Mood instability - A new outcome measure in randomised trials of bipolar disorder?

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study

BACKGROUND

Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions.

AIMS

The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder.

METHOD

We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients.

RESULTS

Recruitment is ongoing.

CONCLUSIONS

This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Beyond traditional metrics: A novel method for measuring mood instability in bipolar disorder

Background

Clinical care for bipolar disorder (BD) has a narrow focus on prevention and remission of episodes with pre/post treatment reductions in symptom severity as the 'gold standard' for outcomes in clinical trials and measurement-based care strategies. The study aim was to provide a novel method for measuring outcomes in BD that has clinical utility and can stratify individuals with BD based on mood instability.

Methods

Participants were 603 with a BD (n=385), other or non-affective disorder (n=71), or no psychiatric history (n=147) enrolled in an intensive longitudinal cohort for at least 10 years that collects patient reported outcomes measures (PROMs) assessing depression, (hypo)mania, anxiety, and functioning every two months. Mood instability was calculated as the within-person variance of PROMs and stratified into low, moderate, and high thresholds, respectively.

Outcomes

Individuals with BD had significantly higher mood instability index's for depression, (hypo)mania, and anxiety compared to psychiatric comparisons (moderate effects, p's<.001) and healthy controls (large effects, p's<.001). A significantly greater proportion of individuals with BD fell into the moderate (depression: 52·8%; anxiety: 51·4%; (hypo)mania: 48·3%) and high instability thresholds (depression: 11·5%; anxiety: 9·1%; (hypo)mania: 10·8%) compared to psychiatric comparisons (moderate: 25·5 - 26·6%; high: 0% - 4·7%) and healthy controls (moderate: 2·9% - 17·1%; high: 0% - 1·4%). Being in the high or moderate instability threshold predicted worse health functioning (p's < .00, small to large effects).

Interpretation

Mood instability, as measured in commonly used PROMs, characterized the course of illness over time, correlated with functional outcomes, and significantly differentiated those with BD from healthy controls and psychiatric comparisons. Results suggest a paradigm shift in monitoring outcomes in BD, by measuring mood instability as a primary outcome index.

BLUES - stabilizing mood and sleep with blue blocking eyewear in bipolar disorder - a randomized controlled trial study protocol

INTRODUCTION

Chronotherapeutic interventions for bipolar depression and mania are promising interventions associated with rapid response and benign side effect profiles. Filtering of biologically active short wavelength (blue) light by orange tinted eyewear has been shown to induce antimanic and sleep promoting effects in inpatient mania. We here describe a study protocol assessing acute and long-term stabilizing effects of blue blocking (BB) glasses in outpatient treatment of bipolar disorder.

PATIENTS AND METHODS

A total of 150 outpatients with bipolar disorder and current symptoms of (hypo)-mania will be randomized 1:1 to wear glasses with either high (99%) (intervention group) or low (15%) (control group) filtration of short wavelength light (<500 nm). Following a baseline assessment including ratings of manic and depressive symptoms, sleep questionnaires, pupillometric evaluation and 48-h actigraphy, participants will wear the glasses from 6 PM to 8 AM for 7 consecutive days. The primary outcome is the between group difference in change in Young Mania Rating Scale scores after 7 days of intervention (day 9). Following the initial treatment period, the long-term stabilizing effects on mood and sleep will be explored in a 3-month treatment paradigm, where the period of BB treatment is tailored to the current symptomatology using a 14-h antimanic schedule during (hypo-) manic episodes (BB glasses or dark bedroom from 6 PM to 8 AM) and a 2-h maintenance schedule (BB glasses on two hours prior to bedtime/dark bedroom) during euthymic and depressive states.The assessments will be repeated at follow-up visits after 1 and 3 months. Throughout the 3-month study period, participants will perform continuous daily self-monitoring of mood, sleep and activity in a smartphone-based app. Secondary outcomes include between-group differences in actigraphic sleep parameters on day 9 and in day-to-day instability in mood, sleep and activity, general functioning and objective sleep markers (actigraphy) at weeks 5 and 15.

TRIAL REGISTRATION

The trial will be registered at www.clinicaltrials.gov prior to initiation and has not yet received a trial reference.

ADMINISTRATIVE INFORMATION

The current paper is based on protocol version 1.0_31.07.23. Trial sponsor: Lars Vedel Kessing.

Correlation between emotion dysregulation and mood symptoms of bipolar disorder: A systematic review and meta-analysis

BACKGROUND

Emotion dysregulation (ED) is a transdiagnostic construct characterized by difficulties regulating intense emotions. People with bipolar disorder (BD) are more likely to show ED and use maladaptive emotion regulation strategies than adaptive ones. However, little is known about whether ED in BD is a trait or it is rather an epiphenomenon of mood symptoms.

METHODS

We conducted a systematic review and meta-analysis of the evidence across major literature databases reporting correlations between measures of emotion regulation (overall ED and different emotion regulation strategies) and measures of depressive and (hypo)manic symptoms in BD from inception until April 12th, 2022.

RESULTS

Fourteen studies involving 1371 individuals with BD were included in the qualitative synthesis, of which 11 reported quantitative information and were included in the meta-analysis. ED and maladaptive strategies were significantly higher during periods with more severe mood symptoms, especially depressive ones, while adaptive strategies were lower.

CONCLUSION

ED significantly correlates with BD symptomatology, and it mainly occurs during mood alterations. ED may be a target for specific psychotherapeutic and pharmacological treatments, according to precision psychiatry. However, further studies are needed, including patients with mood episodes and longitudinal design, to provide more robust evidence and explore the causal direction of the associations.

Emotion dysregulation in bipolar disorder compared to other mental illnesses: a systematic review and meta-analysis

People with bipolar disorder (BD) often present emotion dysregulation (ED), a pattern of emotional expression interfering with goal-directed behavior. ED is a transdiagnostic construct, and it is unclear whether it manifests itself similarly in other conditions, such as major depressive disorder (MDD) or borderline personality disorder (BPD), or has specific features in BD. The present systematic review and meta-analysis explored ED and adopted emotion regulation (ER) strategies in BD compared with other psychiatric conditions. PubMed/MEDLINE, EMBASE, Scopus, and PsycINFO databases were systematically searched from inception to April 28th, 2022. Studies implementing validated instruments assessing ED or ER strategies in BD and other psychiatric disorders were reviewed, and meta-analyses were conducted. Twenty-nine studies yielding multiple comparisons were included. BD was compared to MDD in 20 studies (n = 2451), to BPD in six studies (n = 1001), to attention deficit hyperactivity disorder in three studies (n = 232), to anxiety disorders in two studies (n = 320), to schizophrenia in one study (n = 223), and to post-traumatic stress disorder in one study (n = 31). BD patients did not differ from MDD patients in adopting most adaptive and maladaptive ER strategies. However, small-to-moderate differences in positive rumination and risk-taking behaviors were observed. In contrast, patients with BPD presented an overall higher degree of ED and more maladaptive ER strategies. There were insufficient data for a meta-analytic comparison with other psychiatric disorders. The present report further supports the idea that ED is a transdiagnostic construct spanning a continuum across different psychiatric disorders, outlining specific clinical features that could represent potential therapeutic targets.

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.

A systematic review of manic/hypomanic and depressive switches in patients with bipolar disorder in naturalistic settings: The role of antidepressant and antipsychotic drugs

The present systematic review was aimed at critically summarizing the evidence about treatment-emergent manic/hypomanic and depressive switches during the course of bipolar disorder (BD). A systematic search of the MEDLINE, EMBASE, CINAHL, Web of Science, and PsycInfo electronic databases was conducted until March 24th, 2021, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Observational studies clearly reporting data regarding the prevalence of treatment-emergent mood switches in patients with BD were considered for inclusion. Thirty-two original studies met the inclusion criteria. In the majority of cases, manic switches were analyzed; only 3 papers investigated depressive switches in type I BD. Treatment-emergent mania/hypomania in BD subjects ranged from 17.3% to 48.8% and was more frequent with antidepressant monotherapy compared to combination treatment with mood stabilizers, especially lithium, or second-generation antipsychotics. A higher likelihood of mood switch has been reported with tricyclics and a lower rate with bupropion. Depressive switches were detected in 5-16% of type I BD subjects and were associated with first-generation antipsychotic use, the concomitant use of first- and second-generation antipsychotics, and benzodiazepines. The included studies presented considerable methodological heterogeneity, small sample sizes and comparability flaws. In conclusion, many studies, although heterogeneous and partly discordant, have been conducted on manic/hypomanic switches, whereas depressive switches during treatment with antipsychotics are poorly investigated. In BD subjects, both antidepressant and antipsychotic medications seems to play a role in the occurrence of mood switches, although the effects of different pharmacological compounds have yet to be fully investigated.

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.

Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study

BACKGROUND

Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity alongside physiological alterations that wearables can capture.

OBJECTIVE

We explored whether physiological wearable data could predict: (aim 1) the severity of an acute affective episode at the intra-individual level, (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to the prior predictions, generalization across patients, and associations between affective symptoms and physiological data.

METHODS

We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded with a research-grade wearable (Empatica E4) across three consecutive timepoints (acute, response, and remission of episode). Euthymic patients and healthy controls (HC) were recorded during a single session (∼48 hours). Manic and depressive symptoms were assessed with standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), temperature (TEMP), blood volume pulse (BVP), heart rate (HR), and electrodermal activity (EDA). For data pre-processing, invalid physiological data were removed using a rule-based filter, channels were time-aligned at 1 second time units and then segmented window lengths of 32 seconds, since those parameters showed the best performances. We developed deep learning predictive models, assessed channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel fully automated method for analysis of physiological data from a research-grade wearable device, including a rule-based filter for invalid data and a viable supervised learning pipeline for time-series analyses.

RESULTS

35 sessions (1,512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 HC (age 39.7±12.6; 31.6% female) were analyzed. (aim 1) The severity of mood episodes was predicted with moderate (62%-85%) accuracies. (aim 2) The polarity of episodes was predicted with moderate (70%) accuracy. The most relevant features for the former tasks were ACC, EDA, and HR. Kendall W showed fair agreement (0.383) in feature importance across classification tasks. Generalization of the former models were of overall low accuracy, with better results for the intra-individual models. "Increased motor activity" was associated with ACC (NMI>0.55), "aggressive behavior" with EDA (NMI=1.0), "insomnia" with ACC (NMI∼0.6), "motor inhibition" with ACC (NMI∼0.75), and "psychic anxiety" with EDA (NMI=0.52).

CONCLUSIONS

Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression respectively. These findings represent a promising pathway towards personalized psychiatry, in which physiological wearable data could allow early identification and intervention of mood episodes.

CLINICALTRIAL

Monitoring and treatment in patients with bipolar disorder using smartphones-New perspectives for improved quality in patient care

The rapid international growth in access to and capabilities of mobile and wireless technologies (mHealth) presents a feasible route towards augmenting traditional mental health care. The interest in mHealth science in psychiatry has been further heightened by the acknowledged potential for these tools to improve individual risk prediction and diagnostic precision, as well as improved treatment options. We have conducted research within smartphone-based monitoring and treatment in patients with bipolar disorder through the last decade. We conclude that the technological capabilities of smartphones are already changing mental health care and is accompanied by an early but promising evidence base. However, further efforts towards strengthening the evidence and implementation must be addressed for digital mental health technologies to truly improve mental health research and treatment in the future.