Psychotic exacerbation and emotional dampening in the daily life of patients with schizophrenia switched to aripiprazole therapy: a collection of standardized case reports

The experience sampling methodology as a digital clinical tool for more person-centered mental health care: an implementation research agenda

This position paper by the international IMMERSE consortium reviews the evidence of a digital mental health solution based on Experience Sampling Methodology (ESM) for advancing person-centered mental health care and outlines a research agenda for implementing innovative digital mental health tools into routine clinical practice. ESM is a structured diary technique recording real-time self-report data about the current mental state using a mobile application. We will review how ESM may contribute to (1) service user engagement and empowerment, (2) self-management and recovery, (3) goal direction in clinical assessment and management of care, and (4) shared decision-making. However, despite the evidence demonstrating the value of ESM-based approaches in enhancing person-centered mental health care, it is hardly integrated into clinical practice. Therefore, we propose a global research agenda for implementing ESM in routine mental health care addressing six key challenges: (1) the motivation and ability of service users to adhere to the ESM monitoring, reporting and feedback, (2) the motivation and competence of clinicians in routine healthcare delivery settings to integrate ESM in the workflow, (3) the technical requirements and (4) governance requirements for integrating these data in the clinical workflow, (5) the financial and competence related resources related to IT-infrastructure and clinician time, and (6) implementation studies that build the evidence-base. While focused on ESM, the research agenda holds broader implications for implementing digital innovations in mental health. This paper calls for a shift in focus from developing new digital interventions to overcoming implementation barriers, essential for achieving a true transformation toward person-centered care in mental health.

Modelling the temporal interplay between stress and affective disturbances in pathways to psychosis: an experience sampling study

BACKGROUND

One putative psychological mechanism through which momentary stress impacts on psychosis in individuals with increased liability to the disorder is via affective disturbance. However, to date, this has not been systematically tested. We aimed to investigate whether (i) cross-sectional and temporal effects of momentary stress on psychotic experiences via affective disturbance, and (ii) the reverse pathway of psychotic experiences on stress via affective disturbance were modified by familial liability to psychosis.

METHODS

The Experience Sampling Method was used in a pooled data set of six studies with three groups of 245 individuals with psychotic disorder, 165 unaffected first-degree relatives, and 244 healthy control individuals to index familial liability. Multilevel moderated mediation models were fitted to investigate indirect effects across groups cross-sectionally and multilevel cross-lagged panel models to investigate temporal effects in the proposed pathways across two measurement occasions.

RESULTS

Evidence on indirect effects from cross-sectional models indicated that, in all three groups, effects of stress on psychotic experiences were mediated by negative affect and, vice versa, effects of psychotic experiences on stress were mediated by negative affect, with all indirect effects being weakest in relatives. Longitudinal modelling of data provided no evidence of temporal priority of stress in exerting its indirect effects on psychotic experiences via affective disturbance or, vice versa.

CONCLUSIONS

Our findings tentatively suggest a rapid vicious cycle of stress impacting psychotic experiences via affective disturbances, which does, however, not seem to be consistently modified by familial liability to psychosis.

Reported Affect Changes as a Function of Response Delay: Findings From a Pooled Dataset of Nine Experience Sampling Studies

Delayed responses are a common phenomenon in experience sampling studies. Yet no consensus exists on whether they should be excluded from the analysis or what the threshold for exclusion should be. Delayed responses could introduce bias, but previous investigations of systematic differences between delayed and timely responses have offered unclear results. To investigate differences as a function of delay, we conducted secondary analyses of nine paper and pencil based experience sampling studies including 1,528 individuals with different clinical statuses. In all participants, there were significant decreases in positive and increases in negative affect as a function of delay. In addition, delayed answers of participants without depression showed higher within-person variability and an initial strengthening in the relationships between contextual stress and affect. Participants with depression mostly showed the opposite pattern. Delayed responses seem qualitatively different from timely responses. Further research is needed to understand the mechanisms underlying these differences.

We Really Need Clear Guidelines and Recommendations for Safer and Proper Use of Aripiprazole and Risperidone in a Pediatric Population: Real-World Analysis of EudraVigilance Database

Background: Although aripiprazole and risperidone are used widespread in pediatrics, there are still limited pieces of evidence on their actual safety profile. By using the EudraVigilance database, we carried out an analysis to perform a comprehensive overview of reported adverse events among children and adolescents treated with aripiprazole and risperidone. Methods: Descriptive analysis was performed of all individual case safety reports (ISCRs) submitted to EudraVigilance associated with aripiprazole and risperidone and related to the pediatric population from 2016 to 2018. Results: A total of 855 and 2,242 ISCRs for aripiprazole and risperidone, respectively, were recorded for a total of 11,042 suspected adverse drug reactions (2,993 for aripiprazole and 8,049 for risperidone). Most ISCRs were related to male patients (65.0 and 86.3% for aripiprazole and risperidone, respectively) and were serious (81.0 and 94.1% for aripiprazole and risperidone, respectively). Schizophrenia spectrum and other psychotic disorders, such as disruptive, impulse-control, and conduct disorders, and autism spectrum disorder were the top three clinical indications for aripiprazole (19.0, 16.1, and 11.6%, respectively). For risperidone, attention-deficit/hyperactivity disorder (25.4%), disruptive, impulse-control, and conduct disorders (17.1%), and bipolar and related disorders (14.2%) were more commonly reported as clinical indications. Data also showed a high proportion of use for clinical conditions not authorized in children. Psychiatric disorders were the main related adverse events for aripiprazole (20.2%), and among these, suicidal behavior was one of the most reported (14.9%). Reproductive system and breast disorders were the main related adverse events for risperidone (19.8%), and gynecomastia was the most reported event; metabolism and nutrition disorders, mainly reported as weight gain disorders, were more reported in children (3-11 years) than in adolescents (12-17 years). Conclusions: Our results demonstrate that spontaneously reported adverse events associated with aripiprazole and risperidone reflect what is already known in terms of safety profile, although with about 90% of them being serious. This analysis stresses the need for further studies and effective training and information activities to better define the actual benefit/risk ratio of these drugs in pediatric patients.

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modeling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression, and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy = 82% and sensitivity = 82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future.

The Cascade of Stress: A Network Approach to Explore Differential Dynamics in Populations Varying in Risk for Psychosis

Stress plays a central role in the development and persistence of psychosis. Network analysis may help to reveal mechanisms at the level of the micro-dynamic effects between stress, other daily experiences and symptomatology. This is the first study to examine time-lagged networks of the relations between minor daily stress, momentary affect/thoughts, psychotic experiences, and other potentially relevant daily life contexts in individuals varying in risk for psychosis. Intensive longitudinal data were obtained through 6 studies. The combined sample consisted of 654 individuals varying in risk for psychosis: healthy control subjects (n = 244), first-degree relatives of psychotic patients (n = 165), and psychotic patients (n = 245). Using multilevel models combined with permutation testing, group-specific time-lagged network connections between daily experiences were compared between groups. Specifically, the role of stress was examined. Risk for psychosis was related to a higher number of significant network connections. In all populations, stress had a central position in the network and showed direct and significant connections with subsequent psychotic experiences. Furthermore, the higher the risk for psychosis, the more variables "loss of control" and "suspicious" were susceptible to influences by other network nodes. These findings support the idea that minor daily stress may play an important role in inducing a cascade of effects that may lead to psychotic experiences.

The experience sampling method as an mHealth tool to support self-monitoring, self-insight, and personalized health care in clinical practice

BACKGROUND

The experience sampling method (ESM) builds an intensive time series of experiences and contexts in the flow of daily life, typically consisting of around 70 reports, collected at 8-10 random time points per day over a period of up to 10 days.

METHODS

With the advent of widespread smartphone use, ESM can be used in routine clinical practice. Multiple examples of ESM data collections across different patient groups and settings are shown and discussed, varying from an ESM evaluation of a 6-week randomized trial of mindfulness, to a twin study on emotion dynamics in daily life.

RESULTS

Research shows that ESM-based self-monitoring and feedback can enhance resilience by strengthening the capacity to use natural rewards. Personalized trajectories of starting or stopping medication can be more easily initiated and predicted if sensitive feedback data are available in real time. In addition, personalized trajectories of symptoms, cognitive abilities, symptoms impacting on other symptoms, the capacity of the dynamic system of mental health to "bounce back" from disturbance, and patterns of environmental reactivity yield uniquely personal data to support shared decision making and prediction in clinical practice. Finally, ESM makes it possible to develop insight into previous implicit patterns of thought, experience, and behavior, particularly if rapid personalized feedback is available.

CONCLUSIONS

ESM enhances clinical practice and research. It is empowering, providing co-ownership of the process of diagnosis, treatment evaluation, and routine outcome measurement. Blended care, based on a mix of face-to-face and ESM-based outside-the-office treatment, may reduce costs and improve outcomes.

Do people with schizophrenia experience more negative emotion and less positive emotion in their daily lives? A meta-analysis of experience sampling studies

Research on emotion experience in response to valenced stimuli has consistently shown that people with schizophrenia have the capacity to experience emotion. Specifically, people with schizophrenia report similar experiences to both positive and negative emotion-eliciting stimuli as individuals without the disorder. However, it is less clear if people with schizophrenia experience similar levels of positive emotion and negative emotion outside of standardized laboratory contexts, as in their daily lives. One reliable method for assessing emotion experience in schizophrenia has been the Experience Sampling Method (ESM), or Ecological Momentary Assessment (EMA). Using the PRISMA guidelines for meta-analysis, we reviewed the literature for all studies that included people with and without schizophrenia, and that included a positive or negative emotion assessment during participants' daily lives. The current study is a meta-analysis of 12 EMA studies of emotion experience, which included a total of 619 people with schizophrenia and 730 healthy controls. Results indicate that people with schizophrenia consistently report more negative and less positive emotion than healthy control participants. These findings differ from laboratory-based studies, which may be due to several factors, including environmental differences, effects of the disorder that appear more clearly in daily life, or additional concerns, such as depression, which has been shown to be related to negative emotion in schizophrenia. Importantly, these findings are in line with questionnaire-based measures of emotion experience, lending some support for their use in research and clinical settings.

An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis

INTRODUCTION

Dynamic relationships between the symptoms of psychosis can be shown in individual networks of psychopathology. In a single patient, data collected with the Experience Sampling Method (ESM-a method to construct intensive time series of experience and context) can be used to study lagged associations between symptoms in relation to illness severity and pharmacological treatment.

METHOD

The patient completed, over the course of 1 year, for 4 days per week, 10 daily assessments scheduled randomly between 10 minutes and 3 hours apart. Five a priori selected symptoms were analysed: 'hearing voices', 'down', 'relaxed', 'paranoia' and 'loss of control'. Regression analysis was performed including current level of one symptom as the dependent variable and all symptoms at the previous assessment (lag) as the independent variables. Resulting regression coefficients were printed in graphs representing a network of symptoms. Network graphs were generated for different levels of severity: stable, impending relapse and full relapse.

RESULTS

ESM data showed that symptoms varied intensely from moment to moment. Network representations showed meaningful relations between symptoms, e.g. 'down' and 'paranoia' fuelling each other, and 'paranoia' negatively impacting 'relaxed'. During relapse, symptom levels as well as the level of clustering between symptoms markedly increased, indicating qualitative changes in the network. While 'hearing voices' was the most prominent symptom subjectively, the data suggested that a strategic focus on 'paranoia', as the most central symptom, had the potential to bring about changes affecting the whole network.

CONCLUSION

Construction of intensive ESM time series in a single patient is feasible and informative, particularly if represented as a network, showing both quantitative and qualitative changes as a function of relapse.

Experience sampling and ecological momentary assessment studies in psychopharmacology: A systematic review

Experience sampling methods (ESM) and ecological momentary assessment (EMA) offer insight into daily life experiences, including symptoms of mental disorders. The application of ESM/EMA in psychopharmacology can be a valuable addition to more traditional measures such as retrospective self-report questionnaires because they may help reveal the impact of psychotropic medication on patients' actual experiences. In this paper we systematically review the existing literature on the use of ESM/EMA in psychopharmacology research. To this end, we searched the PsycInfo and Medline databases for all available ESM/EMA studies on the use of psychotropic medication in patients with DSM-III-R and DSM-IV disorders. Dissertations were excluded. We included 18 studies that applied ESM/EMA to study the effects of medication on patients with major depressive disorder, substance use disorder, attention-deficit hyperactivity disorder, psychotic disorder, and anxiety disorder. We found that ESM/EMA may allow researchers and clinicians to track patients during different phases of treatment: before treatment to predict outcome, during treatment to examine the effects of treatment on symptoms and different aspects of daily life experience, and after treatment to detect vulnerability for relapse. Moreover, ESM/EMA can potentially help determine how long and in what contexts medications are effective. Thus, ESM/EMA may benefit both researchers and clinicians and might prove to be an effective tool for improving the treatment of psychiatric patients.

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach

BACKGROUND

It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques.

METHOD

A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters.

RESULTS

Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics.

CONCLUSIONS

Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.