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Background: The fifth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) introduced the dissociative subtype of posttraumatic stress disorder (D-PTSD). To assess this subtype, the Dissociative Subtype of PTSD Scale (DSPS), a 15-item self-report measure to identify lifetime and current dissociative symptoms of D-PTSD, was developed. However, so far, the scale has only been validated in war veterans. Moreover, criterion validity and diagnostic utility have not been examined yet. Objective: We aimed to validate the DSPS in two samples of civilian trauma-exposed German-speaking participants. Methods: In Study 1, a pre-registered online study, participants with and without PTSD symptoms ( N  = 558) answered questionnaires about traumatic experiences, dissociation, PTSD, depression, generalized anxiety disorder, somatic symptom disorder, alcohol use disorder, absorption, and dissociative responding to trauma-related questionnaires. In Study 2, which used secondary data of a pre-registered clinical study, participants with a PTSD diagnosis ( N  = 71) answered questionnaires about traumatic experiences, dissociation, PTSD, depression, generalized anxiety disorder, somatic symptom disorder, and dissociative responding to standardized trauma exposure. Moreover, PTSD, D-PTSD, and other diagnoses were assessed with structured clinical interviews. Results: Analyses confirmed a three-factor structure as well as high internal consistency, and high convergent, discriminant, and criterion validity of the DSPS. Moreover, the scale was able to identify a latent D-PTSD group and individuals with D-PTSD diagnosis. Conclusions: The DSPS constitutes a reliable and valid tool to assess D-PTSD symptoms in clinical practice and research and thereby may contribute to a better understanding of these debilitating symptoms.

The current study investigated heart rate (HR) and heart rate variability (HRV) across day and night in patients with disorders of consciousness (DOC). We recorded 24-h electrocardiography in 26 patients with DOC (i.e., unresponsive wakefulness syndrome (UWS; n = 16) and (exit) minimally conscious state ((E)MCS; n = 10)). To examine diurnal variations, HR and HRV indices in the time, frequency, and entropy domains were computed for periods of clear day- (forenoon: 8 a.m.-2 p.m.; afternoon: 2 p.m.-8 p.m.) and nighttime (11 p.m.-5 a.m.). The results indicate that patients' interbeat intervals (IBIs) were larger during the night than during the day, indicating HR slowing. The patients in UWS showed larger IBIs compared to the patients in (E)MCS, and the patients with non-traumatic brain injury showed lower HRV entropy than the patients with traumatic brain injury. Additionally, higher HRV entropy was associated with higher EEG entropy during the night. Thus, cardiac activity varies with a diurnal pattern in patients with DOC and can differentiate between patients' diagnoses and etiologies. Moreover, the interaction of heart and brain appears to follow a diurnal rhythm. Thus, HR and HRV seem to mirror the integrity of brain functioning and, consequently, might serve as supplementary measures for improving the validity of assessments in patients with DOC.

Background: Peritraumatic dissociation is purported to emerge together with attenuated autonomic arousal, immobility, and staring. However, empirical evidence is scarce and heterogeneous. Moreover, it is still a matter of debate whether these responses predict intrusion formation., Objective: The present trauma-analogue study examined associations between peritraumatic dissociation, autonomic activation, facial movements, staring, and intrusion formation., Method: Seventy-one healthy women watched a highly aversive film, while autonomic activation (heart rate, respiratory sinus arrhythmia, skin conductance level), facial movements (temporal variations in corrugator electromyography), and staring (fixation duration, tracklength) were assessed. Afterwards, participants rated the intensity of dissociation during film viewing and reported intrusions and associated distress in a smartphone application for 24 hours., Results: Peritraumatic dissociation was linked to higher autonomic arousal (higher heart rate and, on a trend-level, lower respiratory sinus arrhythmia), increased facial movements, and staring (lower tracklength). Peritraumatic dissociation, higher autonomic arousal (higher heart rate and lower respiratory sinus arrhythmia), staring (higher fixation duration), and, on a trend-level, more facial movements were linked to higher intrusion load (number x distress of intrusions) and together explained 59% of variance. Skin conductance level was neither linked to peritraumatic dissociation nor intrusion load., Conclusions: Our results suggest that, at low-dissociation-levels observed in trauma-analogue studies, peritraumatic dissociation may occur together with heightened autonomic arousal and facial movements, indexing increased negative affect. Staring might, irrespectively of dissociation-levels, serve as objective marker for dissociation. Together, peritraumatic dissociation and its psychophysiological correlates might set the stage for later intrusion formation., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study's results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant's environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a "gold standard" of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant's perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.