Background: Dopamine agonists (DA) are the first line therapy for prolactinoma and symptomatic hyperprolactinemia; use as an adjuvant treatment for acromegaly and Cushing's disease is rare. Some patients develop de novo psychiatric symptoms or have exacerbation of pre-existing conditions during DA therapy. A practical, clinically sensitive depression and impulse control disorders (ICD; particularly hypersexuality and gambling disorders) detection tool is important for identifying at risk patients. The Barratt Impulsivity Scale (BIS-11) and the 9-item Patient Health Questionnaire (PHQ-9) are sensitive in identifying impulsivity and depression. Objective: Detail use of the BIS-11 and PHQ-9 as screening tools for depression and ICD in patients with pituitary disease at a high-volume academic pituitary center. Methods: DA-treated and naïve patients with pituitary disease were included. Patients with a known history of depression or psychiatric disorder were excluded. PHQ-9 standardized interpretation criteria were utilized to classify depression severity. For BIS-11, threshold was established based on previous studies. Statistical analysis was with SPSS version 25. Results: Seventy-six DA-treated and 27 naïve patients were included. Moderate and moderately severe depression were more prevalent in DA-treated patients; severe depression only found in DA-treated patients. A normal BIS-11 score was noted in 76.69%; higher scores (not significant) were noted in DA-treated patients. There was a positive correlation between higher BIS-11 and PHQ-9 scores; higher in DA-treated patients (r = 0.52, p < 0.001) than DA-naïve patients. Patients with BIS-11 scores ≥60 were younger and received lower cumulative DA doses compared to patients with BIS scores <60. There was no association between male sex and BIS-11 ≥60 and male sex did not increase the odds of increased scores (OR = 0.66, CI95% 0.25-1.76, p = 0.41). No significant difference was found for macroadenoma, prolactin levels, testosterone levels, hypogonadism, testosterone replacement in men, and increased impulsivity or depression scores. Hinojosa-Amaya et al. Depression, Impulse-Control; DA-Treated and Naïve Conclusion: Use of PHQ-9 and BIS-11 is practical for routine screening of depression and ICD during outpatient pituitary clinic visits for patients with pituitary disease both naïve to treatment and during DA therapy. We recommend close follow-up after initiation of DA therapy for younger patients, regardless of dose.

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.

<p>We recruited 53 BD participants at two Canadian academic psychiatric hospitals (the Centre for Addiction and Mental Health, Toronto; the Royal Ottawa Hospital, Ottawa) between April 2016 and December 2019. Participants were provided with a BioHarness™ 3.0 wearable physiological electronic (e-) monitoring device, which they wore continuously for 24 hours. </p> <p>Posture data were recorded in units of degrees from vertical, sampled every sec (1 Hz) with a sensitivity range of 1° to 8°, and a dynamic range of ±180°. The sensors were configured such that a posture value of -90° indicates a supine posture (i.e., lying face up), and a 90° posture indicates a prone posture (i.e., lying face down). Posture was represented as a 1 Hz-discretized single channel of angular positions of a participant’s chest over the course of 24 hours. </p> <p>We extracted a set of 9 time-domain features to characterize postural dynamics in terms of amplitude, energy, variability, and transitions for 3 different periods: day (from 7:00 AM to 2:59 PM), evening (from 3:00 PM to 10:59 PM), and night (from 11:00 PM to 6:59 AM). To assess posture amplitude, we computed the mean posture (angle in degrees) and its range; to assess posture dynamics’ energy content, we computed the root mean squared (RMS) value; to assess posture variability, we computed the coefficient of variation (CV), interquartile range (IQR), and median absolute deviation (MAD). Kurtosis and skewness were computed to assess the postural statistical distribution in terms of distribution sharpness and symmetry. Lastly, the number of postural transitions was computed using Bayesian Online Changepoint Detection (BOCD) which identifies the abrupt changes in sequential data generative parameters, such that each changepoint is indicative of a postural transition (e.g., being upright to bending over, or lying down to sitting upright). </p> <p>We used the Kruskal-Wallis test to assess the level of inter-cluster statistical significance for each posture feature, and corrected the p-values using the Benjamini-Hochberg (BH) method. Then, in each posture-specific cluster, we assessed the median and IQR of cluster-specific illness burden variables. We computed pairwise Spearman correlation coefficients to assess the strength and direction of the association between the postural dynamics descriptors (e.g., mean, IQR) and illness burden continuous variables (e.g., lifetime number of depressive episodes). We used a Chi Square test to assess the association between posture and categorical illness burden variables (e.g., history of suicide attempts, family history of suicide). We controlled for age, baseline functional capacity, and body mass index (BMI) by setting them as control variables in a multiple linear regression model. The p-values were corrected using the BH method. </p> <p>To identify cluster members (i.e., participants who shared similar postural dynamics), we performed hierarchical clustering of BD participants using posture features as model input.</p> <p><br></p>

Background Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. Methods Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. Results Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed “perfect” adherence; 37.1% showed “good” adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. Conclusions Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement.