Stress Watch: The Use of Heart Rate and Heart Rate Variability to Detect Stress: A Pilot Study Using Smart Watch Wearables.

Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that individuals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the individual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress

Associations between Sleep Quality and Heart Rate Variability: Implications for a Biological Model of Stress Detection Using Wearable Technology.

INTRODUCTION: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles; however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy individuals. AIM: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy individuals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. METHODS: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). RESULT: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. CONCLUSION: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score

The relationship between neurocognitive performance and HRV parameters in nurses and non-healthcare participants.

Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non-invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non-nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini-Mental State Examination and Cognistat psychometric questionnaires. ECG-derived HRV parameters were acquired during the Trier Social Stress Test. Between-group differences were found in domain-specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain-specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain-specific cognitive performance in the non-nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition

Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

ObjectiveMental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.MethodsTo overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.ResultsThe results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%

BRAF V600E status may facilitate decision-making on active surveillance of low-risk papillary thyroid microcarcinoma.

Introduction: Conservative active surveillance has been proposed for low-risk papillary thyroid microcarcinoma (PTMC), defined as 641.0 cm and lacking clinical aggressive features, but controversy exists with accepting it as not all such PTMCs are uniformly destined for benign prognosis. This study investigated whether BRAF V600E status could further risk stratify PTMC, particularly low-risk PTMC, and can thus help with more accurate case selection for conservative management. Methods: This international multicenter study included 743 patients treated with total thyroidectomy for PTMC (584 women and 159 men), with a median age of 49 years (interquartile range [IQR], 39-59 years) and a median follow-up time of 53 months (IQR, 25-93 months). Results: On overall analyses of all PTMCs, tumour recurrences were 6.4% (32/502) versus 10.8% (26/241) in BRAF mutation-negative versus BRAF mutation-positive patients (P = 0.041), with a hazard ratio (HR) of 2.44 (95% CI (confidence interval), 1.15-5.20) after multivariate adjustment for confounding clinical factors. On the analyses of low-risk PTMC, recurrences were 1.3% (5/383) versus 4.3% (6/139) in BRAF mutation-negative versus BRAF mutation-positive patients, with an HR of 6.65 (95% CI, 1.80-24.65) after adjustment for confounding clinical factors. BRAF mutation was associated with a significant decline in the Kaplan-Meier recurrence-free survival curve in low-risk PTMC. Conclusions: BRAF V600E differentiates the recurrence risk of PTMC, particularly low-risk PTMC. Given the robust negative predictive value, conservative active surveillance of BRAF mutation-negative low-risk PTMC is reasonable whereas the increased recurrence risk and other well-known adverse effects of BRAF V600E make the feasibility of long-term conservative surveillance uncertain for BRAF mutation-positive PTMC

Maternal Plasma 25-Hydroxyvitamin D Concentrations and the Risk for Gestational Diabetes Mellitus

Background: Evidence is accumulating for a role of vitamin D in maintaining normal glucose homeostasis. However, studies that prospectively examined circulating concentrations of 25-hydroxyvitamin D (25-[OH] D) in relation to diabetes risk are limited. Our objective is to determine the association between maternal plasma 25-[OH] D concentrations in early pregnancy and the risk for gestational diabetes mellitus (GDM). Methods: A nested case-control study was conducted among a prospective cohort of 953 pregnant women. Among them, 57 incident GDM cases were ascertained and 114 women who were not diagnosed with GDM were selected as controls. Controls were frequency matched to cases for the estimated season of conception of the index pregnancy. Results: Among women who developed GDM, maternal plasma 25-[OH] D concentrations at an average of 16 weeks of gestation were significantly lower than controls (24.2 vs. 30.1 ng/ml, P<0.001). This difference remained significant (3.62 ng/ml lower on average in GDM cases than controls (P value = 0.018)) after the adjustment for maternal age, race, family history of diabetes, and pre-pregnancy BMI. Approximately 33% of GDM cases, compared with 14% of controls (P<0.001), had maternal plasma 25-[OH] D concentrations consistent with a pre-specified diagnosis of vitamin D deficiency (<20 ng/ml). After adjustment for the aforementioned covariates including BMI, vitamin D deficiency was associated with a 2.66-fold (OR (95% CI): 2.66 (1.01–7.02)) increased GDM risk. Moreover, each 5 ng/ml decrease in 25-[OH] D concentrations was related to a 1.29-fold increase in GDM risk (OR (95% CI): 1.29 (1.05–1.60)). Additional adjustment for season and physical activity did not change findings substantially. Conclusions: Findings from the present study suggest that maternal vitamin D deficiency in early pregnancy is significantly associated with an elevated risk for GDM

Bayesian approach to determining penetrance of pathogenic SDH variants.

BACKGROUND: Until recently, determining penetrance required large observational cohort studies. Data from the Exome Aggregate Consortium (ExAC) allows a Bayesian approach to calculate penetrance, in that population frequencies of pathogenic germline variants should be inversely proportional to their penetrance for disease. We tested this hypothesis using data from two cohorts for succinate dehydrogenase subunits A, B and C (SDHA-C) genetic variants associated with hereditary pheochromocytoma/paraganglioma (PC/PGL). METHODS: Two cohorts were 575 unrelated Australian subjects and 1240 unrelated UK subjects, respectively, with PC/PGL in whom genetic testing had been performed. Penetrance of pathogenic SDHA-C variants was calculated by comparing allelic frequencies in cases versus controls from ExAC (removing those variants contributed by The Cancer Genome Atlas). RESULTS: Pathogenic SDHA-C variants were identified in 106 subjects (18.4%) in cohort 1 and 317 subjects (25.6%) in cohort 2. Of 94 different pathogenic variants from both cohorts (seven in SDHA, 75 in SDHB and 12 in SDHC), 13 are reported in ExAC (two in SDHA, nine in SDHB and two in SDHC) accounting for 21% of subjects with SDHA-C variants. Combining data from both cohorts, estimated lifetime disease penetrance was 22.0% (95% CI 15.2% to 30.9%) for SDHB variants, 8.3% (95% CI 3.5% to 18.5%) for SDHC variants and 1.7% (95% CI 0.8% to 3.8%) for SDHA variants. CONCLUSION: Pathogenic variants in SDHB are more penetrant than those in SDHC and SDHA. Our findings have important implications for counselling and surveillance of subjects carrying these pathogenic variants

Single-nuclei and bulk-tissue gene-expression analysis of pheochromocytoma and paraganglioma links disease subtypes with tumor microenvironment

Pheochromocytomas (PC) and paragangliomas (PG) are rare neuroendocrine tumors associated with autonomic nerves. Here we use single-nuclei RNA-seq and bulk-tissue gene-expression data to characterize the cellular composition of PCPG and normal adrenal tissues, refine tumor gene-expression subtypes and make clinical and genotypic associations. We confirm seven PCPG gene-expression subtypes with significant genotype and clinical associations. Tumors with mutations in VHL, SDH-encoding genes (SDHx) or MAML3-fusions are characterized by hypoxia-inducible factor signaling and neoangiogenesis. PCPG have few infiltrating lymphocytes but abundant macrophages. While neoplastic cells transcriptionally resemble mature chromaffin cells, early chromaffin and neuroblast markers are also features of some PCPG subtypes. The gene-expression profile of metastatic SDHx-related PCPG indicates these tumors have elevated cellular proliferation and a lower number of non-neoplastic Schwann-cell-like cells, while GPR139 is a potential theranostic target. Our findings therefore clarify the diverse transcriptional programs and cellular composition of PCPG and identify biomarkers of potential clinical significance.Magnus Zethoven, Luciano Martelotto, Andrew Pattison, Blake Bowen, Shiva Balachander, Aidan Flynn, Fernando J. Rossello, Annette Hogg, Julie A.Miller, Zdenek Frysak, Sean Grimmond, Lauren Fishbein, Arthur S. Tischler, Anthony J. Gill, Rodney J. Hicks, Patricia L. M. Dahia, Roderick Clifton-Bligh, Karel Pacak, Richard W. Tothil