Mood instability in bipolar disorder type I versus type II-continuous daily electronic self-monitoring of illness activity using smartphones

Mood instability in patients with unipolar depression measured using smartphones and the association with measures of wellbeing, recovery and functioning

OBJECTIVE

While mood instability is strongly linked to depression, its ramifications remain unexplored. In patients diagnosed with unipolar depression (UD), our objective was to investigate the association between mood instability, calculated based on daily smartphone-based patient-reported data on mood, and functioning, quality of life, perceived stress, empowerment, rumination, recovery, worrying and wellbeing.

METHODS

Patients with UD completed daily smartphone-based self-assessments of mood for 6 months, making it possible to calculate mood instability using the Root Mean Squared Successive Difference (rMSSD) method. A total of 59 patients with UD were included. Data were analyzed using mixed effects regression models.

RESULTS

There was a statistically significant association between increased mood instability and increased perceived stress (adjusted model: B: 0.010, 95% CI: 0.00027; 0.021, p = 0.044), and worrying (adjusted model: B: 0.0060, 95% CI: 0.000016; 0.012, p = 0.049), and decreased quality of life (adjusted model: B: -0.0056, 95% CI: -0.011; -0.00028, p = 0.039), recovery (adjusted model: B: -0.032, 95% CI: -0.0059; -0.00053, p = 0.019) and wellbeing. There were no statistically significant associations between mood instability and functioning, empowerment, and rumination (p's >0.09).

CONCLUSION

These findings underscore the significant influence of mood instability on patients' daily lives. Identification of mood fluctuations offer potential insights into the trajectory of the illness in these individuals.

Can we predict a "tsunami"? Symptomatic and syndromal density, mood instability and treatment intensity in people with bipolar disorders under a strict and long lockdown

BACKGROUND

Converging evidence supports the involvement of circadian rhythm disturbances in the course and morbidity of bipolar disorders (BD). During 2020, lockdown measures were introduced worldwide to contain the health crisis caused by the COVID-19 pandemic. As a result, chronobiological rhythms were critically disrupted and illness outcomes were expected to worsen. The current study aimed to explore changes in morbidity among BD patients living under lockdown.

METHODS

Ninety BD outpatients under naturalistic treatment conditions were followed from March to September 2020 using a mood chart technique. Different treatment and illness variables, including mood instability, were assessed and compared with the outcomes obtained during the same 28-week period in 2019.

RESULTS

For most clinical variables, no significant differences were observed between time periods. A slight decrease was found in symptom intensity (from 15.19 ± 20.62 to 10.34 ± 15.79, FDR-adjusted p = 0.04) and in the number of depressive episodes (from 0.39 ± 0.74 to 0.22 ± 0.63, FDR-adjusted p = 0.03), whereas the intensity of pharmacological treatment remained unchanged. Previous illness course predicted mood outcomes during the confinement.

LIMITATIONS

Follow-up periods were relatively short. Further, actigraphy or other methods capable of ensuring significant changes in physical activity were not used.

CONCLUSIONS

In line with other studies, our findings show no worsening in the clinical morbidity of BD patients during lockdown. This conspicuous contrast between our initial predictions and the observed findings highlights the fact that we are still far from being able to provide accurate predictive models for BD.

The dynamic interplay between sleep and mood: an intensive longitudinal study of individuals with bipolar disorder

BACKGROUND

Sleep disturbances are important symptoms to monitor in people with bipolar disorder (BD) but the precise longitudinal relationships between sleep and mood remain unclear. We aimed to examine associations between stable and dynamic aspects of sleep and mood in people with BD, and assess individual differences in the strength of these associations.

METHODS

Participants (N = 649) with BD-I (N = 400) and BD-II (N = 249) provided weekly self-reports of insomnia, depression and (hypo)mania symptoms using the True Colours online monitoring tool for 21 months. Dynamic structural equation models were used to examine the interplay between weekly reports of insomnia and mood. The effects of clinical and demographic characteristics on associations were also assessed.

RESULTS

Increased variability in insomnia symptoms was associated with increased mood variability. In the sample as a whole, we found strong evidence of bidirectional relationships between insomnia and depressive symptoms but only weak support for bidirectional relationships between insomnia and (hypo)manic symptoms. We found substantial variability between participants in the strength of prospective associations between insomnia and mood, which depended on age, gender, bipolar subtype, and a history of rapid cycling.

CONCLUSIONS

Our results highlight the importance of monitoring sleep in people with BD. However, researchers and clinicians investigating the association between sleep and mood should consider subgroup differences in this relationship. Advances in digital technology mean that intensive longitudinal data on sleep and mood are becoming increasingly available. Novel methods to analyse these data present an exciting opportunity for furthering our understanding of BD.

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.

Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders

Introduction

This study aimed to investigate the psychometric properties of the Affective Lability Scale-short form (ALS-SF) among Chinese patients with mood disorders, and to compare ALS-SF subscale scores between patients with major depressive disorder (MDD) and patients with bipolar disorder (BD) depression.

Methods

A total of 344 patients with mood disorders were included in our study. Participants were measured through a set of questionnaires including the Chinese version of ALS-SF, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and NEO-Five Factor Inventory (NEO-FFI). Exploratory factor analysis and confirmatory factor analysis were applied to examine the psychometric properties of ALS-SF. Besides, correlation and regression analyses were performed to explore the relationship between affective lability and depression, anxiety, and neuroticism. Independent samples t-tests were used to compare the subscale scores of ALS-SF between the MDD and BD depression groups.

Results

Results of factor analysis indicated that the model of ALS-SF was consistent with ALS-SF. The ALS-SF showed a solid validity and high internal consistency (Cronbach's alpha = 0.861). In addition, each subscale of ALS-SF was significantly correlated with PHQ-9, GAD-7, and NEO-FFI neuroticism subscale, except for the anger subscale showed no significant correlation with PHQ-9. Besides, the depression/elation and anger factor scores in patients with BD depression were higher than in patients with MDD.

Conclusion

Our study suggests that the Chinese version of ALS-SF has good reliability and validity for measuring affective lability in Chinese patients with mood disorders. Assessing affective lability would assist clinicians to distinguish between MDD and BP depression and may decrease the risks of misdiagnosis.

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.

Developing “MinDag” – an app to capture symptom variation and illness mechanisms in bipolar disorder

Introduction

The illness course of bipolar disorder (BD) is highly heterogeneous with substantial variation between individuals with the same BD subtype and within individuals over time. This heterogeneity is not well-delineated and hampers the development of more targeted treatment. Furthermore, although lifestyle-related behaviors are believed to play a role in the illness course, such mechanisms are poorly understood. To address some of these knowledge gaps, we aimed to develop an app for collection of multi-dimensional longitudinal data on BD-relevant symptoms and lifestyle-related behaviors.

Methods

An app named MinDag was developed at the Norwegian Center for Mental Disorders Research in Oslo, Norway. The app was designed to tap into selected areas: mood, sleep, functioning/activities (social, occupational, physical exercise, leisure), substance use, emotional reactivity, and psychotic experiences. Ethical, security and usability issues were highly prioritized throughout the development and for the final app solution. We conducted beta- and pilot testing to eliminate technical problems and enhance usability and acceptability.

Results

The final version of MinDag comprises six modules; three which are presented for the user once daily (the Sleep module in the morning and the Mood and Functoning/Activities modules in the evening) and three which are presented once weekly (Substance Use, Emotional Reactivity, and Psychotic Experiences modules). In general, MinDag was well received in both in the beta-testing and the pilot study, and the participants provided valuable feedback that was taken into account in the final development. MinDag is now in use as part of the research protocol at the NORMENT center and in a specialized treatment unit for BD at Oslo University Hospital in Norway.

Discussion

We believe that MinDag will generate unique longitudinal data well suited for capturing the heterogeneity of BD and clarifying important unresolved issues such as how life-style related behavior may influence BD symptoms. Also, the experiences and knowledge derived from the development of MinDag may contribute to improving the security, acceptability, and benefit of digital tools in mental health.

A systematic review of engagement reporting in remote measurement studies for health symptom tracking

Remote Measurement Technologies (RMTs) could revolutionise management of chronic health conditions by providing real-time symptom tracking. However, the promise of RMTs relies on user engagement, which at present is variably reported in the field. This review aimed to synthesise the RMT literature to identify how and to what extent engagement is defined, measured, and reported, and to present recommendations for the standardisation of future work. Seven databases (Embase, MEDLINE and PsycINFO (via Ovid), PubMed, IEEE Xplore, Web of Science, and Cochrane Central Register of Controlled Trials) were searched in July 2020 for papers using RMT apps for symptom monitoring in adults with a health condition, prompting users to track at least three times during the study period. Data were synthesised using critical interpretive synthesis. A total of 76 papers met the inclusion criteria. Sixty five percent of papers did not include a definition of engagement. Thirty five percent included both a definition and measurement of engagement. Four synthetic constructs were developed for measuring engagement: (i) engagement with the research protocol, (ii) objective RMT engagement, (iii) subjective RMT engagement, and (iv) interactions between objective and subjective RMT engagement. The field is currently impeded by incoherent measures and a lack of consideration for engagement definitions. A process for implementing the reporting of engagement in study design is presented, alongside a framework for definition and measurement options available. Future work should consider engagement with RMTs as distinct from the wider eHealth literature, and measure objective versus subjective RMT engagement.Registration: This review has been registered on PROSPERO [CRD42020192652].

Temporal relationships of ecological momentary mood and actigraphy-based sleep measures in bipolar disorder

Sleep disturbances are a key feature of bipolar disorder (BD), and poor sleep has been linked to mood symptoms. Recent use of ecological momentary assessment (EMA) has allowed for nuanced exploration of the sleep-mood link; though, the scale and directionality of this relationship is still unclear. Using EMA, actigraphy, and self-reported sleep measures, this study examines the concurrent and predictive relationships between sleep and mood. Participants with BD (n = 56) wore actigraphy devices for up to 14 days and completed validated scales and daily EMA surveys about mood and sleep quality. Linear mixed models were used to examine overall and time-lagged relationships between sleep and mood variables. EMA mood ratings were correlated with validated rating scales for depression, mania, anxiety, and impulsivity. Poor self-reported sleep quality was associated with worse overall ratings of sadness and anger. Worse self-reported sleep quality was associated with greater sadness the following day. Higher daytime impulsivity was associated with worse sleep quality the following night. Exploratory analyses found relationships between worse and more variable mood (sadness, anger, and impulsivity) with worse and more variable sleep that evening (efficiency, WASO, and sleep onset time). The sample size was modest, fairly homogenous, and included mainly euthymic persons with BD. EMA-based assessments of mood and sleep are correlated with validated scale scores and provide novel insight into intra-individual variability. Further work on the complex two-way interactions between sleep and mood is needed to better understand how to improve outcomes in BD.

Pilot Testing in the Wild: Feasibility, Acceptability, Usage Patterns, and Efficacy of an Integrated Web and Smartphone Platform for Bipolar II Disorder

Background

Bipolar II disorder (BD-II) is associated with significant burden, disability, and mortality; however, there continues to be a dearth of evidence-based psychological interventions for this condition. Technology-mediated interventions incorporating self-management have untapped potential to help meet this need as an adjunct to usual clinical care.

Objective

The objective of this pilot study is to assess the feasibility, acceptability, and clinical utility of a novel intervention for BD-II (Tailored Recovery-oriented Intervention for Bipolar II Experiences; TRIBE), in which mindfulness-based psychological content is delivered via an integrated web and smartphone platform. The focus of the study is evaluation of the dynamic use patterns emerging from ecological momentary assessment and intervention to assist the real-world application of mindfulness skills learned from web-delivered modules.

Methods

An open trial design using pretest and posttest assessments with nested qualitative evaluation was used. Individuals (aged 18-65 years) with a diagnosis of BD-II were recruited worldwide and invited to use a prototype of the TRIBE intervention over a 3-week period. Data were collected via web-based questionnaires and phone interviews at baseline and 3-week follow-up.

Results

A total of 25 participants completed baseline and follow-up assessments. Adherence rates (daily app use) were 65.6% across the 3-week study, with up to 88% (22/25) of participants using the app synergistically alongside the web-based program. Despite technical challenges with the prototype intervention (from user, hardware, and software standpoints), acceptability was adequate, and most participants rated the intervention positively in terms of concept (companion app with website: 19/25, 76%), content (19/25, 76%), and credibility and utility in supporting their management of bipolar disorder (17/25, 68%). Evaluation using behavioral archetypes identified important use pathways and a provisional model to inform platform refinement. As hypothesized, depression scores significantly decreased after the intervention (Montgomery-Asberg Depression Rating Scale baseline mean 8.60, SD 6.86, vs follow-up mean 6.16, SD 5.11; t₂₄=2.63; P=.01; Cohen d=0.53, 95% CI 0.52-4.36).

Conclusions

Our findings suggest that TRIBE is feasible and represents an appropriate and acceptable self-management program for patients with BD-II. Preliminary efficacy results are promising and support full development of TRIBE informed by the present behavioral archetype analysis. Modifications suggested by the pilot study include increasing the duration of the intervention and increasing technical support.

Process and Outcome Evaluations of Smartphone Apps for Bipolar Disorder: Scoping Review

Background

Mental health apps (MHAs) provide opportunities for accessible, immediate, and innovative approaches to better understand and support the treatment of mental health disorders, especially those with a high burden, such as bipolar disorder (BD). Many MHAs have been developed, but few have had their effectiveness evaluated.

Objective

This systematic scoping review explores current process and outcome measures of MHAs for BD with the aim to provide a comprehensive overview of current research. This will identify the best practice for evaluating MHAs for BD and inform future studies.

Methods

A systematic literature search of the health science databases PsycINFO, MEDLINE, Embase, EBSCO, Scopus, and Web of Science was undertaken up to January 2021 (with no start date) to narratively assess how studies had evaluated MHAs for BD.

Results

Of 4051 original search results, 12 articles were included. These 12 studies included 435 participants, and of these, 343 had BD type I or II. Moreover, 11 of the 12 studies provided the ages (mean 37 years) of the participants. One study did not report age data. The male to female ratio of the 343 participants was 137:206. The most widely employed validated outcome measure was the Young Mania Rating Scale, being used 8 times. The Hamilton Depression Rating Scale-17/Hamilton Depression Rating Scale was used thrice; the Altman Self-Rating Mania Scale, Quick Inventory of Depressive Symptomatology, and Functional Assessment Staging Test were used twice; and the Coping Inventory for Stressful Situations, EuroQoL 5-Dimension Health Questionnaire, Generalized Anxiety Disorder Scale-7, Inventory of Depressive Symptomatology, Mindfulness Attention Awareness Scale, Major Depression Index, Morisky-Green 8-item, Perceived Stress Scale, and World Health Organization Quality of Life-BREF were used once. Self-report measures were captured in 9 different studies, 6 of which used MONARCA. Mood and energy levels were the most commonly used self-report measures, being used 4 times each. Furthermore, 11 of the 12 studies discussed the various confounding factors and barriers to the use of MHAs for BD.

Conclusions

Reported low adherence rates, usability challenges, and privacy concerns act as barriers to the use of MHAs for BD. Moreover, as MHA evaluation is itself developing, guidance for clinicians in how to aid patient choices in mobile health needs to develop. These obstacles could be ameliorated by incorporating co-production and co-design using participatory patient approaches during the development and evaluation stages of MHAs for BD. Further, including qualitative aspects in trials that examine patient experience of both mental ill health and the MHA itself could result in a more patient-friendly fit-for-purpose MHA for BD.

Portable technologies for digital phenotyping of bipolar disorder: A systematic review

BACKGROUND

Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We present a systematic review of the evidence about the use of portable digital devices for the identification of BD, BD types and BD mood states and for symptom assessment.

METHODS

We searched Web of Knowledge^SM, Scopus ®, IEEE Xplore, and ACM Digital Library databases (until 5/1/2021) for articles evaluating the use of portable/wearable digital devices, such as smartphone apps, wearable sensors, audio and/or visual recordings, and multimodal tools. The protocol is registered in PROSPERO (CRD42020200086).

RESULTS

We included 62 studies (2325 BD; 724 healthy controls, HC): 27 using smartphone apps, either for recording self-assessments (n = 10) or for passively gathering metadata (n = 7) or both (n = 10); 15 using wearable sensors for physiological parameters; 17 analysing audio and/or video recordings; 3 using multiple technologies. Two thirds of the included studies applied artificial intelligence (AI)-based approaches. They achieved fair to excellent classification performances.

LIMITATIONS

The included studies had small sample sizes and marked heterogeneity. Evidence of overfitting emerged, limiting generalizability. The absence of clear guidelines about reporting classification performances, with no shared standard metrics, makes results hardly interpretable and comparable.

CONCLUSIONS

New technologies offer a noteworthy opportunity to BD digital phenotyping with objectivity and high granularity. AI-based models could deliver important support in clinical decision-making. Further research and cooperation between different stakeholders are needed for addressing methodological, ethical and socio-economic considerations.

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.

Patients' adherence to smartphone apps in the management of bipolar disorder: a systematic review

Background

Despite an increasing number of available mental health apps in the bipolar disorder field, these tools remain scarcely implemented in everyday practice and are quickly discontinued by patients after downloading. The aim of this study is to explore adherence characteristics of bipolar disorder patients to dedicated smartphone interventions in research studies.

Methods

A systematic review following PRISMA guidelines was conducted. Three databases (EMBASE, PsychInfo and MEDLINE) were searched using the following keywords: "bipolar disorder" or "mood disorder" or “bipolar” combined with “digital” or “mobile” or “phone” or “smartphone” or “mHealth” or “ehealth” or "mobile health" or “app” or “mobile-health”.

Results

Thirteen articles remained in the review after exclusion criteria were applied. Of the 118 eligible studies, 39 did not provide adherence characteristics. Among the selected papers, study length, sample size and definition of measures of adherence were strongly heterogeneous. Activity rates ranged from 58 to 91.6%.

Conclusion

The adherence of bipolar patients to apps is understudied. Standardised measures of adherence should be defined and systematically evaluated in future studies dedicated to these tools.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40345-021-00224-6.

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.

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.

Going digital: a narrative overview of the effects, quality and utility of mobile apps in chronic disease self-management

Objective Smartphone health applications (apps) are being increasingly used to assist patients in chronic disease self-management. The effects of such apps on patient outcomes are uncertain, as are design features that maximise usability and efficacy, and the best methods for evaluating app quality and utility. Methods In assessing efficacy, PubMed, Cochrane Library and EMBASE were searched for systematic reviews (and single studies if no systematic review was available) published between January 2007 and January 2018 using search terms (and synonyms) of 'smartphone' and 'mobile applications', and terms for each of 11 chronic diseases: asthma, chronic obstructive lung disease (COPD), diabetes, chronic pain, serious mental health disorders, alcohol and substance addiction, heart failure, ischaemic heart disease, cancer, cognitive impairment, chronic kidney disease (CKD). With regard to design features and evaluation methods, additional reviews were sought using search terms 'design', 'quality,' 'usability', 'functionality,' 'adherence', 'evaluation' and related synonyms. Results Of 13 reviews and six single studies assessing efficacy, consistent evidence of benefit was seen only with apps for diabetes, as measured by decreased glycosylated haemoglobin levels (HbA1c). Some, but not all, studies showed benefit in asthma, low back pain, alcohol addiction, heart failure, ischaemic heart disease and cancer. There was no evidence of benefit in COPD, cognitive impairment or CKD. In all studies, benefits were clinically marginal and none related to morbid events or hospitalisation. Twelve design features were identified as enhancing usability. An evaluation framework comprising 32 items was formulated. Conclusion Evidence of clinical benefit of most available apps is very limited. Design features that enhance usability and maximise efficacy were identified. A provisional 'first-pass' evaluation framework is proposed that can help decide which apps should be endorsed by government agencies following more detailed technical assessments and which could then be recommended with confidence by clinicians to their patients. What is known about the topic? Smartphone health apps have attracted considerable interest from patients and health managers as a means of promoting more effective self-management of chronic diseases, which leads to better health outcomes. However, most commercially available apps have never been evaluated for benefits or harms in clinical trials, and there are currently no agreed quality criteria, standards or regulations to ensure health apps are user-friendly, accurate in content, evidence based or efficacious. What does this paper add? This paper presents a comprehensive review of evidence relating to the efficacy, usability and evaluation of apps for 11 common diseases aimed at assisting patients in self-management. Consistent evidence of benefit was only seen for diabetes apps; there was absent or conflicting evidence of benefit for apps for the remaining 10 diseases. Benefits that were detected were of marginal clinical importance, with no reporting of hard clinical end-points, such as mortality or hospitalisations. Only a minority of studies explicitly reported using behaviour change theories to underpin the app intervention. Many apps lacked design features that the literature identified as enhancing usability and potential to confer benefit. Despite a plethora of published evaluation tools, there is no universal framework that covers all relevant clinical and technical attributes. An inclusive list of evaluation criteria is proposed that may overcome this shortcoming. What are the implications for practitioners? The number of smartphone apps will continue to grow, as will the appetite for patients and clinicians to use them in chronic disease self-management. However, the evidence to date of clinical benefit of most apps already available is very limited. Design features that enhance usability and clinical efficacy need to be considered. In making decisions about which apps should be endorsed by government agencies and recommended with confidence by clinicians to their patients, a comprehensive but workable evaluation framework needs to be used by bodies assuming the roles of setting and applying standards.

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.

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.

Mood instability across the perinatal period: A cross-sectional and longitudinal study

BACKGROUND

As a trans-diagnostic concept, mood instability (MI) is significantly linked to a variety of psychiatric disorders in general and clinical samples. However, there is limited research on perinatal MI, even though perinatal women experience an elevated level of MI. In this study, we examined the relationship between perinatal MI and its risk factors, the association between antenatal MI and postpartum depression (PPD), and the trajectory of perinatal MI.

METHODS

A total of 648 women participated in this longitudinal study at three points: T1 (17.4 ± 4.9 weeks pregnant), T2 (30.6 ± 2.7 weeks pregnant), and T3 (4.2 ± 2.1 weeks postpartum). Linear regression was used to examine MI and its risk factors, hierarchical multiple regression was utilized to investigate the relationship between antenatal MI and PPD, and a linear mixed model was employed to examine the trajectory of perinatal MI over T1-T3.

RESULTS

Perinatal depression, history of depression, and stress at T1, T2, and T3, and labor/birth complications at T3 were significant risk factors for MI. MI at T1 was associated with PPD after controlling for important confounders at T1. The trajectory of perinatal MI had a declined trend from early pregnancy to postpartum.

LIMITATIONS

The participants were predominantly Caucasian and with post-secondary education, which may limit the generalization of our findings. A lack of research on perinatal MI limited our ability to discuss the topic in relation to existing literature.

CONCLUSIONS

This study expands our understanding of MI in perinatal women, and indicates that more research is needed.

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.

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.

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.

Characterization of Mood Instability through Bipolar Disorders: A cluster-analytic approach using weekly prospective life-chart methodology

BackgroundThe aim of this study was to characterize mood instability (MI) in Bipolar Disorder (BD) and to investigate potential differences between subtype I and II. MethodsLife-charts from weekly mood ratings of 90 patients were used to compute: weeks spent with symptoms, number of episodes, and MI. Regression analyses were conducted to assess the relationship between BD subtype and MI adjusting by all potential confounding factors. Hierarchical cluster analysis was performed to determine the appropriate number of clusters that described the data and to assign subjects to a specific cluster based on their MI. We then compared clusters on clinical and psychosocial outcomes. ResultsMedian follow-up was 5 years (IQR: 3.6-7.9). Patients spent 15.2%, 5%, and 3% of follow-up with depressive, manic, and mixed symptoms, respectively. BD type II presented higher MI (β = 1.83, 95% CI: 0.66-3.00) and subsydromal symptoms than BD type I patients. No differences in functioning or recurrences were found between subtypes. Differences in MI between the two clusters mimicked those between type I and II but enhanced (β = 3.86, 95%CI -4.72, -2.66). High MI (n = 43) patients presented poorer functioning and higher recurrences compared to Low MI patients (n = 43). ConclusionBD type II presented higher MI and subsyndromal symptoms than BD type I patients. However, these differences did not translate into clinically relevant outcomes. A classification based on MI may provide useful clinical insights.

Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder

Variable mood is an important feature of psychiatric disorders. However, its measurement and relationship to objective measureas of physiology and behaviour have rarely been studied. Smart-phones facilitate continuous personalized prospective monitoring of subjective experience and behavioural and physiological signals can be measured through wearable devices. Such passive data streams allow novel estimates of diurnal variability. Phase and amplitude of diurnal rhythms were quantified using new techniques that fitted sinusoids to heart rate (HR) and acceleration signals. We investigated mood and diurnal variation for four days in 20 outpatients with bipolar disorder (BD), 14 with borderline personality disorder (BPD) and 20 healthy controls (HC) using a smart-phone app, portable electrocardiogram (ECG), and actigraphy. Variability in negative affect, positive affect, and irritability was elevated in patient groups compared with HC. The study demonstrated convincing associations between variability in subjective mood and objective variability in diurnal physiology. For BPD there was a pattern of positive correlations between mood variability and variation in activity, sleep and HR. The findings suggest BPD is linked more than currently believed with a disorder of diurnal rhythm; in both BPD and BD reducing the variability of sleep phase may be a way to reduce variability of subjective mood.

Longitudinal mood monitoring in bipolar disorder: Course of illness as revealed through a short messaging service

BACKGROUND

Online self-monitoring of mood can be used to investigate differences in course patterns across patient groups. This study explored the feasibility of remote symptom capture with True Colours, a self-rated online mood monitoring tool completed on a weekly basis.

METHODS

Participants with bipolar disorder (N = 297) completed weekly depression and mania questionnaires over an average of 27.5 months (range 1 -81 months). Subgroups defined by sex, age, and bipolar I vs. II status were compared on time in various mood states, number of episodes, and week-to-week mood variability.

RESULTS

Compliance with weekly questionnaires was generally high (median, 92% of weeks). Mood symptoms occurred for an average of 55.4% of weeks across the follow-up period. Females spent more time with hypomanic/manic and depressive symptoms and had more depressive episodes compared to males. Younger participants were found to experience more hypomanic/manic episodes and showed greater variability in mood symptoms than older participants. No significant differences in mood symptoms or variability were observed between bipolar I and II patients.

LIMITATIONS

This was a naturalistic study with a heterogeneous cohort, and did not include a control group. True Colours does not identify mood fluctuations that may occur in the days between weekly assessments.

CONCLUSIONS

Monitoring moods through an online tool is both feasible and informative regarding course of illness in patients with bipolar disorder. Interventions aiming to reduce mood variability and manic/hypomanic episodes in the early phases of bipolar disorder may enhance the long-term symptomatic course of the illness.

American tertiary clinic-referred bipolar II disorder versus bipolar I disorder associated with hastened depressive recurrence

BACKGROUND

Bipolar disorder (BD) is a chronic, frequently comorbid condition characterized by high rates of mood episode recurrence and suicidality. Little is known about prospective longitudinal characterization of BD type II (BD II) versus type I (BD I) in relation to time to depressive recurrence and recovery from major depressive episode. We therefore assessed times to depressive recurrence/recovery in tertiary clinic-referred BD II versus I patients.

METHODS

Outpatients referred to Stanford BD Clinic during 2000-2011 were assessed with Systematic Treatment Enhancement Program for BD (STEP-BD) Affective Disorders Evaluation and with Clinical Monitoring Form during up to 2 years of naturalistic treatment. Prevalence and clinical correlates of bipolar subtype in recovered (euthymic ≥8 weeks) and depressed patients were assessed. Kaplan-Meier analyses assessed the relationships between bipolar subtype and longitudinal depressive severity, and Cox proportional hazard analyses assessed the potential mediators.

RESULTS

BD II versus BD I was less common among 105 recovered (39.0 vs. 61.0%, p = 0.03) and more common among 153 depressed (61.4 vs. 38.6%, p = 0.006) patients. Among recovered patients, BD II was associated with 6/25 (24.0%) baseline unfavorable illness characteristics/mood symptoms/psychotropics and hastened depressive recurrence (p = 0.015). Among depressed patients, BD II was associated with 8/25 (33.0%) baseline unfavorable illness characteristics/mood symptoms/psychotropics, but only non-significantly associated with delayed depressive recovery.

CONCLUSIONS

BD II versus BD I was significantly associated with current depression and hastened depressive recurrence, but only non-significantly associated with delayed depressive recovery. Research on bipolar subtype relationships with depressive recurrence/recovery is warranted to enhance clinical management of BD patients.

The promise of digital mood tracking technologies: are we heading on the right track?

The growing understanding that mood disorders are dynamic in nature and fluctuate over variable epochs of time has compelled researchers to develop innovative methods of monitoring mood. Technological advancement now allows for the detection of minute-to-minute changes while also capturing a longitudinal perspective of an individual's illness. Traditionally, assessments of mood have been conducted by means of clinical interviews and paper surveys. However, these methods are often inaccurate due to recall bias and compliance issues, and are limited in their capacity to collect and process data over long periods of time. The increased capability, availability and affordability of digital technologies in recent decades has offered a novel, non-invasive alternative to monitoring mood and emotion in daily life. This paper reviews the emerging literature addressing the use of digital mood tracking technologies, primarily focusing on the strengths and inherent limitations of using these new methods including electronic self-report, behavioural data collection and wearable physiological biosensors. This developing field holds great promise in generating novel insights into the mechanistic processes of mood disorders and improving personalised clinical care. However, further research is needed to validate many of these novel approaches to ensure that these devices are indeed achieving their purpose of capturing changes in mood.

Digital health technology for use in patients with serious mental illness: a systematic review of the literature

BACKGROUND

As the capabilities and reach of technology have expanded, there is an accompanying proliferation of digital technologies developed for use in the care of patients with mental illness. The objective of this review was to systematically search published literature to identify currently available health technologies and their intended uses for patients with serious mental illness.

MATERIALS AND METHODS

The Medline, Embase, and BIOSIS Previews electronic databases were searched to identify peer-reviewed English language articles that reported the use of digital, mobile, and other advanced technology in patients with schizophrenia/schizoaffective disorder, bipolar disorder, and major depressive disorder. Eligible studies were systematically reviewed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

Eighteen studies that met the inclusion criteria were identified. Digital health technologies (DHTs) assessed in the selected studies included mobile applications (apps), digital medicine, digital personal health records, and an electronic pill container. Smartphone apps accounted for the largest share of DHTs. The intended uses of DHTs could be broadly classified as monitoring to gain a better understanding of illness, clinical assessment, and intervention. Overall, studies indicated high usability/feasibility and efficacy/effectiveness, with several reporting validity against established clinical scales. Users were generally engaged with the DHT, and mobile assessments were deemed helpful in monitoring disease symptoms.

CONCLUSION

Rapidly proliferating digital technologies seem to be feasible for short-term use in patients with serious mental illness; nevertheless, long-term effectiveness data from naturalistic studies will help demonstrate their usefulness and facilitate their adoption and integration into the mental health-care system.

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Background

Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones.

Objective

We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes.

Methods

We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols.

Results

A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage.

Conclusions

The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.

Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment

BACKGROUND

We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables.

OBJECTIVE

The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety.

METHODS

We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable.

RESULTS

We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC.

CONCLUSIONS

This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice.

Differences in clinical presentation between bipolar I and II disorders in the early stages of bipolar disorder: A naturalistic study

AIM

In a naturalistic clinical study of patients in the early stages of bipolar disorders the aim was to assess differences between patients with bipolar I (BD I) and bipolar II (BD II) disorders on clinical characteristics including affective symptoms, subjective cognitive complaints, functional level, the presence of comorbid personality disorders and coping strategies.

METHODS

Diagnoses were confirmed using the Structured Clinical Interview for DSM-IV Disorders. Clinical symptoms were rated with the Young Mania Rating Scale and the Hamilton Depression Rating Scale, and functional status using the Functional Assessment Short Test. Cognitive complaints were assessed using the Massachusetts General Hospital Cognitive and Physical Functioning Questionnaire, the presence of comorbid personality disorders using the Standardized Assessment of Personality - Abbreviated Scale and coping style using the Coping Inventory for Stressful Situations.

RESULTS

In total, 344 patients were included (BD I (n=163) and BD II (n=181). Patients with BD II presented with significantly more depressive symptoms, more cognitive complaints, lower overall functioning, and a higher prevalence of comorbid personality disorders. Finally, they exhibited a trend towards using less adaptive coping styles.

LIMITATION

It cannot be omitted that some patients may have progressed from BD II to BD I. Most measures were based on patient self report.

CONCLUSIONS

Overall, BD II was associated with a higher disease burden. Clinically, it is important to differentiate BD II from BD I and research wise, there is a need for tailoring and testing specific interventions towards BD II.

Psychoeducation and online mood tracking for patients with bipolar disorder: A randomised controlled trial

BACKGROUND

Psychoeducation is an effective adjunct to medications in bipolar disorder (BD). Brief psychoeducational approaches have been shown to improve early identification of relapse. However, the optimal method of delivery of psychoeducation remains uncertain. Here, our objective was to compare a short therapist-facilitated vs. self-directed psychoeducational intervention for BD.

METHODS

BD outpatients who were receiving medication-based treatment were randomly assigned to 5 psychoeducation sessions administered by a therapist (Facilitated Integrated Mood Management; FIMM; n=60), or self-administered psychoeducation (Manualized Integrated Mood Management; MIMM; n=61). Follow-up was based on patients' weekly responses to an electronic mood monitoring programme over 12 months.

RESULTS

Over follow-up, there were no group differences in weekly self-rated depression symptoms or relapse/readmission rates. However, knowledge of BD (assessed with the Oxford Bipolar Knowledge questionnaire (OBQ)) was greater in the FIMM than the MIMM group at 3 months. Greater illness knowledge at 3 months was related to a higher proportion of weeks well over 12 months.

LIMITATIONS

Features of the trial may have reduced the sensitivity to our psychoeducation approach, including that BD participants had been previously engaged in self-monitoring.

CONCLUSIONS

Improved OBQ score, while accelerated by a short course of therapist-administered psychoeducation (FIMM), was seen after both treatments. It was associated with better outcome assessed as weeks well. When developing and testing a new psychosocial intervention, studies should consider proximal outcomes (e.g., acquired knowledge) and their short-term impact on illness course in bipolar disorder.

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

BACKGROUND

Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose.

METHODS

We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools.

RESULTS

MZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires.

LIMITATIONS

All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings.

CONCLUSIONS

MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

Mood and motor activity in euthymic bipolar disorder with sleep disturbance

BACKGROUND

The aims of this observational study of patients with euthymic bipolar disorder and sleep disturbance were to 1) compare characteristics related to mood and sleep between two groups with stable and unstable rest-activity cycles and 2) detect between-group differences in motor activity patterns.

METHODS

43 patients wore an actigraph for 6-8 days while reporting daily mood and sleep. Patients were defined as having an unstable rest-activity cycle if their diurnal active period duration presented variation above 2h from the mean during one week: 22 patients had stable and 21 unstable rest-activity cycles. Mood variability was defined as at least moderate symptoms and a change across two levels on a 7-point mood scale during one week.

RESULTS

Patients with unstable rest-activity cycles were younger (37 vs. 48 years, p=0.01) and displayed more mood variability (p=0.02). Ten of 11 patients diagnosed with delayed sleep phase disorder were in the unstable group (p<0.01), and the unstable group had later and more variable get-up-times and bedtimes. In actigraphy recordings, the mean activity counts per minute did not differ between groups, but the minute-to-minute variability was elevated (p=0.04) and increased relative to the overall variability (p=0.03).

LIMITATIONS

A relatively small study sample and a 1-week study period prevent exploration of long-term clinical implications of results.

CONCLUSIONS

A subgroup of euthymic patients with bipolar disorder displayed unstable rest-activity cycles combined with mood variability and motor activity patterns that resemble findings in affective episodes.

Evidence-based guidelines for treating bipolar disorder: Revised third edition recommendations from the British Association for Psychopharmacology

The British Association for Psychopharmacology guidelines specify the scope and targets of treatment for bipolar disorder. The third version is based explicitly on the available evidence and presented, like previous Clinical Practice Guidelines, as recommendations to aid clinical decision making for practitioners: it may also serve as a source of information for patients and carers, and assist audit. The recommendations are presented together with a more detailed review of the corresponding evidence. A consensus meeting, involving experts in bipolar disorder and its treatment, reviewed key areas and considered the strength of evidence and clinical implications. The guidelines were drawn up after extensive feedback from these participants. The best evidence from randomized controlled trials and, where available, observational studies employing quasi-experimental designs was used to evaluate treatment options. The strength of recommendations has been described using the GRADE approach. The guidelines cover the diagnosis of bipolar disorder, clinical management, and strategies for the use of medicines in short-term treatment of episodes, relapse prevention and stopping treatment. The use of medication is integrated with a coherent approach to psychoeducation and behaviour change.

Remote Patient Monitoring via Non-Invasive Digital Technologies: A Systematic Review

BACKGROUND

We conducted a systematic literature review to identify key trends associated with remote patient monitoring (RPM) via noninvasive digital technologies over the last decade.

MATERIALS AND METHODS

A search was conducted in EMBASE and Ovid MEDLINE. Citations were screened for relevance against predefined selection criteria based on the PICOTS (Population, Intervention, Comparator, Outcomes, Timeframe, and Study Design) format. We included studies published between January 1, 2005 and September 15, 2015 that used RPM via noninvasive digital technology (smartphones/personal digital assistants [PDAs], wearables, biosensors, computerized systems, or multiple components of the formerly mentioned) in evaluating health outcomes compared to standard of care or another technology. Studies were quality appraised according to Critical Appraisal Skills Programme.

RESULTS

Of 347 articles identified, 62 met the selection criteria. Most studies were randomized control trials with older adult populations, small sample sizes, and limited follow-up. There was a trend toward multicomponent interventions (n = 26), followed by smartphones/PDAs (n = 12), wearables (n = 11), biosensor devices (n = 7), and computerized systems (n = 6). Another key trend was the monitoring of chronic conditions, including respiratory (23%), weight management (17%), metabolic (18%), and cardiovascular diseases (16%). Although substantial diversity in health-related outcomes was noted, studies predominantly reported positive findings.

CONCLUSIONS

This review will help decision makers develop a better understanding of the current landscape of peer-reviewed literature, demonstrating the utility of noninvasive RPM in various patient populations. Future research is needed to determine the effectiveness of RPM via noninvasive digital technologies in delivering patient healthcare benefits and the feasibility of large-scale implementation.