Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would be the earliest indicator of future depressive symptoms. The study was a prospective, observational, contactless study. Participants were 127 outpatients with a primary diagnosis of BD, followed up for 12.6 (5.7) [(mean (SD)] months. They wore a smart ring continuously, which monitored their daily activity and sleep parameters. Participants were also asked to complete weekly self-ratings using the Patient Health Questionnaire (PHQ-9) and Altman Self-Rating Mania Scale (ASRS) scales. Primary outcome measures were depressive symptom onset detection metrics (i.e., accuracy, sensitivity, and specificity); and detection delay (in days), compared between self-rating scales and wearable data. Depressive symptoms were labeled as two or more consecutive weeks of total PHQ-9 > 10, and data-driven symptom onsets were detected using time-frequency spectral derivative spike detection (TF-SD2). Our results showed that day-to-day variability in the number of steps anticipated the onset of depressive symptoms 7.0 (9.0) (median (IQR)) days before they occurred, significantly earlier than the early prediction window provided by deep sleep duration (median (IQR), 4.0 (5.0) days; p <.05). Taken together, our results demonstrate that changes in activity were the earliest indicator of depressive symptoms in participants with BD. Transition to dynamic representations of behavioral phenomena in psychiatry may facilitate episode forecasting and individualized preventive interventions.

• Keywords
• Activity, Bipolar disorder, Densely-sampled, Mood variability, Onset, Sleep, Wearable technology,
• Publication type
• Journal Article MeSH

INTRODUCTION: The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture of mood regulation in euthymic patients with and without a history of AIM. METHODS: Eighty-four euthymic participants were included. Participants rated their mood, anxiety and energy levels daily using an electronic (e-) visual analog scale, for a mean (SD) of 280.8(151.4) days. We analyzed their multivariate time series by computing each variable's auto-correlation, inter-variable cross-correlation, and composite multiscale entropy of mood, anxiety, and energy. Then, we compared the data features of participants with a history of AIM and those without AIM, using analysis of covariance, controlling for age, sex, and current treatment. RESULTS: Based on 18,103 daily observations, participants with AIM showed significantly stronger day-to-day auto-correlation and cross-correlation for mood, anxiety, and energy than those without AIM. The highest cross-correlation in participants with AIM was between mood and energy within the same day (median (IQR), 0.58 (0.27)). The strongest negative cross-correlation in participants with AIM was between mood and anxiety series within the same day (median (IQR), -0.52 (0.34)). CONCLUSION: Patients with a history of AIM have a different underlying mood architecture compared to those without AIM. Their mood, anxiety and energy stay the same from day-to-day; and their anxiety is negatively correlated with their mood.

• Keywords
• antidepressant‐induced mania (AIM), auto‐correlation, bipolar disorder, cross‐correlation, euthymia, mood regulation, time series analysis,
• MeSH
• Affect * drug effects   MeSH
• Antidepressive Agents * therapeutic use   adverse effects   MeSH
• Bipolar Disorder * drug therapy   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Mania * drug therapy   chemically induced   MeSH
• Psychiatric Status Rating Scales MeSH
• Anxiety drug therapy   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Names of Substances
• Antidepressive Agents * MeSH

There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data Ratio (MDR), we assessed missing self-rating data and sensor data monitoring activity and sleep. Missing data were lowest for participants in the midst of a depressive episode, intermediate for participants with subsyndromal symptoms, and highest for participants who were euthymic. Over a mean ± SD follow-up of 246 ± 181 days, missing data remained unchanged for participants whose clinical status did not change throughout the study (i.e., those who entered the study in a depressive episode and did not improve, or those who entered the study euthymic and remained euthymic). Conversely, when participants' clinical status changed during the study (e.g., those who entered the study euthymic and experienced the occurrence of a depressive episode), missing data for self-rating scales increased, but not for sensor data. Overall missing data were associated with participants' clinical status and its changes, suggesting that these are not missing at random.

• MeSH
• Bipolar Disorder * epidemiology   MeSH
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Young Adult MeSH
• Self Report MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: Predictive models for mental disorders or behaviors (e.g., suicide) have been successfully developed at the level of populations, yet current demographic and clinical variables are neither sensitive nor specific enough for making individual clinical predictions. Forecasting episodes of illness is particularly relevant in bipolar disorder (BD), a mood disorder with high recurrence, disability, and suicide rates. Thus, to understand the dynamic changes involved in episode generation in BD, we propose to extract and interpret individual illness trajectories and patterns suggestive of relapse using passive sensing, nonlinear techniques, and deep anomaly detection. Here we describe the study we have designed to test this hypothesis and the rationale for its design. METHOD: This is a protocol for a contactless cohort study in 200 adult BD patients. Participants will be followed for up to 2 years during which they will be monitored continuously using passive sensing, a wearable that collects multimodal physiological (heart rate variability) and objective (sleep, activity) data. Participants will complete (i) a comprehensive baseline assessment; (ii) weekly assessments; (iii) daily assessments using electronic rating scales. Data will be analyzed using nonlinear techniques and deep anomaly detection to forecast episodes of illness. DISCUSSION: This proposed contactless, large cohort study aims to obtain and combine high-dimensional, multimodal physiological, objective, and subjective data. Our work, by conceptualizing mood as a dynamic property of biological systems, will demonstrate the feasibility of incorporating individual variability in a model informing clinical trajectories and predicting relapse in BD.

• Keywords
• Bipolar disorder, Episode prediction, Machine learning, Wearable device,
• MeSH
• Bipolar Disorder * diagnosis   MeSH
• Adult MeSH
• Cohort Studies MeSH
• Humans MeSH
• Mood Disorders diagnosis   MeSH
• Recurrence MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH