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

The Causal Effects between Mood Swings and Gastrointestinal Diseases: A Mendelian Randomization Study

Background

Numerous studies have examined the links between mental disorders such as depression and bipolar disorder, and gastrointestinal (GI) diseases. However, few studies have investigated the link between mood swings and GI diseases. Given the impact of mood swings on various conditions and the growing comprehension of the gut-brain axis, this study aims to explore their causal relationship using Mendelian randomization (MR) methods.

Methods

Single-nucleotide polymorphisms (SNPs) associated with mood swings were obtained from a recent study. SNPs associated with GI diseases were identified from the FinnGen project. We conducted two-sample bidirectional MR analyses using three methods, primarily the inverse variance weighting (IVW) method. Furthermore, we performed sensitivity analyses and false discovery rate (FDR) analysis to validate the accuracy and robustness of the results.

Results

Bidirectional MR analysis revealed significant causal effects between mood swings and GI diseases according to the IVW method (odds ratio (OR): 1.213; 95% confidence interval (CI): 1.118-1.316; P = 3.490e-6; P FDR = 8.730e-5). Mood swings were linked to an increased risk for 11 of 24 diseases, including five upper GI diseases (gastroesophageal reflux disease (GERD), acute gastritis, gastroduodenal ulcer, duodenal ulcer, and functional dyspepsia), two lower GI diseases (diverticular disease of the intestine and irritable bowel syndrome (IBS)) and four hepatobiliary and pancreatic diseases (nonalcoholic fatty liver disease (NAFLD), chronic pancreatitis, acute pancreatitis, and pancreatic cancer). Inverse MR analysis showed no causal relationship between 24 GI diseases and mood swings.

Conclusions

This comprehensive MR analysis suggests that genetically predicted mood swings may be a risk factor in the development of GI diseases. Interventions for mood swings may help to treat GI diseases.

Greater within- and between-day instability is associated with worse anxiety and depression symptoms

BACKGROUND

Depression and anxiety affect hundreds of millions of people worldwide, and their prevalence increased during the COVID-19 pandemic as social schedules were disrupted. This study explores the associations between anxiety and depression and within- and between-day instability of affective, somatic, and cognitive symptoms during the early pandemic stages.

METHODS

Participants (n = 153, ages 18-77, 72 % female) reported daily levels of affective (anxiety/sadness), somatic (appetite/sleepiness), and cognitive (concentration/energy) symptoms for 14-44 days at five timepoints: 0, 3, 6, 9, and 12 h after awakening. At the end of the study, participants completed validated scales for anxiety (GAD-7) and depression (PHQ-9). Symptom instability was assessed using the Absolute Real Variability (ARV) index. Regression models examined within-day instability (WD-I) and between-day instability (BD-I) with GAD-7 and PHQ-9 scores as outcomes.

RESULTS

Greater instability (both WD-I and BD-I) of affective symptoms correlated with elevated GAD-7 and PHQ-9 scores. For somatic and cognitive symptoms, greater BD-I was associated with higher scores.

LIMITATIONS

The study used retrospective daily data, which could benefit from real-time assessments for improved accuracy.

CONCLUSIONS

This study provides empirical evidence of a connection between greater anxiety and depression severity and increased instability in daily mood and physiological symptoms. The findings underscore the importance of consistent symptom monitoring to understand overall mental health trajectories. Additionally, it highlights the role of daily routines in stabilizing the circadian system, potentially regulating physiological and psychological processes and reducing symptom instability.

The purpose of early maladaptive schemas (EMS) in the relationship dysfunction among people with bipolar disorder in the euthymic phase

OBJECTIVES

Although free from characterized manic and depressive episodes, the euthymic period in bipolar disorder (BD) remains characterized by a whole host of difficulties, particularly relational. These difficulties are factors of vulnerabilities and relapses. People's perception of their own relationships has an impact on their symptomatology, their responses to treatment and on relapse rates. Young's early maladaptive schemas (EMS) approach proves to be relevant for understanding the construction of these perceptions and working on them. Nevertheless, to this date, few studies have investigated which EMS are related to relationship dysfunction in this particular population. Determining the link between EMS and relationship difficulties would be relevant to understand psychosocial impairment of people with BD in euthymic states. The present study aims to investigate the associations between the different domains of EMS and relationship dysfunction among patients with bipolar disorder in the euthymic phase.

METHODS

Data are extracted from the FACE-BD cohort, within the BD Expert Center in Versailles. Inclusion criteria were to be aged between 18 and 65 and to be an outpatient with a diagnosis of bipolar disorder (DSM-IV-TR). Patients had to be euthymic at the time of inclusion, according to DSM-IV-TR criteria with a cut-off score of 14 on the Montgomery-Asberg Depression Rating Scale and the Young Mania Rating Scale. Non-inclusion criteria were meeting at the time of the study the criteria for characteristic depressive episode, hypomania or mania according to the DSM-IV-TR. Sociodemographic data, clinical features associated with relationship dysfunction were assessed. EMS and EMS domains were assessed with the YSQ-R short form (Young Schema Questionnaire 3 Short Form) and current relationship dysfunction were assessed with the FAST (Functioning assessment short test subscale). Successive simple linear regression analyses were performed to investigate the association between the severity scores of each EMS and the intensity of relationship dysfunction. Furthermore, successive simple linear regression analyses investigated the association between EMS domain scores and intensity of relationship dysfunction. Multiple linear regression analyses were performed to test the association between EMS scores, then EMS domains, and the intensity of relationship dysfunction after adjusting for age as well as the intensity of residual depressive and manic symptoms.

RESULTS

Relationship dysfunction is partly associated with EMS activation in particular in the separation and rejection domain (P<0.0001), the other-directedness domain (P=0.031) and the over-vigilance and inhibition domain (P=0.005). Having residual depressive symptoms is also among the factors contributing to the relationship dysfunctions of people with bipolar disorder in the euthymic phase.

DISCUSSION

This is the first study demonstrating that the activation of several domains of EMS is a risk factor of relationships difficulties for people in euthymic phase of bipolar disorder. It is necessary to identify which EMS are specifically activated and their domains of belonging in order to prevent and reduce them. EMS are a lever for functional remission. It is therefore relevant to refer people reporting relationship problems to schema therapy consistent with a personalized care. Finally, future studies should focus on the mechanisms underlying the complex relationship between EMS domains and relationship dysfunction in people with bipolar disorder in the euthymic phase. It may also be relevant for future research to control for different types of relationship dysfunction. EMS may be differentially associated with several types of interpersonal problems. The relations between different adaptation styles and EMS should be further investigated to offer more personalized care, with the aim to improve functional remission.

Genetically predicted mood swings increased risk of cardiovascular disease: Evidence from a Mendelian randomization analysis

BACKGROUND

Mood swings is linked to a higher risk of cardiovascular diseases (CVDs). However, the causal relationships between them remain unknown.

METHODS

We conducted this Mendelian randomization (MR) analysis to evaluate the causal associations between mood swings (n = 373,733) and 5 CVDs, including CAD, MI, HF, AF, and stroke using summary data of large-scale genome-wide association studies (GWAS). FinnGen datasets validated the results. Various MR approaches, sensitivity analyses, multivariable MR (MVMR), and two-step MR mediation analyses were applied.

RESULTS

The MR analysis revealed significant causal effects of mood swings on CAD (OR = 1.45, 95 % CI 1.24-1.71; P = 5.52e-6), MI (OR = 1.60, 95 % CI 1.32-1.95; P = 1.77e-6), HF (OR = 1.42, 95 % CI 1.18-1.71; P = 2.32e-4), and stroke (OR = 1.48, 95 % CI 1.19-1.83; P = 3.46e-4), excluding AF (P = 0.16). In the reverse MR analysis, no causal relationships were observed. The results were reproducible using FinnGen data. In the MVMR analysis, the causal effects of mood swings on CAD, MI, HF and stroke still remain significant after adjusting potential confounding factors including BMI, smoking and T2DM, but not for LDL and hypertension. Further mediation analysis indicated hypertension may mediate the causal pathways from mood swings to CAD (18.11 %, 95 % CI: 8.83 %-27.39 %), MI (16.40 %, 95 % CI: 7.93 %-24.87 %), HF (13.06 %, 95 % CI: 6.25 %-19.86 %), and stroke (18.04 %, 95 % CI: 8.73 %-27.34 %).

CONCLUSION

Mood swings has a significant causal impact on the development of CAD, MI, HF, and stroke, partly mediated by hypertension.

A randomized controlled trial to compare the effects of time-restricted eating versus Mediterranean diet on symptoms and quality of life in bipolar disorder

BACKGROUND

The primary objective of this randomized controlled trial (RCT) is to establish the effectiveness of time-restricted eating (TRE) compared with the Mediterranean diet for people with bipolar disorder (BD) who have symptoms of sleep disorders or circadian rhythm sleep-wake disruption. This work builds on the growing evidence that TRE has benefits for improving circadian rhythms. TRE and Mediterranean diet guidance will be offered remotely using self-help materials and an app, with coaching support.

METHODS

This study is an international RCT to compare the effectiveness of TRE and the Mediterranean diet. Three hundred participants will be recruited primarily via social media. Main inclusion criteria are: receiving treatment for a diagnosis of BD I or II (confirmed via DIAMOND structured diagnostic interview), endorsement of sleep or circadian problems, self-reported eating window of ≥ 12 h, and no current mood episode, acute suicidality, eating disorder, psychosis, alcohol or substance use disorder, or other health conditions that would interfere with or limit the safety of following the dietary guidance. Participants will be asked to complete baseline daily food logging for two weeks and then will be randomly allocated to follow TRE or the Mediterranean diet for 8 weeks, during which time, they will continue to complete daily food logging. Intervention content will be delivered via an app. Symptom severity interviews will be conducted at baseline; mid-intervention (4 weeks after the intervention begins); end of intervention; and at 6, 9, and 15 months post-baseline by phone or videoconference. Self-rated symptom severity and quality of life data will be gathered at those timepoints, as well as at 16 weeks post baseline. To provide a more refined index of whether TRE successfully decreases emotional lability and improves sleep, participants will be asked to complete a sleep diary (core CSD) each morning and complete six mood assessments per day for eight days at baseline and again at mid-intervention.

DISCUSSION

The planned research will provide novel and important information on whether TRE is more beneficial than the Mediterranean diet for reducing mood symptoms and improving quality of life in individuals with BD who also experience sleep or circadian problems.

TRIAL REGISTRATION

ClinicalTrials.gov ID NCT06188754.

Development and application of emotion recognition technology - a systematic literature review

BACKGROUND

There is a mutual influence between emotions and diseases. Thus, the subject of emotions has gained increasing attention.

OBJECTIVE

The primary objective of this study was to conduct a comprehensive review of the developments in emotion recognition technology over the past decade. This review aimed to gain insights into the trends and real-world effects of emotion recognition technology by examining its practical applications in different settings, including hospitals and home environments.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines and included a search of 4 electronic databases, namely, PubMed, Web of Science, Google Scholar and IEEE Xplore, to identify eligible studies published between 2013 and 2023. The quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) criteria. The key information from the studies, including the study populations, application scenarios, and technological methods employed, was summarized and analyzed.

RESULTS

In a systematic literature review of the 44 studies that we analyzed the development and impact of emotion recognition technology in the field of medicine from three distinct perspectives: "application scenarios," "techniques of multiple modalities," and "clinical applications." The following three impacts were identified: (i) The advancement of emotion recognition technology has facilitated remote emotion recognition and treatment in hospital and home environments by healthcare professionals. (ii) There has been a shift from traditional subjective emotion assessment methods to multimodal emotion recognition methods that are grounded in objective physiological signals. This technological progress is expected to enhance the accuracy of medical diagnosis. (iii) The evolving relationship between emotions and disease throughout diagnosis, intervention, and treatment processes holds clinical significance for real-time emotion monitoring.

CONCLUSION

These findings indicate that the integration of emotion recognition technology with intelligent devices has led to the development of application systems and models, which provide technological support for the recognition of and interventions for emotions. However, the continuous recognition of emotional changes in dynamic or complex environments will be a focal point of future research.

Affect dimensions and variability during major depressive episodes: Ecological momentary assessment of unipolar, bipolar, and borderline patients and healthy controls

Differentiating major depressive episodes (MDEs) of major depressive disorder (MDD), bipolar disorder (MDE/BD) and the MDEs comorbid with borderline personality disorder (MDE/BPD) is crucial for appropriate treatment, and knowledge of phenomenological differences may aid this. However, studies comparing affect experiences of these three patient groups and healthy subjects are scarce. In our study, participants (N = 114), including patients with MDD (n = 34), MDE/BD (n = 27), and MDE/BPD (n = 24), and healthy controls (HC, n = 29) responded to ecological momentary assessment (EMA) with ten circumplex model affect items ten times daily for seven days (7709 recordings). Explorative factor analysis resulted in two affect dimensions. The positive dimension included active, excited, cheerful (high arousal), and content (low arousal) affects, and the negative dimension irritated, angry, and nervous (high arousal) affects. Relative to HC, patients reported 3.5-fold negative affects (mean MDD 1.36 (SD 0.92), MDE/BD 1.43 (0.76), MDE/BPD 1.81 (0.95) vs. HC 0.44 (0.49) (p < 0.01)) but 0.5-fold positive affects (2.01 (0.90), 1.95 (0.89), 2.24 (1.03), vs. 3.2 (0.95), respectively (p < 0.01)). We used multilevel modelling. Negative-affect within-individual stability was lowest in MDE/BPD and highest in MDD. Negative affect predicted concurrent positive affect more in MDE/BPD than in MDD. Moderate size of subcohorts and no inpatients were limitations. Despite apparently similar MDEs, affective experiences may differ between BPD, BD, and MDD patients. Clinical subgroups of patients with depression may vary in affective instability and concurrent presence of negative and positive affects during depression.

Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol

INTRODUCTION

The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

AIM

To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

METHODS AND ANALYSIS

In an ongoing clinical feasibility study, participants with a confirmed PD diagnosis are being recruited. Participants perform activity tests, including Timed Up and Go (TUG), tremor, finger tapping, drawing and vocalisation, once a week for 2 months using the Mobistudy smartphone app in their homes. Concurrently, participants wear the GENEActiv wrist device for 28 days to measure actigraphy continuously. In addition to using sensors, participants complete the Beck's Depression Inventory, Non-Motor Symptoms Questionnaire (NMSQuest) and Parkinson's Disease Questionnaire (PDQ-8) questionnaires at baseline, at 1 month and at the end of the study. Sleep disorders are assessed through the Parkinson's Disease Sleep Scale-2 questionnaire (weekly) and a custom sleep quality daily questionnaire. User experience questionnaires, Technology Acceptance Model and User Version of the Mobile Application Rating Scale, are delivered at 1 month. Clinical assessment (Movement Disorder Society-Unified Parkinson Disease Rating Scale (MDS-UPDRS)) is performed at enrollment and the 2-month follow-up visit. During visits, a TUG test is performed using the smartphone and the G-Walk motion sensor as reference device. Signal processing and machine learning techniques will be employed to analyse the data collected from Mobistudy app and the GENEActiv and correlate them with the MDS-UPDRS. Compliance and user aspects will be informing the long-term feasibility.

ETHICS AND DISSEMINATION

The study received ethical approval by the Swedish Ethical Review Authority (Etikprövningsmyndigheten), with application number 2022-02885-01. Results will be reported in peer-reviewed journals and conferences. Results will be shared with the study participants.

The computational psychopathology of emotion

Mood and anxiety disorders involve recurring, maladaptive patterns of distinct emotions and moods. Here, we argue that understanding these maladaptive patterns first requires understanding how emotions and moods guide adaptive behavior. We thus review recent progress in computational accounts of emotion that aims to explain the adaptive role of distinct emotions and mood. We then highlight how this emerging approach could be used to explain maladaptive emotions in various psychopathologies. In particular, we identify three computational factors that may be responsible for excessive emotions and moods of different types: self-intensifying affective biases, misestimations of predictability, and misestimations of controllability. Finally, we outline how the psychopathological roles of these factors can be tested, and how they may be used to improve psychotherapeutic and psychopharmacological interventions.

Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects

Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.

The IBER study: a feasibility randomised controlled trial of imagery based emotion regulation for the treatment of anxiety in bipolar disorder

BACKGROUND

Intrusive mental imagery is associated with anxiety and mood instability within bipolar disorder and therefore represents a novel treatment target. Imagery Based Emotion Regulation (IBER) is a brief structured psychological intervention developed to enable people to use the skills required to regulate the emotional impact of these images.

METHODS

Participants aged 18 and over with a diagnosis of bipolar disorder and at least a mild level of anxiety were randomly assigned (1:1) to receive IBER plus treatment as usual (IBER + TAU) or treatment as usual alone (TAU). IBER was delivered in up to 12 sessions overs 16 weeks. Clinical and health economic data were collected at baseline, end of treatment and 16-weeks follow-up. Objectives were to inform the recruitment process, timeline and sample size estimate for a definitive trial and to refine trial procedures. We also explored the impact on participant outcomes of anxiety, depression, mania, and mood stability at 16-weeks and 32-weeks follow-up.

RESULTS

Fifty-seven (28: IBER + TAU, 27: TAU) participants from two sites were randomised, with 50 being recruited within the first 12 months. Forty-seven (82%) participants provided outcome data at 16 and 32-weeks follow-up. Thirty-five participants engaged in daily mood monitoring at the 32-week follow-up stage. Retention in IBER treatment was high with 27 (96%) attending ≥ 7 sessions. No study participants experienced a serious adverse event.

DISCUSSION

The feasibility criteria of recruitment, outcome completion, and intervention retention were broadly achieved, indicating that imagery-focused interventions for bipolar disorder are worthy of further investigation.

Actigraphy-derived physical activity levels and circadian rhythm parameters in patients with psoriatic arthritis: relationship with disease activity, mood, age and BMI

Background

Psoriatic arthritis (PsA) is associated with sleep disturbance, depression and a lifetime risk of obesity and cardiovascular disease. To date, there have been no studies investigating the relationship between objectively-measured physical activity (PA) levels and circadian rhythm disturbance with disease activity, daily symptoms and mood in patients with PsA.

Objective

This pilot study aimed to investigate the relationship between disease activity, daily symptoms and mood on PA and circadian rhythm in PsA.

Design

A prospective cohort study recruiting adults with PsA from rheumatology clinics at a single centre in the UK.

Methods

Participants wore an actigraph and recorded their symptoms and mood on a daily basis via a smartphone app for 28 days. Time spent in sedentary, light and moderate-to-vigorous physical activity (MVPA) and parameters reflecting the circadian rhythm of the rest-activity pattern were derived. This included the onset time of the least active 5-h (L5) and most active 10-h (M10) daily consecutive periods and the relative amplitude (RA). The relationship factors between baseline clinical status, daily symptoms, PA and circadian measures were examined using linear mixed effect regression models.

Results

Nineteen participants (8/19 female) were included. Participants with active PsA spent 63.87 min (95% CI: 18.5-109.3, p = 0.008) more in inactivity and 30.78 min (95% CI: 0.4-61.1, p = 0.047) less in MVPA per day compared to those in minimal disease activity (MDA). Age, body mass index and disease duration were also associated with PA duration. Participants with worse functional impairment had an M10 onset time 1.94 h (95% CI: 0.05-3.39, p = 0.011) later than those with no reported functional impairment. No differences were detected for L5 onset time or RA. Higher scores for positive mood components such as feeling energetic, cheerful and elated were associated with less time in inactivity and greater time spent in MVPA overall.

Conclusion

Our study highlights differences in PA and circadian rest-activity pattern timing based on disease activity, disability and daily mood in PsA. Reduced PA levels in patients with active disease may contribute to the observed increased risk of cardiovascular and metabolic sequelae, with further studies exploring this need.

Early trajectory of clinical global impression as a transdiagnostic predictor of psychiatric hospitalisation: a retrospective cohort study

BACKGROUND

Identifying patients most at risk of psychiatric hospitalisation is crucial to improving service provision and patient outcomes. Existing predictors focus on specific clinical scenarios and are not validated with real-world data, limiting their translational potential. This study aimed to determine whether early trajectories of Clinical Global Impression Severity are predictors of 6 month risk of hospitalisation.

METHODS

This retrospective cohort study used data from the NeuroBlu database, an electronic health records network from 25 US mental health-care providers. Patients with an ICD-9 or ICD-10 code of major depressive disorder, bipolar disorder, generalised anxiety disorder, post-traumatic stress disorder, schizophrenia or schizoaffective disorder, ADHD, or personality disorder were included. Using this cohort, we assessed whether clinical severity and instability (operationalised using Clinical Global Impression Severity measurements) during a 2-month period were predictors of psychiatric hospitalisation within the next 6 months.

FINDINGS

36 914 patients were included (mean age 29·7 years [SD 17·5]; 21 156 [57·3%] female, 15 748 [42·7%] male; 20 559 [55·7%] White, 4842 [13·1%] Black or African American, 286 [0·8%] Native Hawaiian or other Pacific Islander, 300 [0·8%] Asian, 139 [0·4%] American Indian or Alaska Native, 524 (1·4%) other or mixed race, and 10 264 [27·8%] of unknown race). Clinical severity and instability were independent predictors of risk of hospitalisation (adjusted hazard ratio [HR] 1·09, 95% CI 1·07-1·10 for every SD increase in instability; 1·11, 1·09-1·12 for every SD increase in severity; p<0·0001 for both). These associations were consistent across all diagnoses, age groups, and in both males and females, as well as in several robustness analyses, including when clinical severity and clinical instability were based on the Patient Health Questionnaire-9 rather than Clinical Global Impression Severity measurements. Patients in the top half of the cohort for both clinical severity and instability were at an increased risk of hospitalisation compared with those in the bottom half along both dimensions (HR 1·45, 95% CI 1·39-1·52; p<0·0001).

INTERPRETATION

Clinical instability and severity are independent predictors of future risk of hospitalisation, across diagnoses, age groups, and in both males and females. These findings could help clinicians make prognoses and screen patients who are most likely to benefit from intensive interventions, as well as help health-care providers plan service provisions by adding additional detail to risk prediction tools that incorporate other risk factors.

FUNDING

National Institute for Health and Care Research, National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Medical Research Council, Academy of Medical Sciences, and Holmusk.

Mundo de Pólus serious game for people with bipolar disorder

BACKGROUND

Serious games are play-based technologies designed to teach users a wide range of concepts and skills applicable in the non-virtual environment.

OBJECTIVES

This paper describes the process of developing a serious game for people with bipolar disorder to promote symptom recognition and the safe use of medications.

METHODS

This study was based on the User-Centered Design methodological model and the theoretical framework for Participatory Design. We conducted interviews with health professionals and discussion circles with people with bipolar disorder and their family members in order to identify the learning needs related to symptom recognition and safe medication use. A categorical analysis was completed of the participants' reports and the scientific literature and formed the basis for the design of Mundo de Pólus.

RESULTS

The game development process had three pillars (detailed in this manuscript): missions, simulation, and journal. The serious game focuses on the users' perceptions about their experience with the disorder, their interpersonal relationships, coping strategies, use of medications, and non-pharmacological treatments.

CONCLUSIONS

These scientific and technological outcomes are useful to promote literacy and safety in medication therapy for people with bipolar disorder.

Online behavioural activation during the COVID-19 pandemic decreases depression and negative affective bias

BACKGROUND

The COVID-19 pandemic highlighted the need for mental health interventions that can be easily disseminated during a crisis. Behavioural activation (BA) is a cost-effective treatment that can be administered by non-specialists; however, it is unclear whether it is still effective during a time of lockdown and social distancing, when opportunities for positive activity are significantly constrained.

METHODS

Between May and October 2020, we randomised 68 UK participants with mild to moderate low mood to either a 4-week online programme of non-specialist administered BA or to a passive control group. Before and after the intervention, we collected self-report data on mood and COVID-related disruption, as well as measuring emotional cognition as an objective marker of risk for depression.

RESULTS

In comparison to the control group, the BA group showed a significant decrease in depression, anxiety and anhedonia after the intervention, as well as an increase in self-reported activation and social support. Benefits persisted at 1-month follow-up. BA also decreased negative affective bias on several measures of the Facial Emotion Recognition Task and early change in bias was associated with later therapeutic gain. Participants rated the intervention as highly acceptable.

CONCLUSION

This study highlights the benefits of online BA that can be administered by non-specialists after brief training. These findings can help inform the policy response towards the rising incidence of mental health problems during a crisis situation such as a pandemic. They also highlight the use of objective cognitive markers of risk across different treatment modalities.

Novel digital methods for gathering intensive time series data in mental health research: scoping review of a rapidly evolving field

Recent technological advances enable the collection of intensive longitudinal data. This scoping review aimed to provide an overview of methods for collecting intensive time series data in mental health research as well as basic principles, current applications, target constructs, and statistical methods for this type of data.In January 2021, the database MEDLINE was searched. Original articles were identified that (1) used active or passive data collection methods to gather intensive longitudinal data in daily life, (2) had a minimum sample size of N ⩾ 100 participants, and (3) included individuals with subclinical or clinical mental health problems.In total, 3799 original articles were identified, of which 174 met inclusion criteria. The most widely used methods were diary techniques (e.g. Experience Sampling Methodology), various types of sensors (e.g. accelerometer), and app usage data. Target constructs included affect, various symptom domains, cognitive processes, sleep, dysfunctional behaviour, physical activity, and social media use. There was strong evidence on feasibility of, and high compliance with, active and passive data collection methods in diverse clinical settings and groups. Study designs, sampling schedules, and measures varied considerably across studies, limiting the generalisability of findings.Gathering intensive longitudinal data has significant potential to advance mental health research. However, more methodological research is required to establish and meet critical quality standards in this rapidly evolving field. Advanced approaches such as digital phenotyping, ecological momentary interventions, and machine-learning methods will be required to efficiently use intensive longitudinal data and deliver personalised digital interventions and services for improving public mental health.

Emotions as computations

Emotions ubiquitously impact action, learning, and perception, yet their essence and role remain widely debated. Computational accounts of emotion aspire to answer these questions with greater conceptual precision informed by normative principles and neurobiological data. We examine recent progress in this regard and find that emotions may implement three classes of computations, which serve to evaluate states, actions, and uncertain prospects. For each of these, we use the formalism of reinforcement learning to offer a new formulation that better accounts for existing evidence. We then consider how these distinct computations may map onto distinct emotions and moods. Integrating extensive research on the causes and consequences of different emotions suggests a parsimonious one-to-one mapping, according to which emotions are integral to how we evaluate outcomes (pleasure & pain), learn to predict them (happiness & sadness), use them to inform our (frustration & content) and others' (anger & gratitude) actions, and plan in order to realize (desire & hope) or avoid (fear & anxiety) uncertain outcomes.

Measuring affect dynamics: An empirical framework

A fast-growing body of evidence from experience sampling studies suggests that affect dynamics are associated with well-being and health. But heterogeneity in experience sampling approaches impedes reproducibility and scientific progress. Leveraging a large dataset of 7016 individuals, each providing over 50 affect reports, we introduce an empirically derived framework to help researchers design well-powered and efficient experience sampling studies. Our research reveals three general principles. First, a sample of 200 participants and 20 observations per person yields sufficient power to detect medium-sized associations for most affect dynamic measures. Second, for trait- and time-independent variability measures of affect (e.g., SD), distant sampling study designs (i.e., a few daily measurements spread out over several weeks) lead to more accurate estimates than close sampling study designs (i.e., many daily measurements concentrated over a few days), although differences in accuracy across sampling methods were inconsistent and of little practical significance for temporally dependent affect dynamic measures (i.e., RMSSD, autocorrelation coefficient, TKEO, and PAC). Third, across all affect dynamics measures, sampling exclusively on specific days or time windows leads to little to no improvement over sampling at random times. Because the ideal sampling approach varies for each affect dynamics measure, we provide a companion R package, an online calculator ( https://sergiopirla.shinyapps.io/powerADapp ), and a series of benchmark effect sizes to help researchers address three fundamental hows of experience sampling: How many participants to recruit? How often to solicit them? And for how long?

Identifying psychiatric diagnosis from missing mood data through the use of log-signature features

The availability of mobile technologies has enabled the efficient collection of prospective longitudinal, ecologically valid self-reported clinical questionnaires from people with psychiatric diagnoses. These data streams have potential for improving the efficiency and accuracy of psychiatric diagnosis as well predicting future mood states enabling earlier intervention. However, missing responses are common in such datasets and there is little consensus as to how these should be dealt with in practice. In this study, the missing-response-incorporated log-signature method achieves roughly 74.8% correct diagnosis, with f1 scores for three diagnostic groups 66% (bipolar disorder), 83% (healthy control) and 75% (borderline personality disorder) respectively. This was superior to the naive model which excluded missing data and advanced models which implemented different imputation approaches, namely, k-nearest neighbours (KNN), probabilistic principal components analysis (PPCA) and random forest-based multiple imputation by chained equations (rfMICE). The log-signature method provided an effective approach to the analysis of prospectively collected mood data where missing data was common and should be considered as an approach in other similar datasets. Because of treating missing responses as a signal, its superiority also highlights that missing data conveys valuable clinical information.

Positive moods are all alike? Differential affect amplification effects of 'elated' versus 'calm' mental imagery in young adults reporting hypomanic-like experiences

Positive mood amplification is a hallmark of the bipolar disorder spectrum (BPDS). We need better understanding of cognitive mechanisms contributing to such elevated mood. Generation of vivid, emotionally compelling mental imagery is proposed to act as an 'emotional amplifier' in BPDS. We used a positive mental imagery generation paradigm to manipulate affect in a subclinical BPDS-relevant sample reporting high (n = 31) vs. low (n = 30) hypomanic-like experiences on the Mood Disorder Questionnaire (MDQ). Participants were randomized to an 'elated' or 'calm' mental imagery condition, rating their momentary affect four times across the experimental session. We hypothesized greater affect increase in the high (vs. low) MDQ group assigned to the elated (vs. calm) imagery generation condition. We further hypothesized that affect increase in the high MDQ group would be particularly apparent in the types of affect typically associated with (hypo)mania, i.e., suggestive of high activity levels. Mixed model and time-series analysis showed that for the high MDQ group, affect increased steeply and in a sustained manner over time in the 'elated' imagery condition, and more shallowly in 'calm'. The low-MDQ group did not show this amplification effect. Analysis of affect clusters showed high-MDQ mood amplification in the 'elated' imagery condition was most pronounced for active affective states. This experimental model of BPDS-relevant mood amplification shows evidence that positive mental imagery drives changes in affect in the high MDQ group in a targeted manner. Findings inform cognitive mechanisms of mood amplification, and spotlight prevention strategies targeting elated imagery, while potentially retaining calm imagery to preserve adaptive positive emotionality.

Understanding the development of bipolar disorder and borderline personality disorder in young people: a meta-review of systematic reviews

BACKGROUND

There is ongoing debate on the nosological position of bipolar disorder (BD) and borderline personality disorder (BPD). Identifying the unique and shared risks, developmental pathways, and symptoms in emerging BD and BPD could help the field refine aetiological hypotheses and improve the prediction of the onset of these disorders. This study aimed to: (a) systematically synthesise the available evidence from systematic reviews (SRs) and meta-analyses (MAs) concerning environmental, psychosocial, biological, and clinical factors leading to the emergence of BD and BPD; (b) identify the main differences and common features between the two disorders to characterise their complex interplay and, (c) highlight remaining evidence gaps.

METHODS

Data sources were; PubMed, PsychINFO, Embase, Cochrane, CINAHL, Medline, ISI Web of Science. Overlap of included SRs/MAs was assessed using the corrected covered area process. The methodological quality of each included SR and MA was assessed using the AMSTAR.

RESULTS

22 SRs and MAs involving 249 prospective studies met eligibility criteria. Results demonstrated that family history of psychopathology, affective instability, attention deficit hyperactivity disorder, anxiety disorders, depression, sleep disturbances, substance abuse, psychotic symptoms, suicidality, childhood adversity and temperament were common predisposing factors across both disorders. There are also distinct factors specific to emerging BD or BPD.

CONCLUSIONS

Prospective studies are required to increase our understanding of the development of BD and BPD onset and their complex interplay by concurrently examining multiple measures in BD and BPD at-risk populations.

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.

Mental health prior to and during the COVID-19 pandemic in individuals with bipolar disorder: Insights from prospective longitudinal data

OBJECTIVES

Many studies have examined the impact of COVID-19 on the mental health of the public, but few have focused on individuals with existing severe mental illness with longitudinal data before and during the pandemic.

AIMS

To investigate the impact of the COVID-19 pandemic on the mental health of people with bipolar disorder (BD).

METHODS

In an ongoing study of people with BD who used an online mood monitoring tool, True Colours, 356 participants provided weekly data on their mental health. Symptoms of depression, mania, insomnia, and suicidal thoughts were compared in 2019 and 2020. From May 2020, participants also provided weekly data on the effect of the COVID-19 pandemic on anxiety, coping strategies, access to care, and medications.

RESULTS

On average, symptoms of depression, mania, insomnia, and suicidal thoughts did not significantly differ in 2020 compared to 2019, but there was evidence of heterogeneity. There were high rates of anxiety about the pandemic and its impact on coping strategies, which increased to over 70% of responders in January 2021. A significant proportion of participants reported difficulty accessing routine care (27%) and medications (21%).

CONCLUSIONS

Although mood symptoms did not significantly increase during the pandemic overall, we observed heterogeneity among our BD sample and other impacted areas. Individuals' unique histories and psychosocial circumstances are key and should be explored in future qualitative studies. The significant impacts of the pandemic may take time to manifest, particularly among those who are socioeconomically disadvantaged, highlighting the need for further long-term prospective studies.

Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment

Wrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate sample rate, (b) summarizing raw three-dimensional accelerometry data for further processing (accelerometry summary measures), and (c) accurately estimating PA levels and sleep towards understanding participants' 24-hour profiles. We used the CAPTURE-24 dataset, where 148 participants concurrently wore a wrist-worn three-dimensional accelerometer and a wearable camera over approximately 24 h to obtain minute-by-minute labels: sleep; and sedentary light, moderate, and vigorous PA. We propose a new acceleration summary measure, the Rate of Change Acceleration Movement (ROCAM), and compare its performance against three established approaches summarizing three-dimensional acceleration data towards replicating the minute-by-minute labels. Moreover, we compare findings where the acceleration data was sampled at 10, 25, 50, and 100 Hz. We demonstrate the competitive advantage of ROCAM towards estimating the five labels (80.2% accuracy) and building 24-hour profiles where the sample rate of 10 Hz is fully sufficient. Collectively, these findings provide insights facilitating the deployment of large-scale longitudinal actigraphy data processing towards 24-hour PA and sleep-profile assessment.

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.

Using a generative model of affect to characterize affective variability and its response to treatment in bipolar disorder

The affective variability of bipolar disorder (BD) is thought to qualitatively differ from that of borderline personality disorder (BPD), with changes in affect persisting longer in BD. However, quantitative studies have not been able to confirm this distinction. It has therefore not been possible to accurately quantify how treatments like lithium influence affective variability in BD. We assessed the affective variability associated with BD and BPD as well as the effect of lithium using a computational model that defines two subtypes of variability: affective changes that persist (volatility) and changes that do not (noise). We hypothesized that affective volatility would be raised in the BD group, noise would be raised in the BPD group, and that lithium would impact affective volatility. Daily affect ratings were prospectively collected for up to 3 y from patients with BD or BPD and nonclinical controls. In a separate experimental medicine study, patients with BD were randomized to receive lithium or placebo, with affect ratings collected from week -2 to +4. We found a diagnostically specific pattern of affective variability. Affective volatility was raised in patients with BD, whereas affective noise was raised in patients with BPD. Rather than suppressing affective variability, lithium increased the volatility of positive affect in both studies. These results provide a quantitative measure of the affective variability associated with BD and BPD. They suggest a mechanism of action for lithium, whereby periods of persistently low or high affect are avoided by increasing the volatility of affective responses.

Designing Tangibles to Support Emotion Logging for Older Adults: Development and Usability Study

BACKGROUND

The global population is aging, leading to shifts in health care needs. In addition to developing technology to support physical health, there is an increasing recognition of the need to consider how technology can support emotional health. This raises the question of how to design devices that older adults can interact with to log their emotions.

OBJECTIVE

We designed and developed 2 novel tangible devices, inspired by existing paper-based scales of emotions. The findings from a field trial of these devices with older adults are reported.

METHODS

Using interviews, field deployment, and fixed logging tasks, we assessed the developed devices.

RESULTS

Our results demonstrate that the tangible devices provided data comparable with standardized psychological scales of emotion. The participants developed their own patterns of use around the devices, and their experience of using the devices uncovered a variety of design considerations. We discuss the difficulty of customizing devices for specific user needs while logging data comparable to psychological scales of emotion. We also highlight the value of reflecting on sparse emotional data.

CONCLUSIONS

Our work demonstrates the potential for tangible emotional logging devices. It also supports further research on whether such devices can support the emotional health of older adults by encouraging reflection of their emotional state.

Internet-delivered interventions for personality disorders - A scoping review

BACKGROUND

Personality disorders (PDs) form a substantial part of the mental health disease burden. Effective therapies to treat PDs exist, but they are time-consuming, costly, and difficult to scale up. Delivery through the internet could facilitate the scalability of effective treatment methods.

OBJECTIVE

This review summarizes existing evidence on internet-delivered psychotherapy for personality disorders.

METHODS

Because few randomized controlled trials (RCTs) have been carried out, we conducted a scoping review. We performed a systematic literature search in PubMed, Embase, MEDLINE, CINAHL, PsycInfo, and Cochrane. Studies were selected if they conveyed research findings on internet-delivered PD interventions.

RESULTS

Eleven studies were included. The majority (n = 8) focused specifically on borderline personality disorder (BPD) and the other three on PD in general. The most frequently used form of intervention (n = 7) was the addition of a mobile app to a conventional evidence-based face-to-face treatment such as dialectical behavioral therapy (DBT). Most interventions (n = 8) were still in the development and piloting phase; only two RCTs were found. Usability and patient satisfaction were moderate to high in all studies. Three studies demonstrated significant decreases in borderline personality disorder symptoms.The majority of the studies found were pilot or feasibility studies, most involving mobile apps offered in addition to face-to-face treatment. The add-ons were rated feasible, acceptable, and useful by patients. Reported challenges involved technical difficulties such as programming errors and bugs. Only 45% of the included studies reported on changes in PD symptoms, all showing reduction of symptoms and absence of adverse effects.

CONCLUSIONS

This scoping review found that internet interventions for PD are still under-researched, although initial outcomes show promise. The outcomes also encourage future research in terms of developing internet interventions as an add-on to existing treatments, as well as working toward the creation and testing of more encompassing internet-delivered treatments for PD.

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.

iHealth: The ethics of artificial intelligence and big data in mental healthcare

The concept of intelligent health (iHealth) in mental healthcare integrates artificial intelligence (AI) and Big Data analytics. This article is an attempt to outline ethical aspects linked to iHealth by focussing on three crucial elements that have been defined in the literature: self-monitoring, ecological momentary assessment (EMA), and data mining. The material for the analysis was obtained by a database search. Studies and reviews providing outcome data for each of the three elements were analyzed. An ethical framing of the results was conducted that shows the chances and challenges of iHealth. The synergy between self-monitoring, EMA, and data mining might enable the prevention of mental illness, the prediction of its onset, the personalization of treatment, and the participation of patients in the treatment process. Challenges arise when it comes to the autonomy of users, privacy and data security of users, and potential bias.

An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning With an Urban African American Population of Young Adults: User-Centered Design Approach

BACKGROUND

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are increasingly affecting younger populations, particularly African Americans in the southern United States. Access to preventive and therapeutic services, biological factors, and social determinants of health (ie, structural racism, resource limitation, residential segregation, and discriminatory practices) all combine to exacerbate health inequities and their resultant disparities in morbidity and mortality. These factors manifest early in life and have been shown to impact health trajectories into adulthood. Early detection of and intervention in emerging risk offers the best hope for preventing race-based differences in adult diseases. However, young-adult populations are notoriously difficult to recruit and retain, often because of a lack of knowledge of personal risk and a low level of concern for long-term health outcomes.

OBJECTIVE

This study aims to develop a system design for the MOYO mobile platform. Further, we seek to addresses the challenge of primordial prevention in a young, at-risk population (ie, Southern-urban African Americans).

METHODS

Urban African Americans, aged 18 to 29 years (n=505), participated in a series of co-design sessions to develop MOYO prototypes (ie, HealthTech Events). During the sessions, participants were orientated to the issues of CVD risk health disparities and then tasked with wireframing prototype screens depicting app features that they considered desirable. All 297 prototype screens were subsequently analyzed using NVivo 12 (QSR International), a qualitative analysis software. Using the grounded theory approach, an open-coding method was applied to a subset of data, approximately 20% (5/25), or 5 complete prototypes, to identify the dominant themes among the prototypes. To ensure intercoder reliability, 2 research team members analyzed the same subset of data.

RESULTS

Overall, 9 dominant design requirements emerged from the qualitative analysis: customization, incentive motivation, social engagement, awareness, education, or recommendations, behavior tracking, location services, access to health professionals, data user agreements, and health assessment. This led to the development of a cross-platform app through an agile design process to collect standardized health surveys, narratives, geolocated pollution, weather, food desert exposure data, physical activity, social networks, and physiology through point-of-care devices. A Health Insurance Portability and Accountability Act-compliant cloud infrastructure was developed to collect, process, and review data, as well as generate alerts to allow automated signal processing and machine learning on the data to produce critical alerts. Integration with wearables and electronic health records via fast health care interoperability resources was implemented.

CONCLUSIONS

The MOYO mobile platform provides a comprehensive health and exposure monitoring system that allows for a broad range of compliance, from passive background monitoring to active self-reporting. These study findings support the notion that African Americans should be meaningfully involved in designing technologies that are developed to improve CVD outcomes in African American communities.

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review

BACKGROUND

The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The application of models of involvement, such as user-centered design, is becoming standardized to optimize the reach, adoption, and sustained use of this type of technology.

OBJECTIVE

This paper aims to examine the current ways in which users are involved in the design and evaluation of self-monitoring apps for BD by investigating 3 specific questions: are users involved in the design and evaluation of technology? If so, how does this happen? And what are the best practice ingredients regarding the design of mental health technology?

METHODS

We reviewed the available literature on self-tracking technology for BD and make an overall assessment of the level of user involvement in design. The findings were reviewed by an expert panel, including an individual with lived experience of BD, to form best practice ingredients for the design of mental health technology. This combines the existing practices of patient and public involvement and HCI to evolve from the generic guidelines of user-centered design and to those that are tailored toward mental health technology.

RESULTS

For the first question, it was found that out of the 11 novel smartphone apps included in this review, 4 (36%) self-monitoring apps were classified as having no mention of user involvement in design, 1 (9%) self-monitoring app was classified as having low user involvement, 4 (36%) self-monitoring apps were classified as having medium user involvement, and 2 (18%) self-monitoring apps were classified as having high user involvement. For the second question, it was found that despite the presence of extant approaches for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved, and to what extent there is a reported emphasis on the voice of the user, which is the ultimate aim of such design approaches. For the third question, it is recommended that users are involved in all stages of design with the ultimate goal of empowering and creating empathy for the user.

CONCLUSIONS

Users should be involved early in the design process, and this should not just be limited to the design itself, but also to associated research ensuring end-to-end involvement. Communities in health care-based design and HCI design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further.

Development of a conceptual model for research on cyclical variation of patient reported outcome measurements (PROMs) in patients with chronic conditions: a scoping review

Background

Although circadian, seasonal, and other cycles have been observed for a number of chronic conditions, their impact on patient reported outcomes measurements (PROMs) has not been systematically explored, rendering our understanding of the effect of time of measurement on PROM scores very limited. The aim was to conduct a scoping review to determine what is known about how intra-individual cyclical variation might affect the way individuals with chronic conditions respond to patient-reported outcome measures.

Methods

A protocol of a systematic scoping review was registered on PROSPERO (CRD42017058365). We developed a search strategy based on previous relevant reviews and implemented it in: MEDLINE, Embase, PsycINFO, and CINAHL. No restrictions were placed on article types and backward and forward citation searches were conducted. Screening and data extraction were independently completed by up to four reviewers. An adapted version of CASP criteria was used to appraise the quality of included articles. Concepts that were important in understanding the impact of cyclical variation on PROM scores were elicited from the papers and iteratively refined through discussion amongst the authors.

Results

2420 references resulted from the searches, with 33 articles meeting the inclusion criteria. Most study designs included observational research (particularly ecological momentary assessment), 2 were RCTs and 2 were systematic reviews. Studies mainly focused on specific health conditions: mental health, respiratory and musculoskeletal. There was a lack of qualitative research and theoretical framework to explore these concepts more fully. Five overarching concepts emerged: variation in outcomes, variation of scores, psychological status, individual factors, and environmental/situational factors. A conceptual model was developed outlining the relationships between these concepts.

Conclusions

There is empirical evidence that supports cyclical variation in PROM scores across different chronic conditions, with potential very significant implications for administration and interpretation of PROMs. The proposed conceptual model can support further research in this area.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-021-00395-x.

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study

BACKGROUND

Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care.

OBJECTIVE

In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire-a 10-item, mobile app-based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).

METHODS

The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.

RESULTS

A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).

CONCLUSIONS

The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

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.

The Emerging Circadian Phenotype of Borderline Personality Disorder: Mechanisms, Opportunities and Future Directions

PURPOSE OF REVIEW

We review the recent evidence suggesting that circadian rhythm disturbance is a common unaddressed feature of borderline personality disorder (BPD); amelioration of which may confer substantial clinical benefit. We assess chronobiological BPD studies from a mechanistic and translational perspective and highlight opportunities for the future development of this hypothesis.

RECENT FINDINGS

The emerging circadian phenotype of BPD is characterised by a preponderance of comorbid circadian rhythm sleep-wake disorders, phase delayed and misaligned rest-activity patterns and attenuated amplitudes of usually well-characterised circadian rhythms. Such disturbances may exacerbate symptom severity, and specific maladaptive personality dimensions may produce a liability towards extremes in chronotype. Pilot studies suggest intervention may be beneficial, but development is limited. Endogenous and exogenous circadian rhythm disturbances appear to be common in BPD. The interface between psychiatry and chronobiology has led previously to novel efficacious strategies for the treatment of psychiatric disorders. We believe that better characterisation of the circadian phenotype in BPD will lead to a directed biological target for treatment in a condition where there is a regrettable paucity of accessible therapies.

Blood pressure in bipolar disorder: evidence of elevated pulse pressure and associations between mean pressure and mood instability

BACKGROUND

Bipolar disorder (BD) is associated with excess and premature cardiovascular mortality. Elevated blood pressure (BP) is a leading contributor to cardiovascular risk. However, few studies have examined BP in BD in comparison to other psychiatric disorders. Furthermore, the association between BP and mood instability is not presently clear despite increasing interest in repurposing existing antihypertensive medications as possible novel BD treatments. Thus we examined BP differences between BD and borderline personality disorder (BPD), a disorder with a similar symptom profile through chronic mood instability.

METHODS

A total of 106 adults (38 BD, 25 BPD, and 43 healthy controls), evaluated in the Automated Monitoring of Symptom Severity (AMoSS) study, completed a week-long home blood pressure monitoring assessment and ecological momentary assessment of mood. We examined group-wise differences in mean BP and BP variability and their association with mood instability.

RESULTS

BD individuals had a significantly wider resting pulse pressure (40.8 ± 7.4, mmHg) compared to BPD (35.7 ± 5.3, mmHg, P = 0.03) and control participants (37.3 ± 6.3, mmHg, P = 0.036). Systolic BP was negatively associated with sad mood instability, and all measures of mean BP (systolic, diastolic, and mean arterial pressure) were negatively associated with positive mood instability.

CONCLUSIONS

This study demonstrates BP differences between BD and healthy and clinical controls that are within a normotensive range. Early pulse pressure widening may be a modifiable pathophysiological feature of BD that confers later cardiovascular risk. BP may be an important transdiagnostic predictor of mood instability and a potential explicit treatment target.

The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions

The full range of biopsychosocial complexity is mind-boggling, spanning a vast range of spatiotemporal scales with complicated vertical, horizontal, and diagonal feedback interactions between contributing systems. It is unlikely that such complexity can be dealt with by a single model. One approach is to focus on a narrower range of phenomena which involve fewer systems but still cover the range of spatiotemporal scales. The suggestion is to focus on the relationship between temperament in healthy individuals and mental illness, which have been conjectured to lie along a continuum of neurobehavioral regulation involving neurochemical regulatory systems (e.g., monoamine and acetylcholine, opiate receptors, neuropeptides, oxytocin), and cortical regulatory systems (e.g., prefrontal, limbic). Temperament and mental illness are quintessentially dynamical phenomena, and need to be addressed in dynamical terms. A meteorological metaphor suggests similarities between temperament and chronic mental illness and climate, between individual behaviors and weather, and acute mental illness and frontal weather events. The transition from normative temperament to chronic mental illness is analogous to climate change. This leads to the conjecture that temperament and chronic mental illness describe distinct, high level, dynamical phases. This suggests approaching biopsychosocial complexity through the study of dynamical phases, their order and control parameters, and their phase transitions. Unlike transitions in physical systems, these biopsychosocial phase transitions involve information and semiotics. The application of complex adaptive dynamical systems theory has led to a host of markers including geometrical markers (periodicity, intermittency, recurrence, chaos) and analytical markers such as fluctuation spectroscopy, scaling, entropy, recurrence time. Clinically accessible biomarkers, in particular heart rate variability and activity markers have been suggested to distinguish these dynamical phases and to signal the presence of transitional states. A particular formal model of these dynamical phases will be presented based upon the process algebra, which has been used to model information flow in complex systems. In particular it describes the dual influences of energy and information on the dynamics of complex systems. The process algebra model is well-suited for dealing with the particular dynamical features of the continuum, which include transience, contextuality, and emergence. These dynamical phases will be described using the process algebra model and implications for clinical practice will be discussed.

Evaluation einer Mehrfamiliengruppe für Patient_innen mit einer Borderline-Persönlichkeitsstörung und ihre Angehörigen

Zusammenfassung. Hintergrund. Familiäre Konflikte und dysfunktionale Verhaltensweisen spielen eine entscheidende Rolle bei der Entstehung und Aufrechterthaltung von Borderline-Persönlichkeitsstörungen (BPS). Auf der Grundlage der Dialektisch-Behavioralen Therapie (DBT) berücksichtigte Alan Fruzzetti die genannten Faktoren in einem transaktionalen Entstehungmodell. Um BPS-Betroffenen und deren Angehörigen Strategien für eine funktionalere Emotions- und Interaktionsbewältigung an die Hand zu geben, entwickelte Alan Fruzzetti DBT-Familien-Skills. Fragestellung. Die Wirksamkeit dieser Fertigkeiten als therapeutische Intervention im Rahmen eines Mehrfamiliensettings wurde im Rahmen der vorliegenden Pilotstudie evaluiert. Methode. Mittels Tagebuch- und Sitzungsabfragen wurden prozessuale Daten der psychischen Beanspruchung und Anwendungshäufigkeit von DBT-Familien-Skills (AFS) erhoben. Ergebnisse. Via Trendanalysen konnten Hinweise auf hypothesenkonforme Beanspruchungsreduktionen und eine Zunahme der AFS über den zeitlichen Gruppenverlauf bei BPS-Betroffenen und Angehörigen identifiziert werden. Schlussfolgerung. Die Ergebnisse der Pilotstudie liefern erste Hinweise, dass die in einem Mehrfamilienformat angebotenen DBT-Familien-Skills zu einer Reduktion der psychischen Beanspruchung der BPS-Betroffenen und Angehörigen beitragen.

Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations

Background: Remote monitoring and digital phenotyping harbor potential to aid clinical diagnosis, predict episode course and recognize early signs of mental health crises. Digital communication metrics, such as phone call and short message service (SMS) use may represent novel biomarkers of mood and diagnosis in Bipolar Disorder (BD) and Borderline Personality Disorder (BPD). Materials and Methods: BD (n = 17), BPD (n = 17) and Healthy Control (HC, n = 21) participants used a smartphone application which monitored phone calls and SMS messaging, alongside self-reported mood. Linear mixed-effects regression models were used to assess the association between digital communications and mood symptoms, mood state, trait-impulsivity, diagnosis and the interaction effect between mood and diagnosis. Results: Transdiagnostically, self-rated manic symptoms and manic state were positively associated with total and outgoing call frequency and cumulative total, incoming and outgoing call duration. Manic symptoms were also associated with total and outgoing SMS frequency. Transdiagnostic depressive symptoms were associated with increased mean incoming call duration. For the different diagnostic groups, BD was associated with increased total call frequency and BPD with increased total and outgoing SMS frequency and length compared to HC. Depression in BD, but not BPD, was associated with decreased total and outgoing call frequency, mean total and outgoing call duration and total and outgoing SMS frequency. Finally, trait-impulsivity was positively associated with total call frequency, total and outgoing SMS frequency and cumulative total and outgoing SMS length. Conclusion: These results identify a general increase in phone call and SMS communications associated with self-reported manic symptoms and a diagnosis-moderated decrease in communications associated with depression in BD, but not BPD, participants. These findings may inform the development of clinical tools to aid diagnosis and remote symptom monitoring, as well as informing understanding of differential psychopathologies in BD and BPD.

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

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

Clinical Features, Neuropsychology and Neuroimaging in Bipolar and Borderline Personality Disorder: A Systematic Review of Cross-Diagnostic Studies

Background: Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) have clinically been evolving as separate disorders, though there is still debate on the nosological valence of both conditions, their interaction in terms of co-morbidity or disorder spectrum and their distinct pathophysiology. Objective: The objective of this review is to summarize evidence regarding clinical features, neuropsychological performance and neuroimaging findings from cross-diagnostic studies comparing BD and BPD, to further caracterize their complex interplay. Methods: Using PubMed, PsycINFO and TripDataBase, we conducted a systematic literature search based on PRISMA guidelines of studies published from January 1980 to September 2019 which directly compared BD and BPD. Results: A total of 28 studies comparing BD and BPD were included: 19 compared clinical features, 6 neuropsychological performance and three neuroimaging abnormalities. Depressive symptoms have an earlier onset in BPD than BD. BD patients present more mixed or manic symptoms, with BD-I differing from BPD in manic phases. BPD patients show more negative attitudes toward others and self, more conflictive interpersonal relationships, and more maladaptive regulation strategies in affective instability with separate pathways. Impulsivity seems more a trait in BPD rather than a state as in BD. Otherwise, BD and BPD overlap in depressive and anxious symptoms, dysphoria, various abnormal temperamental traits, suicidal ideation, and childhood trauma. Both disorders differ and share deficits in neuropsychological and neuroimaging findings. Conclusion: Clinical data provide evidence of overlapping features in both disorders, with most of those shared symptoms being more persistent and intense in BPD. Thus, categorical classifications should be compared to dimensional approaches in transdiagnostic studies investigating BPD features in BD regarding their respective explanatory power for individual trajectories. Systematic Review Registration: The search strategy was pre-registered in PROSPERO: CRD42018100268.

Assessment of Variability in Irregularly Sampled Time Series: Applications to Mental Healthcare

Variability is defined as the propensity at which a given signal is likely to change. There are many choices for measuring variability, and it is not generally known which ones offer better properties. This paper compares different variability metrics applied to irregularly (nonuniformly) sampled time series, which have important clinical applications, particularly in mental healthcare. Using both synthetic and real patient data, we identify the most robust and interpretable variability measures out of a set 21 candidates. Some of these candidates are also proposed in this work based on the absolute slopes of the time series. An additional synthetic data experiment shows that when the complete time series is unknown, as it happens with real data, a non-negligible bias that favors normalized and/or metrics based on the raw observations of the series appears. Therefore, only the results of the synthetic experiments, which have access to the full series, should be used to draw conclusions. Accordingly, the median absolute deviation of the absolute value of the successive slopes of the data is the best way of measuring variability for this kind of time series.

Recommendations for the use of long-term experience sampling in bipolar disorder care: a qualitative study of patient and clinician experiences

Background

Self-monitoring has been shown to improve the self-management and treatment of patients with bipolar disorder. However, current self-monitoring methods are limited to once-daily retrospectively assessed mood, which may not suit the rapid mood fluctuations in bipolar disorder. The experience sampling method (ESM), which assesses mood in real-time several times a day, may overcome these limitations. This study set out to assess the experiences of patients and clinicians with the addition of ESM monitoring, real-time alerts, and personalized feedback to clinical care. Participants were twenty patients with bipolar disorder type I/II and their clinicians. For four months, patients completed five ESM assessments per day on mood, symptoms, and activities. Weekly symptom questionnaires alerted patients and clinicians to potential episodes. After the monitoring, a personalized feedback report based on the patient’s data was discussed between patient and clinician. Three months later, patient and clinician were both interviewed.

Results

Thematic analysis of the transcripts resulted in four themes: perceived effects of the monitoring, alerts, and feedback, and recommendations for implementation of ESM. ESM was perceived as helping patients to cope better with their disorder by increasing awareness, offering new insights, and encouraging life style adjustments. ESM was further believed to facilitate communication between patient and clinician and to lead to new treatment directions. However, high assessment burden and pre-occupation with negative mood and having a disorder were also described. Patients and clinicians advocated for increased personalization and embedding of ESM in care.

Conclusions

This study demonstrates that long-term ESM monitoring, alerts, and personalized feedback are perceived as beneficial to the treatment and self-management of patients with bipolar disorder. Future research should further test the clinical utility of ESM. Clinically relevant feedback and technology need to be developed to enable personalized integration of ESM in clinical care.

Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals

BACKGROUND

In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC).

METHODS

A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from -3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales.

RESULTS

(1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity.

CONCLUSION

Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262.

Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Are existing self-ratings of acute manic symptoms in adults reliable and valid?-A systematic review

BACKGROUND

Depression research historically uses both self- and clinician ratings of symptoms with significant and substantial correlations. It is often assumed that manic patients lack insight and cannot accurately report their symptoms. This delayed the development of self-rating scales for mania, but several scales now exist and are used in research. Our objective is to systematically review the literature to identify existing self-ratings of symptoms of (hypo)mania and to evaluate their psychometric properties.

METHODS

PubMed, Web of Knowledge, and Ovid were searched up until June 2018 using the keywords: "(hypo)mania," "self-report," and "mood disorder" to identify papers which included data on the validity and reliability of self-rating scales for (hypo)mania in samples including patients with bipolar disorder.

RESULTS

We identified 55 papers reporting on 16 different self-rating scales claiming to assess (hypo)manic symptoms or states. This included single item scales, but also some with over 40 items. Three of the scales, the Internal State Scale (ISS), Altman Self-Rating Mania Scale (ASRM), and Self-Report Manic Inventory (SRMI), provided data about reliability and/or validity in more than three independent studies. Validity was mostly assessed by comparing group means from individuals in different mood states and sometimes by correlation to clinician ratings of mania.

CONCLUSIONS

ASRM, ISS, and SRMI are promising self-rating tools for (hypo)mania to be used in clinical contexts. Future studies are, however, needed to further validate these measures; for example, their associations between each other and sensitivity to change, especially if they are meant to be outcome measures in studies.

Toi Même, a Mobile Health Platform for Measuring Bipolar Illness Activity: Protocol for a Feasibility Study

BACKGROUND

The diagnosis and management of bipolar disorder are limited by the absence of available biomarkers. Patients with bipolar disorder frequently present with mood instability even during remission, which is likely associated with the risk of relapse, impaired functioning, and suicidal behavior, indicating that the illness is active.

OBJECTIVE

This research protocol aimed to investigate the correlations between clinically rated mood symptoms and mood/behavioral data automatically collected using the Toi Même app in patients with bipolar disorder presenting with different mood episodes. This study also aimed to assess the feasibility of this app for self-monitoring subjective and objective mood/behavior parameters in those patients.

METHODS

This open-label, nonrandomized trial will enroll 93 (31 depressive, 31 euthymic, and 31 hypomanic) adults diagnosed with bipolar disorder type I/II (Diagnostic and Statistical Manual of Mental Disorders, 5th edition criteria) and owning an iPhone. Clinical evaluations will be performed by psychiatrists at the baseline and after 2 weeks, 1 month, 2 months, and 3 months during the follow-up. Rather than only accessing the daily mood symptoms, the Toi Même app also integrates ecological momentary assessments through 2 gamified tests to assess cognition speed (QUiCKBRAIN) and affective responses (PLAYiMOTIONS) in real-life contexts, continuously measures daily motor activities (eg, number of steps, distance) using the smartphone's motion sensors, and performs a comprehensive weekly assessment.

RESULTS

Recruitment began in April 2018 and the completion of the study is estimated to be in December 2021. As of April 2019, 25 participants were enrolled in the study. The first results are expected to be submitted for publication in 2020. This project has been funded by the Perception and Memory Unit of the Pasteur Institute (Paris) and it has received the final ethical/research approvals in April 2018 (ID-RCB: 2017-A02450-53).

CONCLUSIONS

Our results will add to the evidence of exploring other alternatives toward a more integrated approach in the management of bipolar disorder, including digital phenotyping, to develop an ethical and clinically meaningful framework for investigating, diagnosing, and treating individuals at risk of developing bipolar disorder or currently experiencing bipolar disorder. Further prospective studies on the validity of automatically generated smartphone data are needed for better understanding the longitudinal pattern of mood instability in bipolar disorder as well as to establish the reliability, efficacy, and cost-effectiveness of such an app intervention for patients with bipolar disorder.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03508427; https://clinicaltrials.gov/ct2/show/NCT03508427.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/18818.

Preventing Cardiovascular Disease Among Urban African Americans With a Mobile Health App (the MOYO App): Protocol for a Usability Study

BACKGROUND

Cardiovascular disease (CVD) disparities are a particularly devastating manifestation of health inequity. Despite advancements in prevention and treatment, CVD is still the leading cause of death in the United States. Additionally, research indicates that African American (AA) and other ethnic-minority populations are affected by CVD at earlier ages than white Americans. Given that AAs are the fastest-growing population of smartphone owners and users, mobile health (mHealth) technologies offer the unparalleled potential to prevent or improve self-management of chronic disease among this population.

OBJECTIVE

To address the unmet need for culturally tailored primordial prevention CVD-focused mHealth interventions, the MOYO app was cocreated with the involvement of young people from this priority community. The overall project aims to develop and evaluate the effectiveness of a novel smartphone app designed to reduce CVD risk factors among urban-AAs, 18-29 years of age.

METHODS

The theoretical underpinning will combine the principles of community-based participatory research and the agile software development framework. The primary outcome goals of the study will be to determine the usability, acceptability, and functionality of the MOYO app, and to build a cloud-based data collection infrastructure suitable for digital epidemiology in a disparity population. Changes in health-related parameters over a 24-week period as determined by both passive (eg, physical activity levels, sleep duration, social networking) and active (eg, use of mood measures, surveys, uploading pictures of meals and blood pressure readings) measures will be the secondary outcome. Participants will be recruited from a majority AA "large city" school district, 2 historically black colleges or universities, and 1 urban undergraduate college. Following baseline screening for inclusion (administered in person), participants will receive the beta version of the MOYO app. Participants will be monitored during a 24-week pilot period. Analyses of varying data including social network dynamics, standard metrics of activity, percentage of time away from a given radius of home, circadian rhythm metrics, and proxies for sleep will be performed. Together with external variables (eg, weather, pollution, and socioeconomic indicators such as food access), these metrics will be used to train machine-learning frameworks to regress them on the self-reported quality of life indicators.

RESULTS

This 5-year study (2015-2020) is currently in the implementation phase. We believe that MOYO can build upon findings of classical epidemiology and longitudinal studies like the Jackson Heart Study by adding greater granularity to our knowledge of the exposures and behaviors that affect health and disease, and creating a channel for outreach capable of launching interventions, clinical trials, and enhancements of health literacy.

CONCLUSIONS

The results of this pilot will provide valuable information about community cocreation of mHealth programs, efficacious design features, and essential infrastructure for digital epidemiology among young AA adults.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16699.