Mood instability is a common feature of mental health disorders and is associated with poor clinical outcomes

Study protocol: group-based psychoeducation for relatives of patients with bipolar disorder-a large scale real-world randomized controlled parallel group trial, the R-bipolar RCT

BACKGROUND

Relatives of patients with bipolar disorder (BD) often experience emotional burden with stress and depressive symptoms that again increase the likelihood of destabilization and relapses in the patient. The effects of group-based psychoeducation have not been investigated in large-scale real-world settings. We are currently conducting a large-scale real-world randomized controlled parallel group trial (RCT) to test whether group-based psychoeducation for 200 relatives to patients with BD improves mood instability and other critical outcomes in relatives and the corresponding patients with BD.

METHODS

The trial is designed as a two-arm, parallel-group randomized trial with a balanced randomization 1:1 to either group-based psychoeducation or a waiting list for approximately 4 months and subsequent group-based psychoeducation. The primary outcome measure is mood instability calculated based on daily smartphone-based mood self-assessments. Other relevant outcomes are measured, including patients' reported outcomes, assessing self-assessed burden, self-efficacy, and knowledge about BD.

DISCUSSION

This protocol describes our currently ongoing randomized controlled trial (RCT) that aims at investigating group-based psychoeducation as an intervention for relatives of individuals diagnosed with bipolar disorder (BD). The study is the first large-scale real-world RCT to focus on a relatively short intervention of psychoeducation (6 sessions of 2 h each) in a large group of relatives (approximately 30 participants per group). With this focus, we wish to test an intervention that is feasible to implement in real-life psychiatric settings with limited budgets and time. It is also the first study to use mood instability in relatives as the primary outcome measure and to investigate whether mood instability and other affective symptoms in patients and relatives covary. It could be considered as limitations, that the trial is not blinded and does not include long-term follow-up.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06176001. Registered on 2023-12-19. The study is approved by the data agency (P-2021-809). The project was allowed to be initiated without permission from the Scientific Ethical Committees for the Capital Region, because it according to section 1, paragraph 4 of the Committee Act was not defined as a health scientific intervention study (case number 21063013).

Genetics of mood instability and risk of cardiovascular diseases: A univariable and multivariable Mendelian randomization study

BACKGROUND

Cardiovascular diseases (CVDs) are significant contributors to global disability and mortality. In addition to traditional cardiovascular risk factors, emerging evidence has suggested that mental health plays a critical role as a risk factor for CVDs. The present study aimed to determine the associations between mood instability and CVDs using Mendelian randomization (MR) analysis.

METHODS

As instrumental variables, we used 62 independent single-nucleotide polymorphisms associated with mood instability at the genome-wide significance threshold in the UK Biobank. Summary-level data for seven CVDs were obtained from the publicly available genome-wide association studies. The estimates were pooled by using a random-effects inverse-variance weighted method. The results were further validated in sensitivity analysis where different MR methods were compared.

RESULTS

After correcting for multiple testing, our analysis revealed that genetic liability to mood instability was associated with increased odds of six cardiovascular diseases, including deep vein thrombosis (odds ratio (OR) 1.21; confidence interval (CI) 1.03-1.42), pulmonary embolism (OR 1.42; 95 % CI 1.09-1.85), heart failure (OR 1.20; 95 % CI 1.09-1.32), arterial hypertension (OR 1.22; 95 % CI 1.11-1.34), myocardial infarction (OR 1.25; 95 % CI 1.11-1.40), and coronary artery disease (OR 1.25; 95 % CI 1.13-1.39). Further, the genetic liability to mood instability was associated with HDL cholesterol, triglycerides, body mass index, smoking, and depression. In multivariable MR models, the association between genetic liability to mood instability and CVDs remained independent from those cardiovascular risk factors.

CONCLUSION

The present MR study suggests potential causal associations of genetic liability to mood instability with increased risk of a broad range of CVDs.

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review

OBJECTIVE

Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care.

METHODS

We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs.

RESULTS

Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP.

CONCLUSIONS

This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.

The effect of smartphone-based monitoring and treatment including clinical feedback versus smartphone-based monitoring without clinical feedback in bipolar disorder: the SmartBipolar trial-a study protocol for a randomized controlled parallel-group trial

INTRODUCTION

A substantial proportion of patients with bipolar disorder experience daily subsyndromal mood swings, and the term "mood instability" reflecting the variability in mood seems associated with poor prognostic factors, including impaired functioning, and increased risk of hospitalization and relapse. During the last decade, we have developed and tested a smartphone-based system for monitoring bipolar disorder. The present SmartBipolar randomized controlled trial (RCT) aims to investigate whether (1) daily smartphone-based outpatient monitoring and treatment including clinical feedback versus (2) daily smartphone-based monitoring without clinical feedback or (3) daily smartphone-based mood monitoring only improves mood instability and other clinically relevant patient-related outcomes in patients with bipolar disorder.

METHODS AND ANALYSIS

The SmartBipolar trial is a pragmatic randomized controlled parallel-group trial. Patients with bipolar disorder are invited to participate as part of their specialized outpatient treatment for patients with bipolar disorder in Mental Health Services in the Capital Region of Denmark. The included patients will be randomized to (1) daily smartphone-based monitoring and treatment including a clinical feedback loop (intervention group) or (2) daily smartphone-based monitoring without a clinical feedback loop (control group) or (3) daily smartphone-based mood monitoring only (control group). All patients receive specialized outpatient treatment for bipolar disorder in the Mental Health Services in the Capital Region of Denmark. The trial started in March 2021 and has currently included 150 patients. The outcomes are (1) mood instability (primary), (2) quality of life, self-rated depressive symptoms, self-rated manic symptoms, perceived stress, satisfaction with care, cumulated number and duration of psychiatric hospitalizations, and medication (secondary), and (3) smartphone-based measures per month of stress, anxiety, irritability, activity, and sleep as well as the percentage of days with presence of mixed mood, days with adherence to medication and adherence to smartphone-based self-monitoring. A total of 201 patients with bipolar disorder will be included in the SmartBipolar trial.

ETHICS AND DISSEMINATION

The SmartBipolar trial is funded by the Capital Region of Denmark and the Independent Research Fund Denmark. Ethical approval has been obtained from the Regional Ethical Committee in The Capital Region of Denmark (H-19067248) as well as data permission (journal number: P-2019-809). The results will be published in peer-reviewed academic journals, presented at scientific meetings, and disseminated to patients' organizations and media outlets.

TRIAL REGISTRATION

Trial registration number: NCT04230421. Date March 1, 2021. Version 1.

Moment-to-moment affective dynamics in schizophrenia and bipolar disorder

BACKGROUND

Affective disturbances in schizophrenia and bipolar disorder may represent a transdiagnostic etiological process as well as a target of intervention. Hypotheses on similarities and differences in various parameters of affective dynamics (intensity, successive/acute changes, variability, and reactivity to stress) between the two disorders were tested.

METHODS

Experience sampling method was used to assess dynamics of positive and negative affect, 10 times a day over 6 consecutive days. Patients with schizophrenia (n = 46) and patients with bipolar disorder (n = 46) were compared against age-matched healthy controls (n = 46).

RESULTS

Compared to controls, the schizophrenia group had significantly more intense momentary negative affect, a lower likelihood of acute changes in positive affect, and reduced within-person variability of positive affect. The bipolar disorder group was not significantly different from either the schizophrenia group or the healthy control group on any affect indexes. Within the schizophrenia group, level of depression was associated with weaker reactivity to stress for negative affect. Within the bipolar disorder group, level of depression was associated with lower positive affect.

CONCLUSIONS

Patients with schizophrenia endured a more stable and negative affective state than healthy individuals, and were less likely to be uplifted in response to happenings in daily life. There is little evidence that these affective constructs characterize the psychopathology of bipolar disorder; such investigation may have been limited by the heterogeneity within group. Our findings supported the clinical importance of assessing multiple facets of affective dynamics beyond the mean levels of intensity.

Mood instability and activity/energy instability in patients with bipolar disorder according to day-to-day smartphone-based data - An exploratory post hoc study

BACKGROUND

Alterations and instability in mood and activity/energy has been associated with impaired functioning and risk of relapse in bipolar disorder. The present study aimed to investigate whether mood instability and activity/energy instability are associated, and whether these instability measures are associated with stress, quality of life and functioning in patients with bipolar disorder.

METHODS

Data from two studies were combined for exploratory post hoc analyses. Patients with bipolar disorder provided smartphone-based evaluations of mood and activity/energy levels from day-to-day. In addition, information on functioning, perceived stress and quality of life was collected. A total of 316 patients with bipolar disorder were included.

RESULTS

A total of 55,968 observations of patient-reported smartphone-based data collected from day-to-day were available. Regardless of the affective state, there was a statistically significant positive association between mood instability and activity/energy instability in all models (all p-values < 0.0001). There was a statistically significant association between mood and activity/energy instability with patient-reported stress and quality of life (e.g., mood instability and stress: B: 0.098, 95 % CI: 0.085; 0.11, p < 0.0001), and between mood instability and functioning (B: 0.045, 95 % CI: 0.0011; 0.0080, p = 0.010).

LIMITATIONS

Findings should be interpreted with caution since the analyses were exploratory and post hoc by nature.

CONCLUSION

Mood instability and activity/energy instability is suggested to play important roles in the symptomatology of bipolar disorder. This highlight that monitoring and identifying subsyndromal inter-episodic fluctuations in symptoms is clinically recommended. Future studies investigating the effect of treatment on these measures would be interesting.

Validation of the Short Form of the Mood Instability Questionnaire-Trait (MIQ-T-SF) in the Korean General Population

OBJECTIVE

Mood instability (MI) is a clinically significant trait associated with psychiatric disorders. However, there are no concise measurements to evaluate MI. The initial Mood Instability Questionnaire-Trait (MIQ-T) was developed to fill this gap. The current study aimed to create a short form of MIQ-T (MIQ-T-SF) that measures MI with high validity and reliability in the Korean general population.

METHODS

Of the 59 items in the MIQ-T, 17 items were chosen for the MIQ-T-SF following the factor analysis process. In total, 540 participants completed the MIQ-T-SF. Cronbach's alpha and McDonald's omega were used to evaluate reliability. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to determine construct validity. Concurrent validity was confirmed via comparisons with Personality Assessment Inventory-Borderline Features Scale. Measurement invariance across gender and age groups was confirmed before analyzing differences in scores using Kruskal-Wallis test.

RESULTS

The MIQ-T-SF displayed expected correlations and high internal consistency (α=0.71-0.90, Ωt=0.72-0.92). Using EFA and CFA, a five-factor structure was confirmed. Measurement invariance was supported, and gender differences were observed.

CONCLUSION

The MIQ-T-SF is an accurate and reliable method to detect MI in the Korean general population. The study's results offer new perspectives for future studies on MI.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol

INTRODUCTION

People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data.

METHODS AND ANALYSIS

Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments.

ETHICS AND DISSEMINATION

The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.

An examination of bidirectional associations between physical activity and mood symptoms among individuals diagnosed and at risk for bipolar spectrum disorders

OBJECTIVES

Activation, a construct including energy and activity, is a central feature of Bipolar Spectrum Disorders (BSDs). Prior research found motor activity is associated with affect, and this relationship may be stronger for individuals with BSDs. The aims of this study were to investigate bidirectional relationships between physical activity and mood and evaluate whether bipolar risk status moderated potential associations.

METHODS

Young adults at low-risk, high-risk, and diagnosed with BSD participated in a 20-day EMA study in which they wore an actiwatch to measure physical activity and sleep/wake cycles. They also reported depressive and hypo/manic symptoms three times daily. Multilevel linear models were estimated to examine how bipolar risk group moderated bidirectional relationships between physical activity and mood symptoms at within-day and between-day timescales.

RESULTS

Physical activity was significantly associated with subsequent mood symptoms at the within-day level. The relationship between physical activity and depressive symptoms was moderated by BSD risk group. An increase in physical activity resulted in a greater reduction of depressive symptoms for the BSD group compared to the low-risk and high-risk groups.

CONCLUSIONS

Interventions targeting activity like behavioral activation may improve residual inter-episode mood symptoms.

Risks and benefits of psilocybin use in people with bipolar disorder: An international web-based survey on experiences of 'magic mushroom' consumption

BACKGROUND

Psilocybin, the primary psychoactive component of psychedelic 'magic mushrooms', may have potential for treating depressive symptoms, and consequent applications for bipolar disorder (BD). Knowledge of the risks and benefits of psilocybin in BD is limited to case studies.

AIM

To support the design of clinical trials, we surveyed experiences of psilocybin use in people with BD.

METHODS

An international web-based survey was used to explore experiences of psilocybin use in people with a self-reported diagnosis of BD. Quantitative findings were summarised using descriptive statistics. Qualitative content analysis was used to investigate free-text responses, with a focus on positive experiences of psilocybin use.

RESULTS

A total of 541 people completed the survey (46.4% female, mean 34.1 years old). One-third (32.2%; n = 174) of respondents described new/increasing symptoms after psilocybin trips, prominently manic symptoms, difficulties sleeping and anxiety. No differences in rates of adverse events overall were observed between individuals with BD I compared to BD II. Use of emergency medical services was rare (n = 18; 3.3%), and respondents (even those who experienced adverse effects) indicated that psilocybin use was more helpful than harmful. Quantitative findings elaborated on perceived benefits, as well as the potential for psilocybin trips to contain both positively and negatively received elements.

CONCLUSIONS

The subjective benefits of psilocybin use for mental health symptoms reported by survey participants encourage further investigation of psilocybin-based treatments for BD. Clinical trials should incorporate careful monitoring of symptoms, as data suggest that BD symptoms may emerge or intensify following psilocybin use.

Comparing the effectiveness of imagery focussed cognitive therapy to group psychoeducation for patients with bipolar disorder: A randomised trial

BACKGROUND

Bipolar disorder is a severe, chronic mental disorder. Treatment options are limited, with pharmacological approaches continuing to dominate. However, relapse rates remain high. Several adjunctive psychosocial interventions, mostly psychoeducation (PE) and cognitive behavioural therapy (CBT), have been trialled, but treatment innovation is still needed. In the past, brief group PE has proven as beneficial as longer individual CBT in reducing levels of depression and increasing self-management strategies. We compared the relative effectiveness of group PE to an imagery focussed cognitive behavioural therapy (ImCT).

STUDY DESIGN

This was a randomised parallel group study with both daily and weekly measures. A total of 62 adult patients were randomly allocated to either ImCT or group PE. Daily, weekly and pre-and post-intervention measures were used to assess impact on (i) mood instability, (ii) overall levels of depression, anxiety and mania, and (iii) general functioning, hopelessness and imagery characteristics. A four-week baseline and 16-week follow-up period were included.

RESULTS

Mood instability reduced in both conditions after intervention. Levels of mania, depression and anxiety also reduced in both conditions, but on the daily measures, depression and anxiety significantly more so in the ImCT condition. Compared with the PE condition, the ImCT condition additionally showed increased level of functioning, reduced hopelessness, and a decrease in intrusive, problematic imagery.

LIMITATIONS

These findings need to be replicated in a larger trial.

CONCLUSIONS

Findings suggest that ImCT is a promising new avenue for management of bipolar disorder, an area in which treatment development is urgently needed.

Mood Instability in Youth at High Risk for Bipolar Disorder

OBJECTIVE

Mood instability is associated with the onset of bipolar disorder (BD) in youth with a family history of the illness. In a clinical trial with youth at high risk for BD, we examined the association between mood instability and symptomatic, psychosocial, and familial functioning over an average of 2 years.

METHOD

Youth (aged 9-17 years) with major depressive disorder or other specified BD, current mood symptoms, and a family history of BD were rated by parents on a mood instability scale. Participants were randomly assigned to 4 months of family-focused therapy or enhanced care psychoeducation, both with medication management as needed. Independent evaluators rated youth every 4-6 months for up to 4 years on symptom severity and psychosocial functioning, whereas parents rated mood instability of the youth and levels of family conflict.

RESULTS

High-risk youth (N = 114; mean age 13.3 ± 2.6 years; 72 female) were followed for an average of 104.3 ± 65.8 weeks (range, 0-255 weeks) after randomization. Youth with other specified BD (vs major depressive disorder), younger age, earlier symptom onset, more severe mood symptoms, lower psychosocial functioning, and more familial conflict over time had higher mood instability ratings throughout the study period. Mood instability mediated the association between baseline diagnosis and mother/offspring conflict at follow-up (Z = 2.88, p = .004, αβ = 0.19, 95% CI = 0.06-0.32). Psychosocial interventions did not moderate these associations.

CONCLUSION

A questionnaire measure of mood instability tracked closely with symptomatic, psychosocial, and family functioning in youth at high risk for BD. Interventions that are successful in reducing mood instability may enhance long-term outcomes among high-risk youth.

CLINICAL TRIAL REGISTRATION INFORMATION

Early Intervention for Youth at Risk for Bipolar Disorder; https://clinicaltrials.gov/; NCT01483391.

The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis

Recent genome-wide association studies of mood instability (MOOD) have found significant positive genetic correlation with major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. GWAS summary statistics for schizophrenia (SCZ, n = 105,318), bipolar disorder (BIP, n = 413,466), DEP (n = 450,619), attention-deficit hyperactivity disorder (ADHD, n = 53,293), and MOOD (n = 363,705) were analyzed using the bivariate causal mixture model and conjunctional false discovery rate methods. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (r_g  = 0.10-0.62). Of 10.4 K genomic variants influencing MOOD, 4 K-9.4 K influenced psychiatric disorders. Furthermore, MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25. Fifty-three jointly associated loci were overlapping across two or more disorders, seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, "synapse organization." The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions may relate to differences in the core clinical features of each disorder.

Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force

BACKGROUND

The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established.

OBJECTIVES

To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators.

METHODS

We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668).

RESULTS

The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators.

CONCLUSION

We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

Associations of presenting symptoms and subsequent adverse clinical outcomes in people with unipolar depression: a prospective natural language processing (NLP), transdiagnostic, network analysis of electronic health record (EHR) data

OBJECTIVE

To investigate the associations of symptoms of mania and depression with clinical outcomes in people with unipolar depression.

DESIGN

A natural language processing electronic health record study. We used network analysis to determine symptom network structure and multivariable Cox regression to investigate associations with clinical outcomes.

SETTING

The South London and Maudsley Clinical Record Interactive Search database.

PARTICIPANTS

All patients presenting with unipolar depression between 1 April 2006 and 31 March 2018.

EXPOSURE

(1) Symptoms of mania: Elation; Grandiosity; Flight of ideas; Irritability; Pressured speech. (2) Symptoms of depression: Disturbed mood; Anhedonia; Guilt; Hopelessness; Helplessness; Worthlessness; Tearfulness; Low energy; Reduced appetite; Weight loss. (3) Symptoms of mania or depression (overlapping symptoms): Poor concentration; Insomnia; Disturbed sleep; Agitation; Mood instability.

MAIN OUTCOMES

(1) Bipolar or psychotic disorder diagnosis. (2) Psychiatric hospital admission.

RESULTS

Out of 19 707 patients, at least 1 depression, overlapping or mania symptom was present in 18 998 (96.4%), 15 954 (81.0%) and 4671 (23.7%) patients, respectively. 2772 (14.1%) patients subsequently developed bipolar or psychotic disorder during the follow-up period. The presence of at least one mania (HR 2.00, 95% CI 1.85 to 2.16), overlapping symptom (HR 1.71, 95% CI 1.52 to 1.92) or symptom of depression (HR 1.31, 95% CI 1.07 to 1.61) were associated with significantly increased risk of onset of a bipolar or psychotic disorder. Mania (HR 1.95, 95% CI 1.77 to 2.15) and overlapping symptoms (HR 1.76, 95% CI 1.52 to 2.04) were associated with greater risk for psychiatric hospital admission than symptoms of depression (HR 1.41, 95% CI 1.06 to 1.88).

CONCLUSIONS

The presence of mania or overlapping symptoms in people with unipolar depression is associated with worse clinical outcomes. Symptom-based approaches to defining clinical phenotype may facilitate a more personalised treatment approach and better predict subsequent clinical outcomes than psychiatric diagnosis alone.

Natural Language Processing Methods and Bipolar Disorder: Scoping Review

BACKGROUND

Health researchers are increasingly using natural language processing (NLP) to study various mental health conditions using both social media and electronic health records (EHRs). There is currently no published synthesis that relates specifically to the use of NLP methods for bipolar disorder, and this scoping review was conducted to synthesize valuable insights that have been presented in the literature.

OBJECTIVE

This scoping review explored how NLP methods have been used in research to better understand bipolar disorder and identify opportunities for further use of these methods.

METHODS

A systematic, computerized search of index and free-text terms related to bipolar disorder and NLP was conducted using 5 databases and 1 anthology: MEDLINE, PsycINFO, Academic Search Ultimate, Scopus, Web of Science Core Collection, and the ACL Anthology.

RESULTS

Of 507 identified studies, a total of 35 (6.9%) studies met the inclusion criteria. A narrative synthesis was used to describe the data, and the studies were grouped into four objectives: prediction and classification (n=25), characterization of the language of bipolar disorder (n=13), use of EHRs to measure health outcomes (n=3), and use of EHRs for phenotyping (n=2). Ethical considerations were reported in 60% (21/35) of the studies.

CONCLUSIONS

The current literature demonstrates how language analysis can be used to assist in and improve the provision of care for people living with bipolar disorder. Individuals with bipolar disorder and the medical community could benefit from research that uses NLP to investigate risk-taking, web-based services, social and occupational functioning, and the representation of gender in bipolar disorder populations on the web. Future research that implements NLP methods to study bipolar disorder should be governed by ethical principles, and any decisions regarding the collection and sharing of data sets should ultimately be made on a case-by-case basis, considering the risk to the data participants and whether their privacy can be ensured.

NeuroBlu, an electronic health record (EHR) trusted research environment (TRE) to support mental healthcare analytics with real-world data

PURPOSE

NeuroBlu is a real-world data (RWD) repository that contains deidentified electronic health record (EHR) data from US mental healthcare providers operating the MindLinc EHR system. NeuroBlu enables users to perform statistical analysis through a secure web-based interface. Structured data are available for sociodemographic characteristics, mental health service contacts, hospital admissions, International Classification of Diseases ICD-9/ICD-10 diagnosis, prescribed medications, family history of mental disorders, Clinical Global Impression-Severity and Improvement (CGI-S/CGI-I) and Global Assessment of Functioning (GAF). To further enhance the data set, natural language processing (NLP) tools have been applied to obtain mental state examination (MSE) and social/environmental data. This paper describes the development and implementation of NeuroBlu, the procedures to safeguard data integrity and security and how the data set supports the generation of real-world evidence (RWE) in mental health.

PARTICIPANTS

As of 31 July 2021, 562 940 individuals (48.9% men) were present in the data set with a mean age of 33.4 years (SD: 18.4 years). The most frequently recorded diagnoses were substance use disorders (1 52 790 patients), major depressive disorder (1 29 120 patients) and anxiety disorders (1 03 923 patients). The median duration of follow-up was 7 months (IQR: 1.3 to 24.4 months).

FINDINGS TO DATE

The data set has supported epidemiological studies demonstrating increased risk of psychiatric hospitalisation and reduced antidepressant treatment effectiveness among people with comorbid substance use disorders. It has also been used to develop data visualisation tools to support clinical decision-making, evaluate comparative effectiveness of medications, derive models to predict treatment response and develop NLP applications to obtain clinical information from unstructured EHR data.

FUTURE PLANS

The NeuroBlu data set will be further analysed to better understand factors related to poor clinical outcome, treatment responsiveness and the development of predictive analytic tools that may be incorporated into the source EHR system to support real-time clinical decision-making in the delivery of mental healthcare services.

The Daily Rhythmic Changes of Undergraduate Students' Emotions: An Analysis Based on Tencent Tweets

Emotional stability is of great importance for undergraduates and has significant predictive power for mental health. Emotions are associated with individuals' daily lives and routines. Undergraduates commonly post their opinions and feelings on social networks, providing a huge amount of data for studying their emotional states and rhythms. Based on the construction of the emotion dictionary of undergraduates' Tencent tweets (TTs)-a social network for users to share their life situations and express emotions and feelings to friends-we used big data text analysis technology to analyze the emotion words in 45,996 Tencent tweets published by 894 undergraduates. Then, we used hierarchical linear modeling to further analyze the daily rhythms of undergraduate students' emotions and how demographic variables are associated with the daily rhythmic changes. The results were as follows: (1) Undergraduates tweeted about more positive emotions than negative emotions (love was most common and fear was the least common); (2) The emotions in undergraduates' tweets changed considerably from 1 a.m. to 6 a.m., but were fairly stable during the day; (3) There was a rising trend in the frequency of using emotion words in Tencent tweets during the day as each hour progressed, and there was a higher increase in positive emotion than negative emotion; and (4) The word frequencies and daily rhythms of emotions varied depending on demographic variables. Gender was correlated with the frequencies of gratitude and the daily rhythms of anger. As the grade increased, the frequency of emotion words in most subcategories in TTs decreased and the fluctuation in daily rhythms became smaller. There was no significant difference in the frequency and daily rhythm of emotion words used in TTs based on having had a left-behind experience. The results of the present study provided emotion expression in social networks in Chinese collectivist culture. This study added new evidence to support the notion that positive and negative emotions are independent dimensions.

Patient experience of lasting negative effects of psychological interventions for anxiety and depression in secondary mental health care services: a national cross-sectional study

BACKGROUND

Patients who undergo psychological treatment can report both negative and positive effects, but evidence of factors influencing the likelihood of negative effects is limited.

AIMS

To identify aspects of the organisation and delivery of secondary care psychological treatment services that are associated with patient experiences of negative effects.

METHOD

Cross-sectional survey of people with anxiety and depression who ended psychological treatment delivered by 50 NHS trusts in England. Respondents were asked about how their treatment was organised and delivered and whether they experienced lasting negative effects.

RESULTS

Of 662 respondents, 90 (14.1%) reported experiencing lasting negative effects. People over the age of 65 were less likely than younger respondents to report negative effects. There was an association between reporting neutral or negative effects and not being referred at what respondents considered to be the right time (OR = 1.712, 95% CI = 1.078-2.726), not receiving the right number of sessions (OR = 3.105, 95% CI = 1.934-4.987), and not discussing progress with their therapist (OR 2.063, 95% CI = 1.290-3.301).

CONCLUSIONS

One in seven patients who took part in this survey reported lasting negative effects from psychological treatment. Steps should be taken to prepare people for the potential for negative experiences of treatment, and progress reviewed during therapy in an effort to identify and prevent negative effects.

Characterization of affective lability across subgroups of psychosis spectrum disorders

BACKGROUND

Affective lability is elevated and associated with increased clinical burden in psychosis spectrum disorders. The extent to which the level, structure and dispersion of affective lability varies between the specific disorders included in the psychosis spectrum is however unclear. To have potential value as a treatment target, further characterization of affective lability in these populations is necessary. The main aim of our study was to investigate differences in the architecture of affective lability in different psychosis spectrum disorders, and if putative differences remained when we controlled for current symptom status.

METHODS

Affective lability was measured with The Affective Lability Scale Short Form (ALS-SF) in participants with schizophrenia (SZ, n = 76), bipolar I disorder (BD-I, n = 105), bipolar II disorder (BD-II, n = 68) and a mixed psychosis-affective group (MP, n = 48). Multiple analyses of covariance were conducted to compare the ALS-SF total and subdimension scores of the diagnostic groups, correcting for current psychotic, affective and anxiety symptoms, substance use and sex. Double generalized linear models were performed to compare the dispersion of affective lability in the different groups.

RESULTS

Overall group differences in affective lability remained significant after adjusting for covariates (p = .001). BD-II had higher affective lability compared to SZ and BD-I (p = .004), with no significant differences between SZ and BD-I. There were no significant differences in the contributions of ALS-SF dimensions to the total affective lability or in dispersion of affective lability between the groups.

CONCLUSIONS

This study provides the construct of affective lability in psychosis spectrum disorders with more granular details that may have implications for research and clinical care. It demonstrates that despite overlap in core symptom profiles, BD-I is more similar to SZ than it is to BD-II concerning affective lability and the BD groups should consequently be studied apart. Further, affective lability appears to be characterized by fluctuations between depressive- and other affective states across different psychosis spectrum disorders, indicating that affective lability may be related to internalizing problems in these disorders. Finally, although the level varies between groups, affective lability is evenly spread and not driven by extremes across psychosis spectrum disorders and should be assessed irrespective of diagnosis.

Are we still in the dark? A systematic review on personal daily light exposure, sleep-wake rhythm, and mood in healthy adults from the general population

Insufficient light exposure is assumed to be related to a wide array of health problems, though few studies focus on the role of whole-day light exposure in the habitual setting in the development of these health problems. The current review aims to describe the association between personal light exposure in the habitual setting and sleep-wake rhythm and mood in healthy adults from the general population. Five databases (Embase, Medline Epub, Web of Science, PsycINFO, and Google Scholar) were searched in June 2019. The inclusion criteria included: assessment directly of light exposure on the participants for at least one full day; reporting on both individual personal light exposure and outcomes. The quality of the papers was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies of the National Heart, Lung and Blood Institute. The current review followed the PRISMA guidelines. In total, 8140 papers were identified in the database search. Twenty-five papers were eventually included in this review. All included studies were cross-sectional, and individual light exposure was usually measured with a wrist-worn device. Five studies received a "good" quality rating, 16 received a "fair" rating, and the remaining 4 a "poor" quality rating. The overall quality of the included studies was considered low because of the lack of intervention studies and the fact that light exposure was measured on the wrist. Given the low quality of the included studies, the current review can only provide a first exploration on the association between light exposure and sleep-wake rhythm and mood in healthy adults from the general population. Limited evidence is presented for a positive relationship between the amount and timing of light exposure on the one hand and rest-activity rhythm and some estimates of sleep architecture on the other. The evidence on an association between light exposure and circadian phase, sleep estimates, sleep quality, and mood is conflicting. Data from intervention studies are needed to gain insight into the causal mechanism of the relationship between light exposure and sleep-wake rhythm and mood.

Cognitive Impairments in Schizophrenia: A Study in a Large Clinical Sample Using Natural Language Processing

Background: Cognitive impairments are a neglected aspect of schizophrenia despite being a major factor of poor functional outcome. They are usually measured using various rating scales, however, these necessitate trained practitioners and are rarely routinely applied in clinical settings. Recent advances in natural language processing techniques allow us to extract such information from unstructured portions of text at a large scale and in a cost effective manner. We aimed to identify cognitive problems in the clinical records of a large sample of patients with schizophrenia, and assess their association with clinical outcomes. Methods: We developed a natural language processing based application identifying cognitive dysfunctions from the free text of medical records, and assessed its performance against a rating scale widely used in the United Kingdom, the cognitive component of the Health of the Nation Outcome Scales (HoNOS). Furthermore, we analyzed cognitive trajectories over the course of patient treatment, and evaluated their relationship with various socio-demographic factors and clinical outcomes. Results: We found a high prevalence of cognitive impairments in patients with schizophrenia, and a strong correlation with several socio-demographic factors (gender, education, ethnicity, marital status, and employment) as well as adverse clinical outcomes. Results obtained from the free text were broadly in line with those obtained using the HoNOS subscale, and shed light on additional associations, notably related to attention and social impairments for patients with higher education. Conclusions: Our findings demonstrate that cognitive problems are common in patients with schizophrenia, can be reliably extracted from clinical records using natural language processing, and are associated with adverse clinical outcomes. Harvesting the free text from medical records provides a larger coverage in contrast to neurocognitive batteries or rating scales, and access to additional socio-demographic and clinical variables. Text mining tools can therefore facilitate large scale patient screening and early symptoms detection, and ultimately help inform clinical decisions.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.

OBJECTIVE

The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.

METHODS

This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.

RESULTS

A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.

CONCLUSIONS

Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

A qualitative study on experiences of persons with schizophrenia in oral-health-related quality of life

Our study aimed to explore the views and experiences in oral health and oral-health-related quality of life (OHRQoL) of persons with schizophrenia (PWS) in order to expand the understanding of the factors that either limit or facilitate their healthcare pathway, which can ultimately optimize their oral health and/or OHRQoL. A qualitative study was conducted in France in the Côte d'Or department (530 000 in habitants) centered on PWS's perceived meanings regarding oral health or OHRQoL, and semi-structured individual interviews were used. A conventional content analysis approach was chosen in order to highlight unrevealed themes. A sample of 20 PWS (12 males; 8 females) with a median age was 45.8 (± 9.5) were recruited to assess views and experiences regarding OHRQoL, which were focused on three dimensions: an individual dimension related to experience of "oral symptoms", a second dimension related to experience of "stress and its management", and a third related to "Autonomy dimension in oral health". We showed that PWS clearly expressed their mental representations of oral health and OHRQoL. This study supports that PWS were able to define their needs and had the ability to discuss their oral health and OHRQoL. These finding could be used to support specific interventions for this population to better manage the negative impact of antipsychotics and help them to consult dentists on a regular basis.

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing

Background

Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael’s Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment.

Methods

We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael’s Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F₁ score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors.

Results

SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F₁ metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4–21.2%) were diagnosed with active TB and 46% (95% CI: 43.8–47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8–19.7%) and 40% (95% CI: 37.8–41.6%) respectively

Conclusion

SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.

Associations between the cortisol awakening response and patient-evaluated stress and mood instability in patients with bipolar disorder: an exploratory study

OBJECTIVE

The Cortisol Awakening Response (CAR) measured as the transient increase in cortisol levels following morning awakening appears to be a distinct feature of the HPA axis. Patients with bipolar disorder (BD) experience daily stress, mood instability (MI) and studies have shown disrupted HPA-axis dynamics.

AIMS

to evaluate (1) patient-evaluated stress against the CAR, (2) associations between the CAR and mood symptoms, and (3) the effect of smartphone-based treatment on the CAR.

METHODS

Patients with BD (n = 67) were randomized to the use of daily smartphone-based monitoring (the intervention group) or to the control group for six months. Clinically rated symptoms according to the Hamilton Depression Rating Scale 17-items (HDRS), the Young Mania Rating Scale (YMRS), patient-evaluated perceived stress using Cohen's Perceived Stress Scale (PSS) and salivary awakening cortisol samples used for measuring the CAR were collected at baseline, after three and six months. In the intervention group, smartphone-based data on stress and MI were rated daily during the entire study period.

RESULTS

Smartphone-based patient-evaluated stress (B: 134.14, 95% CI: 1.35; 266.92, p = 0.048) and MI (B: 430.23, 95% CI: 52.41; 808.04, p = 0.026) mapped onto increased CAR. No statistically significant associations between the CAR and patient-evaluated PSS or the HDRS and the YMRS, respectively were found. There was no statistically significant effect of smartphone-based treatment on the CAR.

CONCLUSION

Our data, of preliminary character, found smartphone-based patient-evaluations of stress and mood instability as read outs that reflect CAR dynamics. Smartphone-supported clinical care did not in itself appear to disturb CAR dynamics.

The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature

Background: The analysis of clinical free text from patient records for research has potential to contribute to the medical evidence base but access to clinical free text is frequently denied by data custodians who perceive that the privacy risks of data-sharing are too high. Engagement activities with patients and regulators, where views on the sharing of clinical free text data for research have been discussed, have identified that stakeholders would like to understand the potential clinical benefits that could be achieved if access to free text for clinical research were improved. We aimed to systematically review all UK research studies which used clinical free text and report direct or potential benefits to patients, synthesizing possible benefits into an easy to communicate taxonomy for public engagement and policy discussions. Methods: We conducted a systematic search for articles which reported primary research using clinical free text, drawn from UK health record databases, which reported a benefit or potential benefit for patients, actionable in a clinical environment or health service, and not solely methods development or data quality improvement. We screened eligible papers and thematically analyzed information about clinical benefits reported in the paper to create a taxonomy of benefits. Results: We identified 43 papers and derived five themes of benefits: health-care quality or services improvement, observational risk factor-outcome research, drug prescribing safety, case-finding for clinical trials, and development of clinical decision support. Five papers compared study quality with and without free text and found an improvement of accuracy when free text was included in analytical models. Conclusions: Findings will help stakeholders weigh the potential benefits of free text research against perceived risks to patient privacy. The taxonomy can be used to aid public and policy discussions, and identified studies could form a public-facing repository which will help the health-care text analysis research community better communicate the impact of their work.

Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder

Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data.

Mood, activity, and sleep measured via daily smartphone-based self-monitoring in young patients with newly diagnosed bipolar disorder, their unaffected relatives and healthy control individuals

Diagnostic evaluations and early interventions of patients with bipolar disorder (BD) rely on clinical evaluations. Smartphones have been proposed to facilitate continuous and fine-grained self-monitoring of symptoms. The present study aimed to (1) validate daily smartphone-based self-monitored mood, activity, and sleep, against validated questionnaires and clinical ratings in young patients with newly diagnosed BD, unaffected relatives (UR), and healthy controls persons (HC); (2) investigate differences in daily smartphone-based self-monitored mood, activity, and sleep in young patients with newly diagnosed BD, UR, and HC; (3) investigate associations between self-monitored mood and self-monitored activity and sleep, respectively, in young patients with newly diagnosed BD. 105 young patients with newly diagnosed BD, 24 UR and 77 HC self-monitored 2 to 1077 days (median [IQR] = 65 [17.5-112.5]). There was a statistically significantly negative association between the mood item on Hamilton Depression Rating Scale (HAMD) and smartphone-based self-monitored mood (B = - 0.76, 95% CI - 0.91; - 0.63, p < 0.001) and between psychomotor item on HAMD and self-monitored activity (B = - 0.44, 95% CI - 0.63; - 0.25, p < 0.001). Smartphone-based self-monitored mood differed between young patients with newly diagnosed BD and HC (p < 0.001), and between UR and HC (p = 0.008) and was positively associated with smartphone-based self-reported activity (p < 0.001) and sleep duration (p < 0.001). The findings support the potential of smartphone-based self-monitoring of mood and activity as part of a biomarker for young patients with BD and UR. Smartphone-based self-monitored mood is better to discriminate between young patients with newly diagnosed BD and HC, and between UR and HC, compared with smartphone-based activity and sleep.Trial registration clinicaltrials.gov NCT0288826.

An emotional-response model of bipolar disorders integrating recent findings on amygdala circuits

Because of our classification system limitations for defining psychiatric disorders and understanding their physiopathology, a new research area based on dimensions has emerged. It consists of exploring domains derived from fundamental behavioral components linked to neurobiological systems. Emotional processing is among the most affected dimensions in bipolar disorders (BD), but is excluded from the definition criteria. The purpose of this review is to synthesize the emotional responses disruption during the different phases of BD, using intensity and valence as the two key characteristics of emotions. We integrate those emotional disruptions into an original, emotion-based model contrasting with the current diagnostic frame built on mood. Emotional processing is underpinned by cortico-limbic circuits involving the amygdala. Recent publications showed the crucial role of the amygdala in emotional processes triggered by stimuli of negative, but also positive valence. We show how these neuroscience data can provide physiological basis for emotional disturbances observed in BD. We conclude with translational perspectives to improve the current knowledge about neural substrates underlying altered emotional responses characterizing BD.

Toward the Development of Data Governance Standards for Using Clinical Free-Text Data in Health Research: Position Paper

BACKGROUND

Clinical free-text data (eg, outpatient letters or nursing notes) represent a vast, untapped source of rich information that, if more accessible for research, would clarify and supplement information coded in structured data fields. Data usually need to be deidentified or anonymized before they can be reused for research, but there is a lack of established guidelines to govern effective deidentification and use of free-text information and avoid damaging data utility as a by-product.

OBJECTIVE

This study aimed to develop recommendations for the creation of data governance standards to integrate with existing frameworks for personal data use, to enable free-text data to be used safely for research for patient and public benefit.

METHODS

We outlined data protection legislation and regulations relating to the United Kingdom for context and conducted a rapid literature review and UK-based case studies to explore data governance models used in working with free-text data. We also engaged with stakeholders, including text-mining researchers and the general public, to explore perceived barriers and solutions in working with clinical free-text.

RESULTS

We proposed a set of recommendations, including the need for authoritative guidance on data governance for the reuse of free-text data, to ensure public transparency in data flows and uses, to treat deidentified free-text data as potentially identifiable with use limited to accredited data safe havens, and to commit to a culture of continuous improvement to understand the relationships between the efficacy of deidentification and reidentification risks, so this can be communicated to all stakeholders.

CONCLUSIONS

By drawing together the findings of a combination of activities, we present a position paper to contribute to the development of data governance standards for the reuse of clinical free-text data for secondary purposes. While working in accordance with existing data governance frameworks, there is a need for further work to take forward the recommendations we have proposed, with commitment and investment, to assure and expand the safe reuse of clinical free-text data for public benefit.

Mood instability in patients with newly diagnosed bipolar disorder, unaffected relatives, and healthy control individuals measured daily using smartphones

OBJECTIVES

To investigate whether mood instability (MI) qualify as a trait marker for bipolar disorder (BD) we investigated: 1) differences in smartphone-based self-reported MI between three groups: patients with newly diagnosed BD, unaffected first-degree relatives (UR), and healthy control individuals (HC); 2) the correlation between MI and functioning, stress, and duration of illness, respectively; and 3) the validity of smartphone-based self-evaluated mood ratings as compared to observer-based ratings of depressed and manic mood.

METHODS

203 patients with newly diagnosed BD, 54 UR and 109 HC were included as part of the longitudinal Bipolar Illness Onset study. Participants completed daily smartphone-based mood ratings for a period of up to two years and were clinically assessed with ratings of depression, mania and functioning.

RESULTS

Mood instability scores were statistically significantly higher in patients with BD compared with HC (mean=1.18, 95%CI: 1.12;1.24 vs 1.05, 95%CI: 0.98;1.13, p = 0.007) and did not differ between patients with BD and UR (mean=1.17, 95%CI: 1.07;1.28, p = 0.91). For patients, increased MI scores correlated positively with impaired functioning (p<0.001), increased stress level (p<0.001) and increasing number of prior mood episodes (p<0.001). Smartphone-based mood ratings correlated with ratings of mood according to sub-item 1 on the Hamilton Depression Rating Scale 17-items and the Young Mania Rating Scale, respectively (p´s<0.001).

LIMITATION

The study had a smaller number of UR than planned.

CONCLUSION

Mood instability is increased in patients with newly diagnosed BD and unaffected relatives and associated with decreased functioning. The findings highlight MI as a potential trait marker for BD.

Affective lability across psychosis spectrum disorders

Background.

Despite apparent clinical remission, individuals with psychotic disorders often experience significant impairments across functional domains. Thus, there is a need to search beyond management of core symptoms to optimize treatment outcomes. Affective dysregulation is considered a risk factor for poor clinical and functional outcomes in many mental disorders, but research investigating such features in psychosis, particularly in schizophrenia, is limited. We aimed to investigate the level of affective lability (AL) in participants with schizophrenia- and bipolar spectrum disorders (n = 222) compared to healthy controls (n = 140), as well as clinical correlates of AL in the diagnostic groups.

Methods.

The Affective Lability Scale (ALS-SF) was used to measure total score of AL and subscores covering the domains of anxiety/depression, depression/elation, and anger. An analysis of covariance was performed to compare the ALS-SF total score between groups, correcting for potential confounders, as well as standard multiple regression analyses for diagnosis-specific investigations of the relationship between AL and demographic and clinical features.

Results.

Both the schizophrenia- and bipolar spectrum group had significantly higher ALS-SF total score compared to controls (p < 0.001), and no significant differences between the patient groups were found. In the schizophrenia group, current psychotic and depressive symptoms were significantly and independently associated with AL (p = 0.012 and p = 0.024, respectively).

Conclusions.

The findings indicate that AL is elevated in psychotic disorders and that it transcends diagnostic boundaries. Further research into the causal relationship between psychotic and affective symptoms and AL, as well as its role as a potential therapeutic target in psychosis spectrum disorders, is warranted.

A genome-wide multiphenotypic association analysis identified common candidate genes for subjective well-being, depressive symptoms and neuroticism

Subjective well-being (SWB), depressive symptoms, and neuroticism are common and vital traits of mental disorders. Genetic mechanisms of SWB, depressive symptoms and neuroticism remain elusive now. The large-scale GWAS summary datasets of SWB (n = 229,883), depressive symptoms (n = 180,866), and neuroticism (n = 170,911) were obtained from published studies. MASH tool was applied to the GWAS datasets for identifying candidate SNPs shared by SWB, depressive symptoms and neuroticism. SNPs detected by MASH, were then mapped to target genes considering regulatory SNP (rSNP), methylated quantitative trait locus (MeQTL) and the SNPs near to known genes. Gene set enrichment analysis (GSEA) was conducted by the FUMA platform. A total of 122 candidate SNPs were detected by MASH analysis, mapping to 29 target genes, such as CLDN23, MSRA and XKR6. GO enrichment analysis identified multiple immune related gene sets for SWB, depressive symptoms and neuroticism, such as GSE2770_UNTREATED_VS_IL4_TREATED_ACT_CD4_TCELL_48H_DN (P = 7.32 × 10^-3), GSE6259_FLT3L_INDUCED_DEC205_POS_DC_VS_CD4_TCELL_DN (P = 2.52 × 10^-2). We also found some mental disorders related gene sets were associated with three phenotypes, such as mood instability (P = 1.15 × 10^-6) and neuroticism (P = 1.72 × 10^-6). We identified multiple candidate genes and GO terms shared by SWB, depressive symptoms and neuroticism. Our results support the overlapping genetic mechanisms, and suggest a functional correlation between immunity and SWB, depressive symptoms and neuroticism.

Prevalence and distribution pattern of mood swings in Thai adolescents: a school-based survey in the central region of Thailand

BACKGROUND

Mood swings (MS) are a widely discussed psychiatric ailment of youthful patients. However, there is a lack of research about MS in this population.

METHODS

A school-based, cross-sectional study was conducted to investigate the prevalence and distribution pattern of mood swings due to personal and contextual determinants in Thai adolescents in the central region of Thailand. Participants were 2598 students in high schools and vocational schools in Bangkok and three provinces in the central region of Thailand.

RESULTS

The prevalence of mood swings was 26.4%. It was highest among vocational students in Bangkok at 37.1%. MS were more common in adolescents who exhibited risk behaviors and who resided in hazardous situations. The probabilities of MS by characteristic in 15-24 years olds were: bullying involvement 36.9% (n = 1293), problematic social media use 55.9%(n = 127), high expressed emotion in family 36.6% (n = 1256), and studying in a vocational program 29.5% (n = 1216) and school located in Bangkok 32.4% (n = 561). Also, substance use was a risk for MS with cannabis use at 41.8%(n = 55) and heroin use at 48.0% (n = 25). Hierarchical logistic regression analysis showed that female gender, having a family history of mental problems, bullying involvement, problematic social media use, high expression of emotion in the family, and the interaction between vocational program enrollments and metropolitan/urban residence associated adolescent mood swings (p < .05).

CONCLUSIONS

Findings indicate that the pattern of mood swings was associated with significant bullying involvement, social media use, family circumstance, and school characteristics. The public needs greater awareness of MS patterns and the positive implications of MS screening. Early preventive interventions that may limit later mental illness are needed.

Prevalence and incidence of clinical outcomes in patients presenting to secondary mental health care with mood instability and sleep disturbance

BACKGROUND

Mood instability and sleep disturbance are common symptoms in people with mental illness. Both features are clinically important and associated with poorer illness trajectories. We compared clinical outcomes in people presenting to secondary mental health care with mood instability and/or sleep disturbance with outcomes in people without either mood instability or sleep disturbance.

METHODS

Data were from electronic health records of 31,391 patients ages 16-65 years presenting to secondary mental health services between 2008 and 2016. Mood instability and sleep disturbance were identified using natural language processing. Prevalence of mood instability and sleep disturbance were estimated at baseline. Incidence rate ratios were estimates for clinical outcomes including psychiatric diagnoses, prescribed medication, and hospitalization within 2-years of presentation in persons with mood instability and/or sleep disturbance compared to individuals without either symptom.

RESULTS

Mood instability was present in 9.58%, and sleep disturbance in 26.26% of patients within 1-month of presenting to secondary mental health services. Compared with individuals without either symptom, those with mood instability and sleep disturbance showed significantly increased incidence of prescription of any psychotropic medication (incidence rate ratios [IRR] = 7.04, 95% confidence intervals [CI] 6.53-7.59), and hospitalization (IRR = 5.32, 95% CI 5.32, 4.67-6.07) within 2-years of presentation. Incidence rates of most clinical outcomes were considerably increased among persons with both mood instability and sleep disturbance, relative to persons with only one symptom.

CONCLUSIONS

Mood instability and sleep disturbance are present in a wide range of mental disorders, beyond those in which they are conventionally considered to be symptoms. They are associated with poor outcomes, particularly when they occur together. The poor prognosis associated with mood instability and sleep disorder may be, in part, because they are often treated as secondary symptoms. Mood instability and sleep disturbance need better recognition as clinical targets for treatment in their own right.

The effect of smartphone-based monitoring on illness activity in bipolar disorder: the MONARCA II randomized controlled single-blinded trial

BACKGROUND

Recently, the MONARCA I randomized controlled trial (RCT) was the first to investigate the effect of smartphone-based monitoring in bipolar disorder (BD). Findings suggested that smartphone-based monitoring sustained depressive but reduced manic symptoms. The present RCT investigated the effect of a new smartphone-based system on the severity of depressive and manic symptoms in BD.

METHODS

Randomized controlled single-blind parallel-group trial. Patients with BD, previously treated at The Copenhagen Clinic for Affective Disorder, Denmark and currently treated at community psychiatric centres, private psychiatrists or GPs were randomized to the use of a smartphone-based system or to standard treatment for 9 months. Primary outcomes: differences in depressive and manic symptoms between the groups.

RESULTS

A total of 129 patients with BD (ICD-10) were included. Intention-to-treat analyses showed no statistically significant effect of smartphone-based monitoring on depressive (B = 0.61, 95% CI -0.77 to 2.00, p = 0.38) and manic (B = -0.25, 95% CI -1.1 to 0.59, p = 0.56) symptoms. The intervention group reported higher quality of life and lower perceived stress compared with the control group. In sub-analyses, the intervention group had higher risk of depressive episodes, but lower risk of manic episodes compared with the control group.

CONCLUSIONS

There was no effect of smartphone-based monitoring. In patient-reported outcomes, patients in the intervention group reported improved quality of life and reduced perceived stress. Patients in the intervention group had higher risk of depressive episodes and reduced risk of manic episodes. Despite the widespread use and excitement of electronic monitoring, few studies have investigated possible effects. Further studies are needed.

Actigraphic patterns, impulsivity and mood instability in bipolar disorder, borderline personality disorder and healthy controls

OBJECTIVES

To differentiate the relation between the structure and timing of rest-activity patterns and symptoms of impulsivity and mood instability in bipolar disorder (BD), borderline personality disorder (BPD) and healthy controls (HC).

METHODS

Eighty-seven participants (31 BD, 21 BPD and 35 HC) underwent actigraph monitoring for 28 days as part of the Automated Monitoring of Symptom Severity (AMoSS) study. Impulsivity was assessed at study entry using the BIS-11. Mood instability was subsequently longitudinally monitored using the digital Mood Zoom questionnaire.

RESULTS

BPD participants show several robust and significant correlations between non-parametric circadian rest-activity variables and worsened symptoms. Impulsivity was associated with low interdaily stability (r = -0.663) and weak amplitude (r = -0.616). Mood instability was associated with low interdaily stability (r = -0.773), greater rhythm fragmentation (r = 0.662), weak amplitude (r = -0.694) and later onset of daily activity (r = 0.553). These associations were not present for BD or HCs. Classification analysis using actigraphic measures determined that later L5 onset reliably distinguished BPD from BD and HC but did not sufficiently discriminate between BD and HC.

CONCLUSIONS

Rest-activity pattern disturbance indicative of perturbed sleep and circadian function is an important predictor of symptom severity in BPD. This appears to validate the greater subjective complaints of BPD individuals that are sometimes regarded as exaggerated by clinicians. We suggest that treatment strategies directed towards improving sleep and circadian entrainment may in the future be investigated in BPD.

Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

OBJECTIVE

Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information documented in EHR free-text narratives.

MATERIALS AND METHODS

Our search of 1964 records from PubMed and EMBASE was narrowed to 27 eligible articles. Data related to the purpose, free-text corpus, patients, symptoms, NLP methodology, evaluation metrics, and quality indicators were extracted for each study.

RESULTS

Symptom-related information was presented as a primary outcome in 14 studies. EHR narratives represented various inpatient and outpatient clinical specialties, with general, cardiology, and mental health occurring most frequently. Studies encompassed a wide variety of symptoms, including shortness of breath, pain, nausea, dizziness, disturbed sleep, constipation, and depressed mood. NLP approaches included previously developed NLP tools, classification methods, and manually curated rule-based processing. Only one-third (n = 9) of studies reported patient demographic characteristics.

DISCUSSION

NLP is used to extract information from EHR free-text narratives written by a variety of healthcare providers on an expansive range of symptoms across diverse clinical specialties. The current focus of this field is on the development of methods to extract symptom information and the use of symptom information for disease classification tasks rather than the examination of symptoms themselves.

CONCLUSION

Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives. Efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.

The Emerging Neurobiology of Bipolar Disorder

(Reprinted with permission from Trends in Neurosciences, January 2018, Vol. 41, No. 1 ).

Mood instability during pregnancy and postpartum: a systematic review

Perinatal mood instability (MI) is a common clinical observation in perinatal women, and existing research indicates that MI is strongly associated with a variety of mental disorders. The purpose of this study is to review the evidence of perinatal MI systematically, with a focus on perinatal MI, its relation to perinatal depression, and its effects on children. A systematic search of the literature using PRISMA guidelines was conducted on seven academic health databases to identify any peer-reviewed articles published in English from 1985 to July 2017. Studies were screened, data were extracted, and quality of the selected studies was assessed. A total of 1927 abstracts were returned from the search, with 1063 remaining for abstract screening after duplicate removal, and 4 quantitative studies were selected for final analysis. The selected studies addressed perinatal MI (n = 2), the relation of perinatal MI to perinatal depression (n = 1), and the effects of perinatal MI on children (n = 1). The selected studies identified that perinatal women experienced a significantly higher level of MI than non-perinatal women, MI is a prominent feature in perinatal women with and without depression, mood lability during the early postpartum predicts psychopathology up to 14 months postpartum, and maternal emotion dysregulation, rather than maternal psychopathology, increases the risk of heightened facial affect synchrony in mother-infant interaction. The study reveals a significant gap in the literature of perinatal MI.

Cognitive Impairment in Patients with Bipolar Disorder: Impact of Pharmacological Treatment

Bipolar disorder is an illness characterised by periods of elated and depressed mood. These mood episodes are associated with changes in cognitive function and there is evidence to suggest that cognitive dysfunction persists during euthymia. The extent to which this is a function of the illness or a result of treatment is less clear. In this narrative review, we explore the impact of commonly used medications for bipolar disorder on cognitive function. Specific impairments in executive function and verbal memory have been noted in bipolar disorder. The impact of pharmacological treatments upon cognitive function is mixed with a number of studies reporting conflicting results. Interpretation of the data is further complicated by the variety of cognitive tests employed, study design, the relatively small numbers of patients included and confounding by indication. Overall, there is some evidence that while lithium improves some cognitive domains, it impedes others. Antipsychotics may be deleterious to cognition, although this may relate to the patient population in which they are prescribed. Sodium valproate is also associated with worse cognitive outcomes, while the impact of other antiepileptics is unclear. Overall the quality of evidence is poor and is derived from a relatively small number of studies that often do not account for the significant heterogeneity of the disorder or common comorbidities. The use of consistent methodologies and measures of cognition across studies, as well as in naturalistic settings, would enable more certain conclusions to be drawn.

Is smartphone-based mood instability associated with stress, quality of life, and functioning in bipolar disorder?

OBJECTIVE

Mood instability in patients with bipolar disorder has been associated with impaired functioning and risk of relapse. The present study aimed to investigate whether increased mood instability is associated with increased perceived stress and impaired quality of life and functioning in patients with bipolar disorder.

METHODS

A total of 84 patients with bipolar disorder used a smartphone-based self-monitoring system on a daily basis for 9 months. Data on perceived stress, quality of life, and clinically rated functioning were collected at five fixed time points for each patient during follow-up. A group of 37 healthy individuals served as a control comparison of perceived stress, quality of life, and psychosocial functioning.

RESULTS

The majority of patients presented in full or partial remission. As hypothesized, mood instability was significantly associated with increased perceived stress (B: 10.52, 95% CI: 5.25; 15.77, P < 0.0001) and decreased quality of life (B: -12.17, 95% CI. -19.54; -4.79, P < 0.0001) and functioning (B: -12.04, 95% CI: -19.08; -4.99, P < 0.0001) in patients with bipolar disorder. There were no differences in mood instability according to prescribed psychopharmacological treatment. Compared with healthy individuals, patients reported substantially increased perceived stress and experienced decreased quality of life and decreased functioning based on researcher-blinded evaluation.

CONCLUSION

Mood instability in bipolar disorder is associated with increased perceived stress and decreased quality of life and functioning even during full or partial remission. There is a need to monitor and identify subsyndromal inter-episodic symptoms. Future studies investigating the effect of treatment on mood instability are highly warranted.

Episodic density, subsyndromic symptoms, and mood instability in late-life bipolar disorders: A 5-year follow-up study

OBJECTIVES

Characterization of clinical course in old age bipolar disorder (OABD) is scarce and based solely on episode density (ED). The aim of this study was to explore mood instability (MI) and subsyndromal symptomatology (SS) in a prospective cohort of OABD. Further, we contrasted these measures with a cohort of young age bipolar disorder (YABD).

METHODS

Life charts from weekly mood ratings were used to compute the number of weeks spent with subsyndromal symptoms (SD), the ED, and the MI during follow-up for a cohort of OABD (N = 38) that excluded late onset BD. Linear and logistic regression models were fitted to compare the clinical course of OABD with a cohort of YABD (N = 52) and to explore the relationship between these measures and functional outcomes.

RESULTS

Median follow-up was 5 years (IQR: 3.6-7.9). OABD (61.6 years, SD: 8.3) spent 15%, 6%, and 3% of their follow-up with depressive, manic, and mixed symptoms, respectively, and suffered 4.2 mood changes per year (SD: 2.6). No significant differences between OABD and YABD regarding ED or MI emerged in multivariate analysis, while a higher subsyndromal manic symptom burden was observed in OABD (β coefficient: 3.79, 95%CI: 0.4-7.2). Both SS and MI were associated with functional outcomes in OABD.

CONCLUSIONS

The course of illness throughout OABD was similar to the one observed in YABD except for a higher subsyndromal manic burden. This study extended the association of MI and SD with global functioning to the late-life BD.

Reporting guidelines on remotely collected electronic mood data in mood disorder (eMOOD)-recommendations

Prospective monitoring of mood was started by Kraepelin who made and recorded frequent observations of his patients. During the last decade, the number of research studies using remotely collected electronic mood data has increased markedly. However, standardized measures and methods to collect, analyze and report electronic mood data are lacking. To get better understanding of the nature, correlates and implications of mood and mood instability, and to standardize this process, we propose guidelines for reporting of electronic mood data (eMOOD). This paper provides an overview of remotely collected electronic mood data in mood disorders and discusses why standardized reporting is necessary to evaluate and inform mood research in Psychiatry. Adherence to these guidelines will improve interpretation, reproducibility and future meta-analyses of mood monitoring in mood disorder research.

The association between mixed symptoms, irritability and functioning measured using smartphones in bipolar disorder

OBJECTIVE

To (i) validate patient-evaluated mixed symptoms and irritability measured using smartphones against clinical evaluations; (ii) investigate associations between mixed symptoms and irritability with stress, quality of life and functioning, respectively, in patients with bipolar disorder.

METHODS

A total of 84 patients with bipolar disorder used a smartphone-based system for daily evaluation of mixed symptoms and irritability for nine months. Clinically evaluated symptoms, stress, quality of life and clinically rated functioning were collected multiple times during follow-up.

RESULTS

Patients presented mild affective symptoms. Patient-reported mixed symptoms and irritability correlated with clinical evaluations. In analyses including confounding factors there was a statistically significant association between both mixed symptoms and irritability and stress (P < 0.0001) and between irritability and both quality of life and functioning (P < 0.0001) respectively. There was no association between mixed mood and both quality of life and functioning.

CONCLUSION

Mixed symptoms and irritability can be validly self-reported using smartphones in patients with bipolar disorder. Mixed symptoms and irritability are associated with increased stress even during full or partial remission. Irritability is associated with decreased quality of life and functioning. The findings emphasize the clinical importance of identifying inter-episodic symptoms including irritability pointing towards smartphones as a valid tool.

Differences in mood instability in patients with bipolar disorder type I and II: a smartphone-based study

BACKGROUND

Mood instability in bipolar disorder is associated with a risk of relapse. This study investigated differences in mood instability between patients with bipolar disorder type I and type II, which previously has been sparingly investigated.

METHODS

Patients with bipolar disorder type I (n = 53) and type II (n = 31) used a daily smartphone-based self-monitoring system for 9 months. Data in the present reflect 15.975 observations of daily collected smartphone-based data on patient-evaluated mood.

RESULTS

In models adjusted for age, gender, illness duration and psychopharmacological treatment, patients with bipolar disorder type II experienced more mood instability during depression compared with patients with bipolar disorder type I (B: 0.27, 95% CI 0.007; 0.53, p = 0.044), but lower intensity of manic symptoms. Patients with bipolar disorder type II did not experience lower mean mood or higher intensity of depressive symptoms compared with patients with bipolar disorder type I.

CONCLUSIONS

Compared to bipolar disorder type I, patients with bipolar disorder type II had higher mood instability for depression. Clinically it is of importance to identify these inter-episodic symptoms. Future studies investigating the effect of treatment on mood instability measures are warranted. Trial registration NCT02221336.