Objective: In the present study, a photoplethysmographic (PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder (MDD) and healthy control (HC) subjects is presented. Methods: PPG recordings of 40 MDD and 40 HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate (HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity. Results: Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave ( $A_{13}$ ) in both HC and MDD group. However, autonomic reactivity indices related to $A_{13}$ reached higher values in HC than in MDD subjects (Cohen's d =0.81, AUC = 0.74), implying that the stress response in depressed patients is reduced. A statistically significant (p < 0.001) negative correlation (r = 0.5) between depression severity scores and $A_{13}$ was found. Conclusion: A decreased autonomic reactivity is associated with higher degree of depression. Significance: Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression.

The multidimensionality of the stress response has shown the complexity of this phenomenon and therefore the impossibility of finding a unique biomarker among the physiological variables related to stress. An experimental study was designed and performed to guarantee the correct synchronous and concurrent measure of psychometric tests, biochemical variables and physiological features related to acute emotional stress. The population studied corresponds to a group of 120 university students between 20 and 30 years of age, with healthy habits and without a diagnosis of chronic or psychiatric illnesses. Following the protocol of the experimental pilot, each participant reached a relaxing state and a stress state in two sessions of measurement for equivalent periods. Both states are correctly achieved evidenced by the psychometric test results and the biochemical variables. A Stress Reference Scale is proposed based on these two sets of variables. Then, aiming for a non-invasive and continuous approach, the Acute Stress Model correlated to the previous scale is also proposed, supported only by physiological signals. Preliminary results support the feasibility of measuring/quantifying the stress level. Although the results are limited to the population and stimulus type, the procedure and methodological analysis used for the assessment of acute stress in young people can be extrapolated to other populations and types of stress.

Caregiver burnout syndrome is an increasingly seen condition, although the subjective nature of self-administered psychometric tests and the lack of a consensual diagnostic tool might hinder a proper diagnosis. The availability of objective psychosomatic measures of stress might facilitate the early diagnosis and clinical management of these patients. For this reason, the aim of this work was to develop a quantitative tool to evaluate the stress level of caregivers in a noninvasive and repeatable manner. An observational, controlled, matched study was designed including a group of 38 principal caregivers of chronic patients and a control group of 38 non-caregivers. Psychometric, biochemical, and electrophysiological data were analyzed along with sociodemographic data. A quantitative chronic stress reference scale (CSRs) was constructed based on the weighted contribution of several psychometric and biochemical variables and afterwards, a predictive psychosomatic model (ESBSm) correlated with CSRs was elaborated from extracted variables of several electrophysiological signals monitored for 10 min. The resulting CSR scale shows a high power to discriminate caregivers from the control group while the ESBSm shows a 79% correlation with the CSR scale validated through a 5-fold process. Therefore, the results demonstrate that the ESBS model is an objective and validated tool to diagnose the degree of stress linked to burnout in caregivers of chronic patients from a 10-min session of noninvasive monitoring with a reliability equivalent to the questionnaires currently used to quantify stress in caregivers.

Caregiving induces chronic stress with physical and psychological impact on informal caregivers health. Therefore, subjective and objective indicators are needed for the early diagnosis of pathologic stress to prevent the risk of developing stress-related diseases in caregivers. Our aim was to assess the self-perceived stress, that is, how and how much the stressor affects the individual, through endocrine, metabolic, and immunologic biomarkers levels in geriatric and oncologic informal caregivers. Informal caregivers and non-caregivers were invited to participate in a cross-sectional study at the Clinic Hospital of Barcelona. Demographic and lifestyle characteristics, self-perceived stress (Perceived Stress Scale, State-Trait Anxiety Inventory and Stress Visual Analogue Scale), and biomarkers (copeptin, glucose, glycated hemoglobin, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), cholesterol, triglycerides, α-amylase, cortisol, tumor necrosis factor (TNF-α), and Interleukins (IL-6 and IL-10)) were evaluated. Descriptive and non-parametric statistical data analysis were performed. Fifty-six subjects (19 non-caregivers, 17 geriatric caregivers, and 20 oncologic caregivers) participated. Median age (IQR) was 57 years (47-66) and 71.46% were women. Self-perceived stress was higher in oncologic caregivers than geriatric caregivers in all psychometric test analyzed (Wilcoxon Rank Sum test, p value < .05). Glucose concentrations and glycated hemoglobin levels differed statistically among groups (Kruskal-Wallis test (K-W tests), p value < .05), even though the median levels were not clinically relevant. Levels of other biomarkers did not differ significantly (K-W tests, p value > .05). These findings suggest that perceived stress is not homogeneous in the caregivers community and thus these two groups could be differentiated. These results provide the baseline information to initiate social actions addressed to each group of caregivers to increase their wellbeing.

Background: Changes in lifestyle, finances and work status during COVID-19 lockdowns may have led to biopsychosocial changes in people with pre-existing vulnerabilities such as Major Depressive Disorders (MDDs) and Multiple Sclerosis (MS). Methods: Data were collected as a part of the RADAR-CNS (Remote Assessment of Disease and Relapse—Central Nervous System) program. We analyzed the following data from long-term participants in a decentralized multinational study: symptoms of depression, heart rate (HR) during the day and night; social activity; sedentary state, steps and physical activity of varying intensity. Linear mixed-effects regression analyses with repeated measures were fitted to assess the changes among three time periods (pre, during and post-lockdown) across the groups, adjusting for depression severity before the pandemic and gender. Results: Participants with MDDs (N = 255) and MS (N = 214) were included in the analyses. Overall, depressive symptoms remained stable across the three periods in both groups. A lower mean HR and HR variation were observed between pre and during lockdown during the day for MDDs and during the night for MS. HR variation during rest periods also decreased between pre- and post-lockdown in both clinical conditions. We observed a reduction in physical activity for MDDs and MS upon the introduction of lockdowns. The group with MDDs exhibited a net increase in social interaction via social network apps over the three periods. Conclusions: Behavioral responses to the lockdown measured by social activity, physical activity and HR may reflect changes in stress in people with MDDs and MS. Remote technology monitoring might promptly activate an early warning of physical and social alterations in these stressful situations. Future studies must explore how stress does or does not impact depression severity.

This study aimed to explore the physiological dynamics of cognitive stress in patients with Major Depressive Disorder (MDD) and design a multiparametric model for objectively measuring severity of depression. Physiological signal recordings from 40 MDD patients and 40 healthy controls were collected in a baseline stage, in a stress‐inducing stage using two cognitive tests, and in the recovery period. Several features were extracted from electrocardiography, photoplethysmography, electrodermal activity, respiration, and temperature. Differences between values of these features under different conditions were used as indexes of autonomic reactivity and recovery. Finally, a linear model was designed to assess MDD severity, using the Beck Depression Inventory scores as the outcome variable. The performance of this model was assessed using the MDD condition as the response variable. General physiological hyporeactivity and poor recovery from stress predict depression severity across all physiological signals except for respiration. The model to predict depression severity included gender, body mass index, cognitive scores, and mean heart rate recovery, and achieved an accuracy of 78%, a sensitivity of 97% and a specificity of 59%. There is an observed correlation between the behavior of the autonomic nervous system, assessed through physiological signals analysis, and depression severity. Our findings demonstrated that decreased autonomic reactivity and recovery are linked with an increased level of depression. Quantifying the stress response together with a cognitive evaluation and personalization variables may facilitate a more precise diagnosis and monitoring of depression, enabling the tailoring of therapeutic interventions to individual patient needs.