second, review and commentary papers propose that technological and statistical methods (such as smartphones, wearables, digital phenotyping and machine learning) might become solutions to this problem. In this Review, we aim to strike a balance between the pessimistic picture presented by these meta-analyses and the optimistic picture presented by review and commentary papers about the promise of advanced technological and statistical methods to improve the ability to understand, predict and prevent suicide. We divide our discussion into two broad categories. First, we discuss the research aimed at assessment, with the goal of better understanding or more accurately predicting suicidal thoughts and behaviours. Second, we discuss the literature that focuses on prevention of suicidal thoughts and behaviours. Ecological momentary assessment, wearables and other technological and statistical advances hold great promise for predicting and preventing suicide, but there is much yet to do.

A cultural-ecosocial systems view for psychiatry

While contemporary psychiatry seeks the mechanisms of mental disorders in neurobiology, mental health problems clearly depend on developmental processes of learning and adaptation through ongoing interactions with the social environment. Symptoms or disorders emerge in specific social contexts and involve predicaments that cannot be fully characterized in terms of brain function but require a larger social-ecological view. Causal processes that result in mental health problems can begin anywhere within the extended system of body-person-environment. In particular, individuals' narrative self-construal, culturally mediated interpretations of symptoms and coping strategies as well as the responses of others in the social world contribute to the mechanisms of mental disorders, illness experience, and recovery. In this paper, we outline the conceptual basis and practical implications of a hierarchical ecosocial systems view for an integrative approach to psychiatric theory and practice. The cultural-ecosocial systems view we propose understands mind, brain and person as situated in the social world and as constituted by cultural and self-reflexive processes. This view can be incorporated into a pragmatic approach to clinical assessment and case formulation that characterizes mechanisms of pathology and identifies targets for intervention.

Individual differences in computational psychiatry: a review of current challenges

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is the development of computational assays: integrating computational models with cognitive tasks to infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements in computational modelling and many cross-sectional patient studies, much less attention has been paid to basic psychometric properties (reliability and construct validity) of the computational measures provided by the assays. In this review, we assess the extent of this issue by examining emerging empirical evidence. We find that many computational measures suffer from poor psychometric properties, which poses a risk of invalidating previous findings and undermining ongoing research efforts using computational assays to study individual (and even group) differences. We provide recommendations for how to address these problems and, crucially, embed them within a broader perspective on key developments that are needed for translating computational assays to clinical practice.

Dissecting the depressed mood criterion in adult depression: The heterogeneity of mood disturbances in major depressive episodes

BACKGROUND

Mood disturbances have historically remained a core criterion in diagnosing major depressive episode. DSMs have illustrated this criterion with depressed, hopeless, discouraged, cheerless, and irritable mood, suggesting interchangeability. Extant research has examined individual forms of mood disturbance to depression severity. Less examined is the heterogeneity in mood disturbances and its implication to their association to depression presentations and outcomes.

METHOD

The current study used a nationally representative sample of U.S. adults with unipolar major depressive disorder to study the association between specific forms of mood disturbances to depression severity, chronicity, or symptoms, above and beyond other forms, as well as their relations to functional impairment, suicidal outcomes, and psychiatric comorbidity via generalized linear models.

RESULTS

Cheerless and hopeless mood were associated with depression severity. Hopeless and irritable mood were associated with depression chronicity. Different forms of mood disturbance showed differential relations to depressive symptoms. Cheerless, hopeless, and irritable mood were associated with depression-specific functional interference, incremental to depression severity. Cheerless, hopeless, and discouraged mood were associated with passive suicidal ideation. Hopeless mood was associated with active suicidal ideation. Hopeless and irritable mood were associated with both suicide plan and suicide attempt. Different forms of mood disturbance demonstrated differential associations to comorbid psychiatric conditions.

DISCUSSION

The relations between different forms of mood disturbances and various aspects of depression are nuanced. Theoretically, these relations highlight the potential utility in acknowledging the complexity and heterogeneity in mood disturbances. Clinically, our results suggest potential utility in routinely monitoring mood disturbances.

A randomized controlled trial of a 14-day mindfulness ecological momentary intervention (MEMI) for generalized anxiety disorder

BACKGROUND

Little is known about whether brief mindfulness ecological momentary interventions (MEMIs) yield clinically beneficial effects. This gap exists despite the rapid growth of smartphone mindfulness applications. Specifically, no prior brief MEMI has targeted generalized anxiety disorder (GAD). Moreover, although theories propose that MEMIs can boost executive functioning (EF), they have largely gone untested. Thus, this randomized controlled trial (RCT) aimed to address these gaps by assessing the efficacy of a 14-day smartphone MEMI (versus self-monitoring placebo [SMP]).

METHOD

Participants with GAD were randomly assigned to either condition (68 MEMI and 42 SMP). MEMI participants exercised multiple core mindfulness strategies and were instructed to practice mindfulness continually. Comparatively, SMP participants were prompted to practice self-monitoring and were not taught any mindfulness strategies. All prompts occurred five times a day for 14 consecutive days. Participants completed self-reports and neuropsychological assessments at baseline, posttreatment, and 1-month follow-up (1MFU). Piecewise hierarchical linear modeling analyses were conducted.

RESULTS

MEMI (versus SMP) produced greater pre-1MFU reductions in GAD severity and perseverative cognitions (between-group d = 0.393-0.394) and stronger improvements in trait mindfulness and performance-based inhibition (d = 0.280-0.303). Further, MEMI (versus SMP) led to more considerable pre- to posttreatment reduction in state-level depression and anxiety and more mindfulness gains (d = 0.50-1.13). Overall, between-treatment effects were stronger at pre-1MFU than pre- to posttreatment for trait-level than state-level treatment outcome measures.

CONCLUSIONS

Preliminary findings suggest that the beneficial effect of an unguided brief MEMI to target pathological worry, trait mindfulness, and EF is modest yet potentially meaningful. Other theoretical and clinical implications were discussed.

Multidimensional emotional disorders inventory: Reliability and validity in a Spanish clinical sample

BACKGROUND

The categorical approach to diagnosing mental disorders has been criticized for a number of reasons (e.g., high rates of comorbidity; larger number of diagnostic categories and combination). Diverse alternatives have been proposed using a hybrid or totally dimensional perspective. Despite the evidence supporting use of the Multidimensional Emotional Disorders Inventory (MEDI) for assessing the transdiagnostic dimensions of Emotional Disorders using a dimensional-categorical hybrid approach, no data exist on Spanish clinical samples. The present study explores the validity and reliability of the 49-item MEDI in a clinical sample and provides data for its use.

METHODS

A total of 280 outpatients with emotional disorders attended in different Spanish public Mental Health Units in Spain filled out all questionnaires during the assessment phase and the MEDI again one week after. The instruments used evaluate four main constructs: personality, mood, anxiety and avoidance.

RESULTS

The nine original factors were confirmed and showed adequate reliability (α: 0.66-0.91) and stability (r = 0.76-0.87). No differences in mean scores by sex were presented in any subscale (p ≥ .07). The MEDI subscales correlated significantly with the scales of each of the selected constructs (0.45 < r < 0.76).

LIMITATIONS

The main limitations of this study were the limited sample size and not being able to count on MEDI scores post-transdiagnostic intervention.

CONCLUSIONS

The MEDI demonstrates adequate reliability and validity. It allows to assess diverse symptoms efficiently, thus being of interest for clinical studies and practice.

Emotionally focused couple therapy in cancer survivor couples with marital and sexual problems: a replicated single-case experimental design

Objective

The current research examined the effect of Emotionally Focused Couples Therapy (EFCT) on perceived intimacy, affect, and dyadic connection in cancer survivor couples with relationship challenges.

Method

In this longitudinal replicated single-case study, positive and negative affect, intimacy, partner responsiveness, and expression of attachment-based emotional needs were reported every 3 days before and during treatment. Thirteen couples, with one partner having survived colorectal cancer or breast cancer, participated for the full duration of the study. Statistical analysis of the data was performed using randomization tests, piecewise regression, and multilevel analyses.

Results

Adherence to the therapeutic protocol was tested and found adequate. Compared with baseline, significant positive effects on affect variables were found during the therapeutic process. Positive affect increased and negative affect decreased. Partner responsiveness, perceived intimacy, and the expression of attachment-based emotional needs improved, but only in the later phase of treatment. Results at the group level were statistically significant, whereas effects at the individual level were not.

Discussion

This study found positive group-level effects of EFCT on affect and dyadic outcome measures in cancer survivors. The positive results warrant further research, including randomized clinical trials, to replicate these effects of EFCT in cancer survivor couples experiencing marital and sexual problems.

Linking Affective and Hearing Sciences-Affective Audiology

A growing number of health-related sciences, including audiology, have increasingly recognized the importance of affective phenomena. However, in audiology, affective phenomena are mostly studied as a consequence of hearing status. This review first addresses anatomical and functional bidirectional connections between auditory and affective systems that support a reciprocal affect-hearing relationship. We then postulate, by focusing on four practical examples (hearing public campaigns, hearing intervention uptake, thorough hearing evaluation, and tinnitus), that some important challenges in audiology are likely affect-related and that potential solutions could be developed by inspiration from affective science advances. We continue by introducing useful resources from affective science that could help audiology professionals learn about the wide range of affective constructs and integrate them into hearing research and clinical practice in structured and applicable ways. Six important considerations for good quality affective audiology research are summarized. We conclude that it is worthwhile and feasible to explore the explanatory power of emotions, feelings, motivations, attitudes, moods, and other affective processes in depth when trying to understand and predict how people with hearing difficulties perceive, react, and adapt to their environment.

Dynamics of daily positive and negative affect and relations to anxiety and depression symptoms in a transdiagnostic clinical sample

BACKGROUND

Despite interest in transdiagnostic dimensional approaches to psychopathology, little is known about the dynamic interplay of affecting and internalizing symptoms that cut across diverse mental health disorders. We examined within-person reciprocal effects of negative and positive affect (NA, PA) and symptoms (depression and anxiety), and their between-person associations with affective dynamics (i.e., affect inertia).

METHODS

Individuals currently receiving treatment for psychological disorders (N = 776) completed daily assessments of affect and symptoms across 14 treatment days (average). We used dynamic structural equation modeling to examine daily affect-symptom dynamics.

RESULTS

Within-person results indicated NA-symptom reciprocal effects; PA only predicted subsequent depression symptoms. After accounting for changes in mean symptoms and affect over time, NA-anxiety and PA-depression relations remained particularly robust. Between-person correlations indicated NA inertia was positively associated with NA-symptom effects; PA inertia was negatively associated with PA-symptoms effects.

CONCLUSIONS

Results suggest that transdiagnostic affective treatment approaches may be more useful for reducing internalizing symptoms by decreasing NA compared to increasing PA. Individual differences in resistance to shifting out of affective states (i.e., high NA vs. PA inertia) may be a useful marker for developing tailored interventions.

Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer

Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.

Transitions in depression: if, how, and when depressive symptoms return during and after discontinuing antidepressants

Purpose

The aim of the current study is to provide insight into if, how, and when meaningful changes occur in individual patients who discontinue antidepressant medication. Agreement between macro-level quantitative symptom data, qualitative ratings, and micro-level Ecological Momentary Assessments is examined.

Methods

During and shortly after antidepressant discontinuation, depressive symptoms and ‘feeling down’ were measured in 56 participants, using the SCL-90 depression subscale weekly (macro-level) for 6 months, and 5 Ecological Momentary Assessments daily (micro-level) for 4 months (30.404 quantitative measurements in total). Qualitative information was also obtained, providing additional information to verify that changes were clinically meaningful.

Results

At the macro-level, an increase in depressive symptoms was found in 58.9% of participants that (a) was statistically reliable, (b) persisted for 3 weeks and/or required intervention, and (c) was clinically meaningful to patients. Of these increases, 30.3% happened suddenly, 42.4% gradually, and for 27.3% criteria were inconclusive. Quantitative and qualitative criteria showed a very high agreement (Cohen’s κ = 0.85) regarding if a participant experienced a recurrence of depression, but a moderate agreement (Cohen’s κ = 0.49) regarding how that change occurred. At the micro-level, 41.1% of participants experienced only sudden increases in depressed mood, 12.5% only gradual, 30.4% experienced both types of increase, and 16.1% neither.

Conclusion

Meaningful change is common in patients discontinuing antidepressants, and there is substantial heterogeneity in how and when these changes occur. Depressive symptom change at the macro-level is not the same as depressive symptom change at the micro-level.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment

INTRODUCTION

Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising.

METHODS

We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( N = 65 patients recruited). Both passive mobile sensor data as well as ecological momentary assessments were available for modelling. We evaluated the models' ability to predict symptoms of depression (Patient Health Questionnaire-2; PHQ-2) at the end of each day, and to forecast symptoms of the next day.

RESULTS

In our experiments, we achieve a best mean-absolute-error (MAE) of 0.801 (25% improvement) for predicting PHQ-2 values at the end of the day with subject-dependent standardisation compared to a non-personalised baseline ( MAE = 1.062 ). For one day ahead-forecasting, we can improve the baseline of 1.539 by 12 % to a MAE of 1.349 using a transfer learning approach with shared common layers. In addition, personalisation leads to fairer models at group-level.

DISCUSSION

Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.

Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science

This article considers the status and study of "context" in psychological science through the lens of research on emotional expressions. The article begins by updating three well-trod methodological debates on the role of context in emotional expressions to reconsider several fundamental assumptions lurking within the field's dominant methodological tradition: namely, that certain expressive movements have biologically prepared, inherent emotional meanings that issue from singular, universal processes which are independent of but interact with contextual influences. The second part of this article considers the scientific opportunities that await if we set aside this traditional understanding of "context" as a moderator of signals with inherent psychological meaning and instead consider the possibility that psychological events emerge in ecosystems of signal ensembles, such that the psychological meaning of any individual signal is entirely relational. Such a fundamental shift has radical implications not only for the science of emotion but for psychological science more generally. It offers opportunities to improve the validity and trustworthiness of psychological science beyond what can be achieved with improvements to methodological rigor alone. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being

Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

BACKGROUND

Smartphone self-monitoring of mood, symptoms, and contextual factors through ecological momentary assessment (EMA) provides insights into the daily lives of people undergoing psychiatric treatment. Therefore, EMA has the potential to improve their care. To integrate EMA into treatment, a clinical tool that helps clients and clinicians create personalized EMA diaries and interpret the gathered data is needed.

OBJECTIVE

This study aimed to develop a web-based application for personalized EMA in specialized psychiatric care in close collaboration with all stakeholders (ie, clients, clinicians, researchers, and software developers).

METHODS

The participants were 52 clients with mood, anxiety, and psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses). We engaged them in interviews, focus groups, and usability sessions to determine the requirements for an EMA web application and repeatedly obtained feedback on iteratively improved high-fidelity EMA web application prototypes. We used human-centered design principles to determine important requirements for the web application and designed high-fidelity prototypes that were continuously re-evaluated and adapted.

RESULTS

The iterative development process resulted in Personalized Treatment by Real-time Assessment (PETRA), which is a scientifically grounded web application for the integration of personalized EMA in Dutch clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, an SMS text message-based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated into electronic health record systems to ensure ease of use and sustainable integration in clinical care and adheres to privacy regulations.

CONCLUSIONS

PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this codevelopment process, its extensive but user-friendly personalization options, its integration into electronic health record systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care requires further research. As such, PETRA paves the way for a systematic investigation of the utility of personalized EMA for routine mental health care.