Search Results (14)

Anxiety and depression are highly prevalent mental health disorders often associated with dysregulation of neuroendocrine and immune systems, particularly the hypothalamic–pituitary–adrenal (HPA) axis and the sympathetic–adrenal–medullary (SAM) system. Recent research highlights the potential of salivary biomarkers to serve as non-invasive indicators for psychological distress. This narrative review synthesizes current evidence on key salivary biomarkers, cortisol, alpha-amylase (sAA), secretory immunoglobulin A (sIgA), chromogranin A (CgA), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), C-reactive protein (CRP), brain-derived neurotrophic factor (BDNF), and salivary microRNAs (miRNAs), in relation to anxiety, depression, and stress. A comprehensive literature search (2010–2025) was conducted using multiple databases and relevant MeSH terms. The review reveals consistent associations between these salivary analytes and stress-related disorders, reflecting changes in neuroendocrine activity, immune response, and neuroplasticity. Cortisol and sAA mirror acute stress reactivity, while cytokines and CRP indicate chronic inflammation. BDNF and miRNAs provide insight into neuroplastic dysfunction and gene regulation. Despite promising results, limitations such as variability in sampling methods and biomarker specificity remain. In conclusion, salivary biomarkers offer a promising avenue for early detection, monitoring, and personalization of treatment in mood and anxiety disorders. Conclusions: Cortisol and alpha-amylase serve as the principal markers of acute stress response, whereas cytokines such as IL-6 and TNF-α, together with CRP, indicate chronic inflammation associated with extended emotional distress. Full article

Stress has become a common mental health issue in modern society, causing individuals to experience acute behavioral changes. Exposure to prolonged stress without proper prevention and treatment may cause severe damage to one’s physiological and psychological health. Researchers around the world have been working to find and create solutions for early stress detection using machine learning (ML). This paper investigates the possibility of utilizing Tiny Machine Learning (TinyML) in developing a wearable device, comparable to a smartwatch, that is equipped with both physiological and psychological data detection system to enable edge computing and give immediate feedback for stress prediction. The main challenge of this study was to fit a trained ML model into the microcontroller’s limited memory without compromising the model’s accuracy. A TinyML-based framework using a Raspberry Pi Pico RP2040 on a customized board equipped with several health sensors was proposed to predict stress levels by utilizing accelerations, body temperature, heart rate, and electrodermal activity from a public health dataset. Moreover, a few selected machine learning models underwent hyperparameter tuning before a porting library was used to translate them from Python to C/C++ for deployment. This approach led to an optimized XGBoost model with 86.0% accuracy and only 1.12 MB in size, hence perfectly fitting into the 2 MB constraint of RP2040. The prediction of stress on the edge device was then tested and validated using a separate sub-dataset. This trained model on TinyML can also be used to obtain an immediate reading from the calibrated health sensors for real-time stress predictions. Full article

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on. Full article

Acute psychological stress (APS) is a complex and multifactorial phenomenon that affects metabolism, necessitating real-time detection and interventions to mitigate its effects on glycemia in people with type 1 diabetes. This study investigates the detection of APS using physiological variables measured by the Empatica E4 wristband and employs explainable machine learning to evaluate the importance of the physiological signals. The extreme gradient boosting model is developed for classification of APS and non-stress (NS) with weighted training, achieving an overall accuracy of 99.93%. The Shapley additive explanations (SHAP) technique is employed to interpret the global importance of the physiological signals, determining the order of importance for the variables from most to least as galvanic skin response (GSR), heart rate (HR), skin temperature (ST), and motion sensors (accelerometer readings). The increase in GSR and HR are positively correlated with the occurrence of APS as indicated by high positive SHAP values. The SHAP technique is also used to explain the local signal importance for particular instances of misclassified samples. The detection of APS can inform multivariable automated insulin delivery systems to intervene to counteract the APS-induced glycemic excursions in people with type 1 diabetes. Full article

Acute optic neuritis (AON) is a common cause of sudden visual loss in young patients. Because of the risk of demyelinating disease, patients affected by unilateral or bilateral optic neuritis should be evaluated and treated accordingly. Despite advancements in imaging of the brain and retina, misdiagnosis of AON is not uncommon. Indeed, some acute disorders of the retina have the potential to mimic AON and their prompt diagnosis may avoid unnecessary neurologic investigation, psychological stress to the patient, and delays in treatment. This review describes uncommon retinal disorders presenting with sudden-onset visual loss and absent or subtle funduscopic manifestation that can mimic AON. Multimodal retinal imaging is essential in detecting these conditions and in their differential diagnosis. It behooves neurologists and general ophthalmologists to be aware of these entities and be familiar with multimodal imaging of the retina. Full article

Acute stress response triggers various physiological responses such as energy mobilization to meet metabolic demands. However, the underlying molecular changes in the brain remain largely obscure. Here, we used a brief water avoidance stress (WAS) to elicit an acute stress response in mice. By employing RNA-sequencing and metabolomics profiling, we investigated the acute stress-induced molecular changes in the mouse whole brain. The aberrant expression of 60 genes was detected in the brain tissues of WAS-exposed mice. Functional analyses showed that the aberrantly expressed genes were enriched in various processes such as superoxide metabolism. In our global metabolomic profiling, a total of 43 brain metabolites were significantly altered by acute WAS. Metabolic pathways upregulated from WAS-exposed brain tissues relative to control samples included lipolysis, eicosanoid biosynthesis, and endocannabinoid synthesis. Acute WAS also elevated the levels of branched-chain amino acids, 5-aminovalerates, 4-hydroxy-nonenal-glutathione as well as mannose, suggesting complex metabolic changes in the brain. The observed molecular events in the present study provide a valuable resource that can help us better understand how acute psychological stress impacts neural functions. Full article

Wearable sensor data can be integrated and interpreted to improve the treatment of chronic conditions, such as diabetes, by enabling adjustments in treatment decisions based on physical activity and psychological stress assessments. The challenges in using biological analytes to frequently detect physical activity (PA) and acute psychological stress (APS) in daily life necessitate the use of data from noninvasive sensors in wearable devices, such as wristbands. We developed a recurrent multi-task deep neural network (NN) with long-short-term-memory architecture to integrate data from multiple sensors (blood volume pulse, skin temperature, galvanic skin response, three-axis accelerometers) and simultaneously detect and classify the type of PA, namely, sedentary state, treadmill run, stationary bike, and APS, such as non-stress, emotional anxiety stress, mental stress, and estimate the energy expenditure (EE). The objective was to assess the feasibility of using the multi-task recurrent NN (RNN) rather than independent RNNs for detection and classification of AP and APS. The multi-task RNN achieves comparable performance to independent RNNs, with the multi-task RNN having F1 scores of 98.00% for PA and 98.97% for APS, and a root mean square error (RMSE) of 0.728 $\frac{\mathrm{cal}}{\mathrm{hr}.\mathrm{kg}}$ for EE estimation for testing data. The independent RNNs have F1 scores of 99.64% for PA and 98.83% for APS, and an RMSE of 0.666 $\frac{\mathrm{cal}}{\mathrm{hr}.\mathrm{kg}}$ for EE estimation. The results indicate that a multi-task RNN can effectively interpret the signals from wearable sensors. Additionally, we developed individual and multi-task extreme gradient boosting (XGBoost) for separate and simultaneous classification of PA types and APS types. Multi-task XGBoost achieved F1 scores of 99.89% and 98.31% for the classification of PA types and APS types, respectively, while the independent XGBoost achieved F1 scores of 99.68% and 96.77%, respectively. The results indicate that both multi-task RNN and XGBoost can be used for the detection and classification of PA and APS without loss of performance with respect to individual separate classification systems. People with diabetes can achieve better outcomes and quality of life by including physical activity and psychological stress assessments in treatment decision-making. Full article

Psychological stress affects the immune system and activates peripheral inflammatory pathways. Circulating cell-free DNA (cfDNA) is associated with systemic inflammation, and recent research indicates that cfDNA is an inflammatory marker that is sensitive to psychological stress in humans. The present study investigated the effects of acute stress on the kinetics of cfDNA in a within-subjects design. Twenty-nine males (mean age: 24.34 ± 4.08 years) underwent both the Trier Social Stress Test (TSST) and a resting condition. Blood samples were collected at two time points before and at 9 time points up to 105 min after both conditions. The cfDNA immediately increased 2-fold after the TSST and returned to baseline levels after 30 min after the test, showing that a brief psychological stressor was sufficient to evoke a robust and rapid increase in cfDNA levels. No associations were detected between perceived stress, whereas subjects with higher basal cfDNA levels showed higher increases. The rapid cfDNA regulation might be attributed to the transient activation of immune cells caused by neuroendocrine-immune activation. Further research is required to evaluate the reliability of cfDNA as a marker of neuroendocrine-immune activation, which could be used for diagnostics purposes or monitoring of treatment progression. Full article

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), created a significant problem people’s health around the world. The mental and physical health of entire populations has been negatively impacted due to the introduction of several restriction methods. Maintaining a specific physical activity and fitness level is crucial given the pandemic situation. The connection between physical fitness and mental health has recently received growing attention. In contrast to the message from physiological research, which lauds the general benefits of fitness for physical health, the corresponding psychological literature reveals a more complex relationship. This paper outlines the research evidence, focusing on the relationship between physical fitness and depression, anxiety, and stress before and during the COVID-19 pandemic. Data were obtained from 390 higher education students (measuring their perception before and during the pandemic). They were analyzed by a structural equation modeling multi-group analysis to detect the variance in the test relationship before and during the COVID-19 pandemic. Theoretical and empirical implications are also discussed. Full article

Detection and classification of acute psychological stress (APS) and physical activity (PA) in daily lives of people with chronic diseases can provide precision medicine for the treatment of chronic conditions such as diabetes. This study investigates the classification of different types of APS and PA, along with their concurrent occurrences, using the same subset of feature maps via physiological variables measured by a wristband device. Random convolutional kernel transformation is used to extract a large number of feature maps from the biosignals measured by a wristband device (blood volume pulse, galvanic skin response, skin temperature, and 3D accelerometer signals). Three different feature selection techniques (principal component analysis, partial least squares–discriminant analysis (PLS-DA), and sequential forward selection) as well as four approaches for addressing imbalanced sizes of classes (upsampling, downsampling, adaptive synthetic sampling (ADASYN), and weighted training) are evaluated for maximizing detection and classification accuracy. A long short-term memory recurrent neural network model is trained to estimate PA (sedentary state, treadmill run, stationary bike) and APS (non-stress, emotional anxiety stress, mental stress) from wristband signals. The balanced accuracy scores for various combinations of data balancing and feature selection techniques range between 96.82% and 99.99%. The combination of PLS–DA for feature selection and ADASYN for data balancing provide the best overall performance. The detection and classification of APS and PA types along with their concurrent occurrences can provide precision medicine approaches for the treatment of diabetes. Full article

The detection of psychological stress using the electrocardiogram (ECG) signal is most commonly based on the detection of the R peak—the most prominent part of the ECG waveform—and the heart rate variability (HRV) measurements derived from it. For stress detection algorithms focused on short-duration time windows, there is potential benefit in including HRV features derived from the detection of smaller peaks within the ECG waveform: the P, Q, S, and T waves. However, the potential drawback of using these small peaks is their smaller magnitude and subsequent susceptibility to noise, making them more difficult to reliably detect. In this work, we demonstrate the potential benefits of including smaller waves within binary stress classification using a pre-existing data set of ECG recordings from 57 participants (aged 18–40) with a self-reported fear of spiders during exposure to videos of spiders. We also present an analysis of the performance of an automated peak detection algorithm and the reliability of detection for each of the smaller parts of the ECG waveform. We compared two models, one with only R peak features and one with small peak features. They were similar in precision, recall, F1, area under ROC curve (AUC), and accuracy, with the greatest differences less than the standard deviations of each metric. There was a significant difference in the Akaike Information Criterion (AIC), which represented the information loss of the model. The inclusion of novel small peak features made the model $4.29\times {10}^{28}$ times more probable to minimize the information loss, and the small peak features showed higher regression coefficients than the R peak features, indicating a stronger relationship with acute psychological stress. This difference and further analysis of the novel features suggest that small peak intervals could be indicative of independent processes within the heart, reflecting a psychophysiological response to stress that has not yet been leveraged in stress detection algorithms. Full article

Background: Despite the high number of vaccines administered against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide, the information on the psychological/psychiatric adverse events following immunization (AEFI) with these newly developed vaccines remains scarce. Objective: To describe the frequency of psychological/psychiatric symptoms among recipients of five different anti-SARS-CoV-2 vaccines and to explore the factors associated with their development reported in the nationwide Mexican registry of AEFI against SARS-CoV-2. Methods: Descriptive study of all the psychological/psychiatric symptoms, including anxiety, panic attacks, insomnia, and agitation reported to the Mexican Epidemiological Surveillance System from 21 December 2020 to 27 April 2021, among adult (≥18 years old) recipients of 7,812,845 doses of BNT162b2, ChAdOx1 nCov-19, rAd26-rAd5, Ad5-nCoV, or CoronaVac. The factors associated with their development are determined by multivariate regression analysis. Results: There were 19,163 AEFI reports during the study period; amongst them, 191 (1%) patients had psychological/psychiatric symptoms (median age of 41 years, interquartile range of 32–54; 149 [78%] women) for an observed incidence of 2.44 cases per 100,000 administered doses (95% confidence interval [CI] 2.12–2.82), 72.8% of psychiatric AEFIs were reported among recipients of BNT162b2. The median time from vaccination to symptom onset was 35 min (interquartile range: 10–720). Overall, the most common psychological/psychiatric symptoms were anxiety in 129 (67.5%) patients, panic attacks in 30 (15.7%), insomnia in 25 (13%), and agitation in 11 (5.7%). After adjusting for the confounding factors, the odds for developing psychological/psychiatric symptoms were higher for those concurrently reporting syncope (odds ratio [OR]: 4.73, 95% CI: 1.68–13.33); palpitations (OR: 2.47, 95% CI: 1.65–3.70), and dizziness (OR: 1.59, 95% CI: 1.10–2.28). Conclusion: In our population, psychological/psychiatric symptoms were extremely infrequent AEFIs. No severe psychiatric AEFIs were reported. Immunization stress-related responses might explain most of the detected cases. Full article

Stress is the physical and psychological tension felt by an individual while adapting to difficult situations. Stress is known to alter the expression of stress hormones and cause neuroinflammation in the brain. In this study, miRNAs in serum-derived neuronal exosomes (nEVs) were analyzed to determine whether differentially expressed miRNAs could be used as biomarkers of acute stress. Specifically, acute severe stress was induced in Sprague-Dawley rats via electric foot-shock treatment. In this acute severe-stress model, time-dependent changes in the expression levels of stress hormones and neuroinflammation-related markers were analyzed. In addition, nEVs were isolated from the serum of control mice and stressed mice at various time points to determine when brain damage was most prominent; this was found to be 7 days after foot shock. Next-generation sequencing was performed to compare neuronal exosomal miRNA at day 7 with the neuronal exosomal miRNA of the control group. From this analysis, 13 upregulated and 11 downregulated miRNAs were detected. These results show that specific miRNAs are differentially expressed in nEVs from an acute severe-stress animal model. Thus, this study provides novel insights into potential stress-related biomarkers. Full article

Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS. Full article