State-related differences in heart rate variability in bipolar disorder

Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology

BACKGROUND

Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients.

METHODS

We analyzed data from 54 adults with BD, who wore Fitbits and completed bi-weekly self-report measures for 9 months. We applied machine learning (ML) models to Fitbit data aggregated over two-week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two-week windows with scores above established clinical cutoffs for the Patient Health Questionnaire-8 (PHQ-8) and Altman Self-Rating Mania Scale (ASRM) respectively.

RESULTS

As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC-AUC) in the validation process. In the testing set, the ROC-AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%).

CONCLUSION

We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.

A Bayesian analysis of heart rate variability changes over acute episodes of bipolar disorder

Bipolar disorder (BD) involves autonomic nervous system dysfunction, detectable through heart rate variability (HRV). HRV is a promising biomarker, but its dynamics during acute mania or depression episodes are poorly understood. Using a Bayesian approach, we developed a probabilistic model of HRV changes in BD, measured by the natural logarithm of the Root Mean Square of Successive RR interval Differences (lnRMSSD). Patients were assessed three to four times from episode onset to euthymia. Unlike previous studies, which used only two assessments, our model allowed for more accurate tracking of changes. Results showed strong evidence for a positive lnRMSSD change during symptom resolution (95.175% probability of positive direction), though the sample size limited the precision of this effect (95% Highest Density Interval [-0.0366, 0.4706], with a Region of Practical Equivalence: [-0.05; 0.05]). Episode polarity did not significantly influence lnRMSSD changes.

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

BACKGROUND

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

AIMS

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

METHOD

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

RESULTS

Recruitment is ongoing.

CONCLUSIONS

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

Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study

BACKGROUND

Personal sensing, leveraging data passively and near-continuously collected with wearables from patients in their ecological environment, is a promising paradigm to monitor mood disorders (MDs), a major determinant of the worldwide disease burden. However, collecting and annotating wearable data is resource intensive. Studies of this kind can thus typically afford to recruit only a few dozen patients. This constitutes one of the major obstacles to applying modern supervised machine learning techniques to MD detection.

OBJECTIVE

In this paper, we overcame this data bottleneck and advanced the detection of acute MD episodes from wearables' data on the back of recent advances in self-supervised learning (SSL). This approach leverages unlabeled data to learn representations during pretraining, subsequently exploited for a supervised task.

METHODS

We collected open access data sets recording with the Empatica E4 wristband spanning different, unrelated to MD monitoring, personal sensing tasks-from emotion recognition in Super Mario players to stress detection in undergraduates-and devised a preprocessing pipeline performing on-/off-body detection, sleep/wake detection, segmentation, and (optionally) feature extraction. With 161 E4-recorded subjects, we introduced E4SelfLearning, the largest-to-date open access collection, and its preprocessing pipeline. We developed a novel E4-tailored transformer (E4mer) architecture, serving as the blueprint for both SSL and fully supervised learning; we assessed whether and under which conditions self-supervised pretraining led to an improvement over fully supervised baselines (ie, the fully supervised E4mer and pre-deep learning algorithms) in detecting acute MD episodes from recording segments taken in 64 (n=32, 50%, acute, n=32, 50%, stable) patients.

RESULTS

SSL significantly outperformed fully supervised pipelines using either our novel E4mer or extreme gradient boosting (XGBoost): n=3353 (81.23%) against n=3110 (75.35%; E4mer) and n=2973 (72.02%; XGBoost) correctly classified recording segments from a total of 4128 segments. SSL performance was strongly associated with the specific surrogate task used for pretraining, as well as with unlabeled data availability.

CONCLUSIONS

We showed that SSL, a paradigm where a model is pretrained on unlabeled data with no need for human annotations before deployment on the supervised target task of interest, helps overcome the annotation bottleneck; the choice of the pretraining surrogate task and the size of unlabeled data for pretraining are key determinants of SSL success. We introduced E4mer, which can be used for SSL, and shared the E4SelfLearning collection, along with its preprocessing pipeline, which can foster and expedite future research into SSL for personal sensing.

Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data

BACKGROUND

Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD).

METHOD

We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo.

RESULTS

Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group.

CONCLUSIONS

HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.

Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Reduced heart rate variability during mania in a repeated naturalistic observational study

Background

Bipolar disorder (BD) is a chronic recurrent mood disorder associated with autonomic nervous system (ANS) dysfunction, indexed by heart rate variability (HRV). Changes in HRV between mood states are sparsely studied longitudinally. We aimed to compare HRV of hospitalized manic individuals with their own euthymic selves in a naturalistic observational study.

Methods

34 individuals were included, of which 16 were lost to follow-up. Ultimately 15 patients provided reliable heart rate data in both a manic and euthymic state, using photoplethysmography (PPG) sensor wristbands overnight. We calculated HRV measures Root Mean Square of Successive Differences (RMSSD), High-frequency (HF: 0.15-0.40 Hz), Low-frequency (LF: 0.40-0.15 Hz), Very low-frequency (VLF: 0.0033-0.04 Hz), Total power and Sample Entropy in 5-min night-time resting samples. We compared HRV measures by mood state within individuals using paired t-tests and linear regression to control for age and sex.

Results

HRV was lower in the manic state when compared to the euthymic state for all HRV metrics (p ≤ 0.02), with large to medium effect sizes (g = 1.24 to 0.65). HRV changes were not significantly affected by age or sex.

Conclusion

This longitudinal study provides evidence of lower HRV in manic states compared to euthymia, indicating an association between ANS dysregulation and changes in bipolar mood state. This corroborates previous cross-sectional studies, although the association may be less clear or reversed in hypomanic states. Further investigation in larger longitudinal samples is warranted.

Brief Report: Changes in Nocturnal Heart Rate Variability in People Living With HIV During the First Year of Antiretroviral Therapy Compared With HIV-Uninfected Community Controls

BACKGROUND

Higher nocturnal heart rate and lower nocturnal heart rate variability (HRV) is associated with increased cardiovascular disease mortality. Longitudinal studies on nocturnal HRV in people living with HIV (PLWH) are lacking.

METHODS

We conducted a 1-year prospective cohort study of adult PLWH and HIV-uninfected community controls in northwestern Tanzania. At enrollment, we collected data on cardiovascular risk factors and tested blood samples for hemoglobin, insulin, CD4 cell count, and C-reactive protein. We measured nocturnal HRV and heart rate at baseline and first-year follow-up. Mixed effect linear regression was used to determine predictors of lower HRV.

RESULTS

Of the 111 enrolled participants (74 PLWH and 37 HIV-uninfected adults), 57.7% were female and the median age was 40 years. Over 1 year of follow-up, the average nocturnal heart rate was 4.5 beats/minute higher in PLWH ( P = 0.006). In the fully adjusted model (with age, sex, nocturnal heart rate, and diabetes), average nocturnal HRV was 10.5 milliseconds lower in PLWH compared with HIV-uninfected adults ( P = 0.03). Unlike with nocturnal heart rate, nocturnal HRV did not improve after 1 year of ART in PLWH or HIV-uninfected adults (fully adjusted change = -2.5 milliseconds, P = 0.45). Lower educational attainment, lesser pancreatic β-cell function, and anemia were associated with higher HRV.

CONCLUSIONS

Nocturnal parasympathetic nervous system function was persistently lower in PLWH compared with HIV-uninfected adults even after antiretroviral therapy initiation. Improving nocturnal autonomic nervous system function could be a target for cardiovascular disease prevention in PLWH.

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

BACKGROUND

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

OBJECTIVE

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

METHODS

We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses.

RESULTS

Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), and "motor inhibition" (NMI=0.75). EDA was associated with "aggressive behavior" (NMI=1.0) and "psychic anxiety" (NMI=0.52).

CONCLUSIONS

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

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

BACKGROUND

Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE

We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS

We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS

A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS

Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments

Cardiorespiratory fitness is an established predictor of metabolic disease and mortality. Fitness is directly measured as maximal oxygen consumption (VO2max), or indirectly assessed using heart rate responses to standard exercise tests. However, such testing is costly and burdensome because it requires specialized equipment such as treadmills and oxygen masks, limiting its utility. Modern wearables capture dynamic real-world data which could improve fitness prediction. In this work, we design algorithms and models that convert raw wearable sensor data into cardiorespiratory fitness estimates. We validate these estimates' ability to capture fitness profiles in free-living conditions using the Fenland Study (N=11,059), along with its longitudinal cohort (N = 2675), and a third external cohort using the UK Biobank Validation Study (N = 181) who underwent maximal VO2max testing, the gold standard measurement of fitness. Our results show that the combination of wearables and other biomarkers as inputs to neural networks yields a strong correlation to ground truth in a holdout sample (r = 0.82, 95CI 0.80-0.83), outperforming other approaches and models and detects fitness change over time (e.g., after 7 years). We also show how the model's latent space can be used for fitness-aware patient subtyping paving the way to scalable interventions and personalized trial recruitment. These results demonstrate the value of wearables for fitness estimation that today can be measured only with laboratory tests.

The impulsive horse: comparing genetic, physiological and behavioral indicators to that of human addiction

Stress and genotype elicit changes in impulse control in a range of species that are attributable to adaptations in both the central and peripheral nervous system. We examined aspects of this mechanism in the horse by assessing the effect of a dopamine receptor genotype (DRD4) and central dopaminergic tone (measured via spontaneous blink rate [SBR] and behavioral initiation rate [BIR]), on measures of impulsivity, compulsivity (3-choice serial reaction time task) and sympathetic/ parasympathetic system balance (heart rate variability [HRV]). Genotype did not have a significant effect on any of the parameters measured. SBR but not BIR correlated significantly with levels of impulsivity. There was no clear association of HRV parameters with either measures of central dopaminergic activity or impulsivity/compulsivity. Overall, some elements of the data suggest that the horse may be a useful animal model for assessing the genetic and environmental factors that lead to the physiological and behavioral phenotype of human addiction, particularly when considering the relationship between central dopaminergic tone and impulsivity.

Digital phenotype of mood disorders: A conceptual and critical review

BACKGROUND

Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.

OBJECTIVE

The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.

METHODS

We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.

RESULTS

Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).

CONCLUSION

The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Reduced heart rate variability is associated with higher illness burden in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is associated with premature death and ischemic heart disease is the main cause of excess mortality. Heart rate variability (HRV) predicts mortality in patients with or without cardiovascular disease. While several studies have analyzed the association between HRV and BD, none has analyzed the association of HRV with illness burden in BD.

METHODS

53 participants with BD I and II used a wearable device to assess the association between HRV and factors characterizing illness burden, including illness duration, number and type of previous episode(s), duration of the most severe episode, history of suicide attempts or psychotic symptoms during episodes, and co-morbid psychiatric disorders. We ran unadjusted models and models controlling statistically for age, sex, pharmacotherapy, baseline functional cardiovascular capacity, BMI, years of education, and marital status. We also explored the association between HRV and an overall illness burden index (IBI) integrating all these factors using a weighted geometric mean.

RESULTS

Adjusted and unadjusted models had similar results. Longer illness duration, higher number of depressive episodes, longer duration of most severe manic/hypomanic episode, co-morbid anxiety disorders, and family history of suicide were associated with reduced HRV, as was bipolar depression severity in the participants experiencing a depressive episode. Finally, a higher IBI score was associated with lower HRV.

CONCLUSIONS

High illness burden is associated with reduced HRV in BD. While the IBI needs to be validated in a larger sample, it may provide an overall measure that captures illness burden in BD.

Neurophysiological and Psychological Predictors of Social Functioning in Patients with Schizophrenia and Bipolar Disorder

OBJECTIVE

The aim of this study is to examine social functioning in patients with schizophrenia and bipolar disorder and explore the psychological and neurophysiological predictors of social functioning.

METHODS

Twenty-seven patients with schizophrenia and thirty patients with bipolar disorder, as well as twenty-five healthy controls, completed measures of social functioning (questionnaire of social functioning), neurocognition (Verbal fluency, Korean-Auditory Verbal Learning Test), and social cognition (basic empathy scale and Social Attribution Task-Multiple Choice), and the childhood trauma questionnaire (CTQ). For neurophysiological measurements, mismatch negativity and heart rate variability (HRV) were recorded from all participants. Multiple hierarchical regression was performed to explore the impact of factors on social functioning.

RESULTS

The results showed that CTQ-emotional neglect significantly predicted social functioning in schizophrenia group, while HRV-high frequency significantly predicted social functioning in bipolar disorder patients. Furthermore, emotional neglect and HRV-HF still predicted social functioning in all of the subjects after controlling for the diagnostic criteria.

CONCLUSION

Our results implicated that even though each group has different predictors of social functioning, early traumatic events and HRV could be important indicators of functional outcome irrespective of what group they are.

Heart rate variability in patients with bipolar disorder: From mania to euthymia

Bipolar Disorder (BD) is characterized by the occurrence of mania alternating with euthymia. The aim of the present study was to investigate the impact of BD on the autonomic nervous system, as indicated by heart rate variability (HRV). The study was registered in the Clinical Trials Registration (NCT01272518). Nineteen hospitalized, male patients (age: 34.0 ± 12.3 years) with type I BD were assessed during mania and at discharge on euthymia. HRV data were collected during 20- minutes in supine position at rest, on spontaneous breathing, using the Polar RS 800 CX frequencymeter. HRV measures included variables in time, frequency and non-linear domains. Psychiatric conditions were evaluated by the Mini International Neuropsychiatric Interview (MINI) and the Bech-Rafaelsen mania scale (BRMS). Time domain measures of RMSSD (Cohen's d = 0.668) and pNN50 (Cohen's d = 0.688) increased from first to second assessments. The high-frequency component (HFms2) also increased (Cohen's d = 0.586), while the LF/HF ratio decreased (Cohen's d = 0.785). Non-linear domain measures including the SD1 component (Cohen's d = 0.668), and the SD1/SD2 ratio (Cohen's d = 1.2934) extracted from the Poincare plot analysis increased from first to second assessment. The variables Lmean (Cohen's d = 0.9627), Lmax (Cohen's d = 1.2164), REC% (Cohen's d = 1.0595) and EntShannon (Cohen's d = 1.0607) were higher in mania. By contrast, ApEn (Cohen's d = 0.995) and EntSample (Cohen's d = 1.189) were less during mania, all reflecting ANS improvement. Findings are interpreted in the context of recently published models relating to neurovisceral integration across the continuum of time, and the implications for the future health and wellbeing of patients are considered.

Heart rate variability in bipolar disorder and borderline personality disorder: a clinical review

Heart rate variability (HRV) in psychiatric disorders has become an increasing area of interest in recent years following technological advances that enable non-invasive monitoring of autonomic nervous system regulation. However, the clinical interpretation of HRV features remain widely debated or unknown. Standardisation within studies of HRV in psychiatric disorders is poor, making it difficult to reproduce or build on previous work. Recently, a Guidelines for Reporting Articles on Psychiatry and Heart rate variability checklist has been proposed to address this issue. Here we assess studies of HRV in bipolar disorder and borderline personality disorder against this checklist and discuss the implication for ongoing research in this area.

Diminution of Heart Rate Variability in Bipolar Depression

Autonomic nervous system (ANS) dysregulation in depression is associated with symptoms associated with the ANS. The beat-to-beat pattern of heart rate defined as heart rate variability (HRV) provides a noninvasive portal to ANS function and has been proposed to represent a means of quantifying resting vagal tone. We quantified HRV in bipolar depressed (BDD) patients as a measure of ANS dysregulation seeking to establish HRV as a potential diagnostic and prognostic biomarker for treatment outcome. Forty-seven BDD patients were enrolled. They were randomized to receive either escitalopram-celecoxib or escitalopram-placebo over 8 weeks in a double-blind study design. Thirty-five patients completed the HRV studies. Thirty-six healthy subjects served as controls. HRV was assessed at pretreatment and end of study and compared with that of controls. HRV was quantified and corrected for artifacts using an algorithm that incorporates time and frequency domains to address non-stationarity of the beat-to-beat heart rate pattern. Baseline high frequency-HRV (i.e., respiratory sinus arrhythmia) was lower in BDD patients than controls, although the difference did not reach significance. Baseline low-frequency HRV was significantly lower in BDD patients (ln4.20) than controls (ln = 5.50) (p < 0.01). Baseline heart period was significantly shorter (i.e., faster heart rate) in BDD patients than controls. No significant change in HRV parameters were detected over the course of the study with either treatment. These findings suggest that components of HRV may be diminished in BDD patients.

[Assessment of mood disorders by passive data gathering: The concept of digital phenotype versus psychiatrist's professional culture]

OBJECTIVES

The search for objective clinical signs is a constant practitioners' and researchers' concern in psychiatry. New technologies (embedded sensors, artificial intelligence) give an easier access to untapped information such as passive data (i.e. that do not require patient intervention). The concept of "digital phenotype" is emerging in psychiatry: a psychomotor alteration translated by accelerometer's modifications contrasting with the usual functioning of the subject, or the graphorrhea of patients presenting a manic episode which is replaced by an increase of SMS sent. Our main objective is to highlight the digital phenotype of mood disorders by means of a selective review of the literature.

METHOD

We conducted a selective review of the literature by querying the PubMed database until February 2017 with the terms [Computer] [Computerized] [Machine] [Automatic] [Automated] [Heart rate variability] [HRV] [actigraphy] [actimetry] [digital] [motion] [temperature] [Mood] [Bipolar] [Depression] [Depressive]. Eight hundred and forty-nine articles were submitted for evaluation, 37 articles were included.

RESULTS

For unipolar disorders, smartphones can diagnose depression with excellent accuracy by combining GPS and call log data. Actigraphic measurements showing daytime alteration in basal function while ECG sensors assessing variation in heart rate variability (HRV) and body temperature appear to be useful tools to diagnose a depressive episode. For bipolar disorders, systems which combine several sensors are described: MONARCA, PRIORI, SIMBA and PSYCHE. All these systems combine passive and active data on smartphones. From a synthesis of these data, a digital phenotype of the disorders is proposed based on the accelerometer and the GPS, the ECG, the body temperature, the use of the smartphone and the voice. This digital phenotype thus brings into question certain clinical paradigms in which psychiatrists evolve.

CONCLUSION

All these systems can be used to computerize the clinical characteristics of the various mental states studied, sometimes with greater precision than a clinician could do. Most authors recommend the use of passive data rather than active data in the context of bipolar disorders because automatically generated data reduce biases and limit the feeling of intrusion that self-questionnaires may cause. The impact of these technologies questions the psychiatrist's professional culture, defined as a specific language and a set of common values. We address issues related to these changes. Impact on psychiatrists could be important because their unity seems to be questioned due to technologies that profoundly modify the collect and process of clinical data.

The moderating influence of heart rate variability on stressor-elicited change in pupillary and attentional indices of emotional processing: An eye-Tracking study

Low resting heart rate variability (HRV) is associated with a broad array of negative psychosocial outcomes. Recent theoretical explications of HRV suggest it is an autonomic marker of emotion regulation capacity, but limited research has examined its relationship with emotional information processing indices. The present study utilized eye-tracking methodology to test HRV's theorized role as a marker of emotion regulation capacity in a non-clinical sample. Attentional biases towards threatening, dysphoric, and positive emotional information as well as affective modulation of pupil size were assessed before and after a stress induction. Low resting HRV marginally predicted larger increases in attentional bias towards positive emotional stimuli from pre to post-stress induction and significantly predicted decreased pupil dilation to positive stimuli after the stress induction only; exploratory analyses suggested that this pattern might reflect an unsuccessful attempt at anxious mood repair. HRV was unrelated to negative emotional information processing. Findings are consistent with existing theories of HRV's psychological significance and suggest a specific association with altered positive emotional processing under acute stress.

Heart rate variability in bipolar disorder: A systematic review and meta-analysis

BACKGROUND

Heart rate variability (HRV) has been suggested reduced in bipolar disorder (BD) compared with healthy individuals (HC). This meta-analysis investigated: HRV differences in BD compared with HC, major depressive disorder or schizophrenia; HRV differences between affective states; HRV changes from mania/depression to euthymia; and HRV changes following interventions.

METHODS

A systematic review and meta-analysis reported according to the PRISMA guidelines was conducted. MEDLINE, Embase, PsycINFO, The Cochrane Library and Scopus were searched. A total of 15 articles comprising 2534 individuals were included.

RESULTS

HRV was reduced in BD compared to HC (g=-1.77, 95% CI: -2.46; -1.09, P<0.001, 10 comparisons, n=1581). More recent publication year, larger study and higher study quality were associated with a smaller difference in HRV. Large between-study heterogeneity, low study quality, and lack of consideration of confounding factors in individual studies were observed.

CONCLUSIONS

This first meta-analysis of HRV in BD suggests that HRV is reduced in BD compared to HC. Heterogeneity and methodological issues limit the evidence. Future studies employing strict methodology are warranted.