Sleep-wake variations of electrodermal activity in bipolar disorder.

BACKGROUND: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities. METHODS: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates. RESULTS: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not. CONCLUSION: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

The adaptive ability performance test (ADAPT): A factor analytic study in clients with intellectual disabilities.

BACKGROUND: The adaptive ability performance test (ADAPT) was developed to assess adaptive skills in individuals with intellectual disabilities and borderline intellectual functioning, with or without mental disorders. As a follow-up to earlier research on the ADAPT, a factor analytic study was conducted. METHOD: One thousand and sixty six ADAPTs from clients with (suspected) intellectual disabilities or borderline intellectual functioning and 129 ADAPTs from participants from the general population were collected along with other characteristics (e.g., IQ, psychiatric classifications, living situation). RESULTS: An exploratory factor analysis (EFA) was performed and resulted in good fit indices. Subsequent confirmatory factor analysis (CFA) and multigroup CFA showed acceptable to good fit indices. This resulted in an instrument with eight factors and 62 items. CONCLUSION: Factor analytic results suggest that the ADAPT is a valid instrument that measures adaptive skills in individuals with intellectual disabilities or borderline intellectual functioning.

Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG).

Wearable monitoring devices are an innovative way to measure heart rate (HR) and heart rate variability (HRV), however, there is still debate about the validity of these wearables. This study aimed to validate the accuracy and predictive value of the Empatica E4 wristband against the VU University Ambulatory Monitoring System (VU-AMS) in a clinical population of traumatized adolescents in residential care. A sample of 345 recordings of both the Empatica E4 wristband and the VU-AMS was derived from a feasibility study that included fifteen participants. They wore both devices during two experimental testing and twelve intervention sessions. We used correlations, cross-correlations, Mann-Whitney tests, difference factors, Bland-Altman plots, and Limits of Agreement to evaluate differences in outcomes between devices. Significant correlations were found between Empatica E4 and VU-AMS recordings for HR, SDNN, RMSSD, and HF recordings. There was a significant difference between the devices for all parameters but HR, although effect sizes were small for SDNN, LF, and HF. For all parameters but RMSSD, testing outcomes of the two devices led to the same conclusions regarding significance. The Empatica E4 wristband provides a new opportunity to measure HRV in an unobtrusive way. Results of this study indicate the potential of the Empatica E4 as a practical and valid tool for research on HR and HRV under non-movement conditions. While more research needs to be conducted, this study could be considered as a first step to support the use of HRV recordings provided by wearables.

Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial.

Introduction: Forensic psychiatric patients receive treatment to address their violent and aggressive behavior with the aim of facilitating their safe reintegration into society. On average, these treatments are effective, but the magnitude of effect sizes tends to be small, even when considering more recent advancements in digital mental health innovations. Recent research indicates that wearable technology has positive effects on the physical and mental health of the general population, and may thus also be of use in forensic psychiatry, both for patients and staff members. Several applications and use cases of wearable technology hold promise, particularly for patients with mild intellectual disability or borderline intellectual functioning, as these devices are thought to be user-friendly and provide continuous daily feedback. Method: In the current randomized crossover trial, we addressed several limitations from previous research and compared the (continuous) usability and acceptance of four selected wearable devices. Each device was worn for one week by staff members and patients, amounting to a total of four weeks. Two of the devices were general purpose fitness trackers, while the other two devices used custom made applications designed for bio-cueing and for providing insights into physiological reactivity to daily stressors and events. Results: Our findings indicated significant differences in usability, acceptance and continuous use between devices. The highest usability scores were obtained for the two fitness trackers (Fitbit and Garmin) compared to the two devices employing custom made applications (Sense-IT and E4 dashboard). The results showed similar outcomes for patients and staff members. Discussion: None of the devices obtained usability scores that would justify recommendation for future use considering international standards; a finding that raises concerns about the adaptation and uptake of wearable technology in the context of forensic psychiatry. We suggest that improvements in gamification and motivational aspects of wearable technology might be helpful to tackle several challenges related to wearable technology.

The geometry of synchronization: quantifying the coupling direction of physiological signals of stress between individuals using inter-system recurrence networks.

In the study of synchronization dynamics between interacting systems, several techniques are available to estimate coupling strength and coupling direction. Currently, there is no general 'best' method that will perform well in most contexts. Inter-system recurrence networks (IRN) combine auto-recurrence and cross-recurrence matrices to create a graph that represents interacting networks. The method is appealing because it is based on cross-recurrence quantification analysis, a well-developed method for studying synchronization between 2 systems, which can be expanded in the IRN framework to include N > 2 interacting networks. In this study we examine whether IRN can be used to analyze coupling dynamics between physiological variables (acceleration, blood volume pressure, electrodermal activity, heart rate and skin temperature) observed in a client in residential care with severe to profound intellectual disabilities (SPID) and their professional caregiver. Based on the cross-clustering coefficients of the IRN conclusions about the coupling direction (client or caregiver drives the interaction) can be drawn, however, deciding between bi-directional coupling or no coupling remains a challenge. Constructing the full IRN, based on the multivariate time series of five coupled processes, reveals the existence of potential feedback loops. Further study is needed to be able to determine dynamics of coupling between the different layers.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers.

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a "Wearables" R package and a Shiny "E4 dashboard" application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

Heart rate and skin conductance associations with physical aggression, psychopathy, antisocial personality disorder and conduct disorder: An updated meta-analysis.

The associations between physiological measures (i.e., heart rate and skin conductance) of autonomic nervous system (ANS) activity and severe antisocial spectrum behavior (AB) were meta-analyzed. We used an exhaustive partitioning of variables relevant to the ANS-AB association and investigated four highly relevant questions (on declining effect sizes, psychopathy subscales, moderators, and ANS measures) that are thought to be transformative for future research on AB. We investigated a broad spectrum of physiological measures (e.g., heart rate (variability), pre-ejection period) in relation to AB. The search date for the current meta-analysis was on January 1st, 2020, includes 101 studies and 769 effect sizes. Results indicate that effect sizes are heterogeneous and bidirectional. The careful partitioning of variables sheds light on the complex associations that were obscured in previous meta-analyses. Effects are largest for the most violent offenders and for psychopathy and are dependent on the experimental tasks used, parameters calculated, and analyses run. Understanding the specificity of physiological reactions may be expedient for differentiating between (and within) types of AB.

Physiological Measurements of Stress Preceding Incidents of Challenging Behavior in People With Severe to Profound Intellectual Disabilities: Longitudinal Study Protocol of Single-Case Studies.

BACKGROUND: Clients with severe to profound intellectual disabilities (SPID) and challenging behavior (CB) and the professional caregivers that support them are vulnerable to high stress levels, which negatively impact their well-being and the quality of care. CB is thought to result from an increase in the intensity and frequency of clients' stress experiences. In turn, staff members experience stress in dealing with this behavior, and stressed staff members might behave in ways that increase clients' stress levels, contributing to the origin and maintenance of CB. Research into these dyadic interactions between clients and staff is scarce for people with SPID, especially in real-life situations. The barriers of studying stress in this population include clients' difficulties in communicating stress experiences and the lack of an objective continuous measure of stress. OBJECTIVE: This paper presents a protocol for studying patterns of physiological stress in 15 client-caregiver dyads in the 30 minutes preceding incidents of CB compared to control periods without CB and the interplay between the stress levels of clients and professional caregivers. METHODS: We will conduct 15 single-case studies to assess patterns of physiological stress in dyads of clients with SPID and professional caregivers prior to CB in several Dutch residential institutes. Client-caregiver dyads will wear the Empatica E4 wristband for 20 sessions of 3 to 8 hours without interruptions of daily routines while caregivers report clients' CB. The physiological measures obtained will be electrodermal activity (microsiemens) and heart rate (beats per minute). A multilevel model with repeated measures at the incident level nested within the person level will be applied, employing separate models for electrodermal activity and heart rate to compare stress levels in the 30 minutes prior to incidents with control epochs. Covariates in the models include movement, temperature, and gender. In addition, cross-recurrence quantification analyses will be performed to study the synchronization between the stress levels of clients and professional caregivers. RESULTS: The Ethics Committee of the Radboud University (NL-number: NL71683.091.19) approved the study on February 12, 2020. In total, 15 organizations have declared their commitment to participate in the study. The first result is expected in the spring of 2022. CONCLUSIONS: Study results will demonstrate whether changes in patterns of electrodermal activity and heart rate are apparent in the 30 minutes preceding an incident of CB compared to baseline levels when the client does not engage in CB. The synchronization between caregivers' and clients' physiological stress levels will be explored with cross-recurrence quantification analyses. Insights into the physiological stress levels of clients and caregivers may contribute to a reduction of CB and an improvement of both clients' and caregivers' safety and well-being. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24911.

A Latent Class Analysis of Forensic Psychiatric Patients in Relation to Risk and Protective Factors.

Forensic psychiatric patients form a very heterogeneous population regarding psychopathology, criminal history, and risk factors for reoffending. Therefore, the present study aimed to investigate whether there are more homogeneous classes of forensic patients based on DSM-IV-TR Axis I and II diagnoses and previously committed offenses, by means of explorative latent class analysis (LCA). It was also investigated which risk and protective factors are significantly more prevalent in one class compared to other classes. The study sample contained 722 male forensic psychiatric patients who were unconditionally released between 2004 and 2014 from high-security forensic clinics. Data were retrospectively derived from electronic patient files. Five distinctive patient classes emerged: class with only Axis II diagnosis, class with multiple problems, antisocial class, psychotic class, and intellectually disabled class. These classes differed significantly in risk and protective factors. This study contributes to the understanding of patient classes and provides directions for future, class-tailored interventions.

Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study.

BACKGROUND: The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data. METHOD: Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared: regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine. RESULTS: At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.52, BA 0.74) and the gradient boosting to predict suicide attempt (AUC 0.80, PPV 0.10, BA 0.69). LIMITATIONS: The number of respondents with suicidal behaviour at follow up was small. We only had data on psychological risk factors, limiting the potential of the more complex machine learning algorithms to outperform regular logistical regression. CONCLUSIONS: When applied to population-based longitudinal data containing multiple psychological measurements, machine learning techniques did not significantly improve the predictive accuracy of suicidal behaviour. Adding more detailed data on for example employment, education or previous health care uptake, might result in better performance of machine learning over regular logistical regression.

Burnout symptoms in forensic mental health nurses: Results from a longitudinal study.

Burnout in nursing staff is a major cause for turnover and absenteeism. Identifying risk and protective factors may be helpful in decreasing burnout symptoms. Moreover, research indicates that ambulatory assessments of the autonomic nervous system might be helpful in detecting long-term stress and burnout symptoms. One hundred and ten forensic nursing staff members completed questionnaires measuring experiences with aggressive behaviour, emotional intelligence, personality, and job stress during four waves of data collection across a 2-year period. Multilevel analyses were used to test the predicted associations and moderation effects with (the development of) burnout symptoms. Burnout was predicted by a combination of emotional intelligence, job stress, aggression, personality factors, and skin conductance, but no moderation effects over time were found. Over a period of 2 years, the model approximately predicts a change in one burnout category on the Maslach Burnout Inventory. The amount of burnout symptoms in nurses might be used as an indicator to predict turnover and absenteeism considering the increase in symptoms over time. Nursing staff who experience severe aggression and who have relatively low levels of emotional intelligence and altruism and high levels of neuroticism and job stress should be monitored and supported to decrease the risk of burnout. Staff members can be trained to increase their emotional intelligence and relieve stress to decrease their burnout symptoms and turnover and absenteeism on the long term. Ambulatory assessment might be helpful as a nonintrusive way to detect increasing levels of burnout.

Changes in heart rate and skin conductance in the 30 min preceding aggressive behavior.

Aggressive behavior of inpatients threatens the safety and well-being of both mental health staff members and fellow patients. It was investigated whether heart rate and electrodermal activity can be used to signal imminent aggression. A naturalistic study was conducted in which 100 inpatients wore sensor wristbands during 5 days to monitor their heart rate and electrodermal activity while staff members recorded patients' aggressive incidents on the ward. Of the 100 patients, 36 displayed at least one aggressive incident. Longitudinal multilevel models indicated that heart rate, skin conductance level, and the number of nonspecific skin conductance responses per minute rose significantly in the 20 min preceding aggressive incidents. Although psychopathy was modestly correlated with displaying aggression, it was not a significant predictor of heart rate and skin conductance preceding aggression. The current findings may provide opportunities for the development of individual prediction models to aid acute risk assessment and to predict aggressive incidents in an earlier stage. The current results on the physiological indicators of aggression are promising for reducing aggression and improving both staff as well as patient safety in psychiatric mental health institutions.

Burnout symptoms in forensic psychiatric nurses and their associations with personality, emotional intelligence and client aggression: A cross-sectional study.

WHAT IS KNOWN ON THE SUBJECT?: Client aggression in forensic psychiatry is associated with burnout symptoms in nursing staff. It is unclear what mechanisms contribute to this relationship. The type and severity of aggression might be of importance in the association between client aggression and burnout symptoms, but also the personality characteristics and emotional intelligence of nursing staff. It is unknown whether wearable devices that measure arousal can be used to detect chronic stress and burnout symptoms. WHAT DOES THIS PAPER ADD TO EXISTING KNOWLEDGE?: Especially, physical aggression as experienced by nursing staff is associated with staff's burnout symptoms (e.g., emotional exhaustion and depersonalization). Further research on the aggression questionnaire is necessary. The stress management skill of nursing staff is an important factor to consider in the association between burnout symptoms and client aggression. The wearable device was not useful for detecting burnout symptoms. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Nursing staff who experience (physical) aggression should be carefully monitored and should receive social support from their management to aid their well-being. Contrary to intuition, nurses who reported a higher number of stress management skills might have to be monitored more closely if necessary. ABSTRACT: Introduction Aggressive behaviour of forensic clients is associated with burnout symptoms in nursing staff. The role of staff characteristics as moderators is unclear. Aim We explored the association between type and severity of aggressive behaviour as experienced by nursing staff and staff's burnout symptoms. In addition, the moderating roles of personality characteristics and emotional intelligence (EI) were studied. Moreover, the usefulness of ambulatory skin conductance assessments in detecting arousal related to burnout symptoms was studied. Method A total of 114 forensic nursing staff members filled out questionnaires and wore an ambulatory device. Results Experiencing physical aggression was positively associated with staff's burnout symptoms. Stress management skills, a subscale of EI, but not personality, moderated this relationship. Skin conductance was not associated with burnout symptoms. Remarkably, the association between aggression and burnout symptoms was highest for staff reporting a higher number of stress management skills. Discussion Longitudinal research is necessary to establish causality between client aggression and staff burnout symptoms. In addition, further research is necessary on the validity of the aggression measure used in the current study. Implication for practice Nursing staff who experience physical aggression frequently should receive social support for this, and staff who report high stress management skills should be monitored more carefully after having been confronted with aggression.