Study protocol for a prospective cohort study examining the predictive potential of dynamic symptom networks for the onset and progression of psychosis: the Mapping Individual Routes of Risk and Resilience (Mirorr) study

The associations between daily reports of loneliness and psychotic experiences in the early risk stages for psychosis

AIM

Bi-directional associations between loneliness and psychotic experiences (PEs) have been reported, but the mechanisms underlying these associations are unknown. This study aims to explore associations between daily reports of loneliness and PEs, and test differences in this association across young adult individuals at different levels of risk for psychosis.

METHODS

We analysed 90-day diary data on loneliness and PEs from N = 96 participants (mean age 24.7, range 18-35, 77% female) divided into 4 subgroups, each indexing increased levels of risk for psychosis according to the clinical staging model: 'psychometric' (n = 25), 'low' (n = 27), 'mild' (n = 24), and 'ultra-high'(n = 20) risk. Multilevel vector autoregressive models examined within-day (contemporaneous) and between-day (temporal) associations between loneliness and PEs for the total sample. Next, these associations were compared across subgroups.

RESULTS

Loneliness and PEs were significantly associated contemporaneously (partial correlation B = 0.14) but not temporally. Subgroup membership moderated both contemporaneous and temporal associations. The contemporaneous association between loneliness and PEs was stronger in the low-risk subgroup compared to the mild-risk (B = -0.35, p < .01) and ultra-high-risk (B = -0.36, p < .01) subgroups. The temporal association between loneliness on the previous day and PEs on the current day was stronger in mild-risk subgroup compared to the ultra-high-risk subgroup (B = -0.03, p = .03). After adjusting for multiple testing, only the contemporaneous-but not the temporal-associations remained statistically significant.

CONCLUSIONS

Loneliness is associated with PEs in individuals at risk for psychosis, particularly in those with low to mild symptoms. Our findings tentatively suggest that especially individuals with low expressions of PEs may be more sensitive to social context, but future studies are needed to replicate and further unravel the potentially stage-specific interplay between social context and PEs.

The dynamics of social activation and suspiciousness in individuals at ultra-high risk for psychosis

INTRODUCTION

Social functioning is often impaired during the ultra-high risk (UHR) phase for psychosis, but group-level studies regarding the role of social functioning in transition to psychosis are inconsistent. Exploring the inter-individual differences which underlie the association between social functioning and psychotic symptoms in this phase could yield new insights.

OBJECTIVE

To examine the idiographic and dynamic association between social activation and suspiciousness in individuals at UHR for psychosis using time-series analysis.

METHODS

Twenty individuals at UHR for psychosis completed a diary application every evening for 90 days. Two items on social activation (quantity: 'time spent alone' and quality: 'feeling supported') and two items on suspiciousness ('feeling suspicious' and 'feeling disliked') were used. Time series (T = 90) of each individual were analyzed using vector auto regression analysis (VAR), to estimate the lagged (over 1 day) effect of social activation on suspiciousness, and vice versa, as well as their contemporaneous associations.

RESULTS

Heterogeneous person-specific associations between social activation and suspiciousness were found in terms of strength, direction and temporal aspects.

CONCLUSIONS

The association between social activation and suspiciousness differs amongst individuals who are at UHR for psychosis. These findings underline the importance of tailoring psychosocial interventions to the individual. Future studies may examine whether using results of single-subject studies in clinical practice to personalize treatment goals leads to better treatment outcomes.

Relating stability of individual dynamical networks to change in psychopathology

One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods.

The added value of daily diary data in 1- and 3-year prediction of psychopathology and psychotic experiences in individuals at risk for psychosis

This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.

The dynamic relationship between sleep and psychotic experiences across the early stages of the psychosis continuum

BACKGROUND

Psychotic disorders develop gradually along a continuum of severity. Understanding factors associated with psychosis development, such as sleep, could aid in identification of individuals at elevated risk. This study aimed to assess (1) the dynamic relationship between psychotic experiences (PEs) and sleep quality and quantity, and (2) whether this relationship differed between different clinical stages along the psychosis continuum.

METHODS

We used daily diary data (90 days) of individuals (N = 96) at early stages (i.e. before a first diagnosis of psychosis) along the psychosis continuum. Multilevel models were constructed with sleep quality and sleep quantity as predictors of PEs and vice versa. Post-hoc, we constructed a multilevel model with both sleep quality and quantity as predictors of PEs. In addition, we tested whether associations differed between clinical stages.

RESULTS

Within persons, poorer sleep predicted next day PEs (B = -0.02, p = 0.01), but not vice versa. Between persons, shorter sleep over the 90-day period predicted more PEs (B = -0.04, p = 0.002). Experiencing more PEs over 90-days predicted poorer (B = -0.02, p = 0.02) and shorter (B = -1.06, p = 0.008) sleep. We did not find any significant moderation effects for clinical stage.

CONCLUSIONS

We found a bidirectional relationship between sleep and PEs with daily fluctuations in sleep predicting next day PEs and general patterns of more PEs predicting poorer and shorter sleep. Our results highlight the importance of assessing sleep as a risk marker in the early clinical stages for psychosis.

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.

Reflections on psychological resilience: a comparison of three conceptually different operationalizations in predicting mental health

BACKGROUND

Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience.

OBJECTIVE

We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one's self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health.

METHODS

Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1-T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator.

CONCLUSION

The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience.

E-Clinical High Risk for Psychosis: Viewpoint on Potential of Digital Innovations for Preventive Psychiatry

E-mental health is an emerging area of research that has the potential to overcome some of the current barriers to progress in working with people at clinical high risk for psychosis (CHR-P). This article provides an overview of how e-mental health could be used in the detection, prediction, and treatment in the CHR-P population. Specifically, we evaluate e-detection, e-prediction, and e-therapeutics for this clinical population. E-mental health holds great promise to improve current management of CHR-P individuals.

Beyond the "at risk mental state" concept: transitioning to transdiagnostic psychiatry

The "at risk mental state" for psychosis approach has been a catalytic, highly productive research paradigm over the last 25 years. In this paper we review that paradigm and summarize its key lessons, which include the valence of this phenotype for future psychosis outcomes, but also for comorbid, persistent or incident non-psychotic disorders; and the evidence that onset of psychotic disorder can at least be delayed in ultra high risk (UHR) patients, and that some full-threshold psychotic disorder may emerge from risk states not captured by UHR criteria. The paradigm has also illuminated risk factors and mechanisms involved in psychosis onset. However, findings from this and related paradigms indicate the need to develop new identification and diagnostic strategies. These findings include the high prevalence and impact of mental disorders in young people, the limitations of current diagnostic systems and risk identification approaches, the diffuse and unstable symptom patterns in early stages, and their pluripotent, transdiagnostic trajectories. The approach we have recently adopted has been guided by the clinical staging model and adapts the original "at risk mental state" approach to encompass a broader range of inputs and output target syndromes. This approach is supported by a number of novel modelling and prediction strategies that acknowledge and reflect the dynamic nature of psychopathology, such as dynamical systems theory, network theory, and joint modelling. Importantly, a broader transdiagnostic approach and enhancing specific prediction (profiling or increasing precision) can be achieved concurrently. A holistic strategy can be developed that applies these new prediction approaches, as well as machine learning and iterative probabilistic multimodal models, to a blend of subjective psychological data, physical disturbances (e.g., EEG measures) and biomarkers (e.g., neuroinflammation, neural network abnormalities) acquired through fine-grained sequential or longitudinal assessments. This strategy could ultimately enhance our understanding and ability to predict the onset, early course and evolution of mental ill health, further opening pathways for preventive interventions.