A Systematic Review of Heart Rate Variability as a Measure of Stress in Medical Professionals

LieRHRV system for remote lie detection using heart rate variability parameters

The standard polygraph, or lie detector, is limited by its reliance on average heart rate, subjective examiner interpretation, and the need for direct subject contact. Remote photoplethysmography (rPPG) offers a promising contactless alternative, by using facial videos to extract heart rate variability (HRV). We introduce "LieRHRV," a remote lie detection algorithm based solely on extracted HRV parameters. To test the HRV parameter quality, we compared these parameters to HRV parameters extracted from ECG and photoplethysmography (PPG) records archived in five gold-standard ECG/PPG datasets. A prospective study of 39 healthy volunteers was also performed to evaluate the accuracy of lie detection based on PPG- or rPPG-derived HRV parameters. Effective HRV parameter extraction from both PPG and ECG sources was demonstrated, with comparable outcomes among 60% of the parameters on average with the publicly available datasets, and prospective study with 80% of the parameters. LieRHRV performance on ECG, PPG or rPPG (with parameters selected for PPG) exhibited an accuracy of 83.3 ± 3%, 87.3 ± 4% or 91.7 ± 3.5%, respectively. In comparison, the naïve model for ECG, PPG or rPPG data achieved an accuracy of 58.3 ± 3%, 61.0 ± 3% or 67.0 ± 5%, respectively. This study demonstrated the feasibility and effectiveness of LieRHRV, and offers a promising avenue for advancing lie detection technologies beyond polygraph limitations.

Evaluating the role of heart rate variability in monitoring stress and sleep quality among nurses in the aftermath of the COVID-19 pandemic

AIM

To assess heart rate variability (HRV) as a measure to assess job stress and sleep quality among nurses in the post-COVID-19 period.

BACKGROUND

The COVID-19 pandemic significantly affected nurses, with heightened job stress and impaired sleep quality impacting their well-being and effectiveness in patient care. HRV could offer insights for supporting strategies in the pandemic aftermath.

DESIGN

A quantitative cross-sectional study.

METHODS

This study involved 403 clinical nurses recruited from a teaching hospital in Taiwan. Data on job stress, work frustration, sleep quality and HRV were collected and analysed.

RESULTS

Among the nurses surveyed during the COVID-19 pandemic, 72.7% reported poor sleep quality (PSQI = 9.369). Job stress emerged as a strong predictor of work frustration. High stress levels and poor sleep quality were correlated with significantly decreased HRV, indicating a potential physiological impact of stress on the nurses' health and well-being.

CONCLUSIONS

HRV is a valuable and cost-effective measure for monitoring and managing nurses' well-being in the post-COVID-19 era. Targeted interventions can be implemented to support nurses' overall performance and promote their well-being by identifying those at high risk of job stress and poor sleep quality.

CogWatch: An open-source platform to monitor physiological indicators for cognitive workload and stress

Cognitive workload is a measure of the mental resources a user is dedicating to a given task. Low cognitive workload produces boredom and decreased vigilance, which can lead to an increase in response time. Under high cognitive workload the information processing burden of the user increases significantly, thereby compromising the ability to effectively monitor their environment for unexpected stimuli or respond to emergencies. In cognitive workload and stress monitoring research, sensors are used to measure applicable physiological indicators to infer the state of user. For example, electrocardiography or photoplethysmography are often used to track both the rate at which the heart beats and variability between the individual heart beats. Photoplethysmography and chest straps are also used in studies to track fluctuations in breathing rate. The Galvanic Skin Response is a change in sweat rate (especially on the palms and wrists) and is typically measured by tracking how the resistance of two probes at a fixed distance on the subject's skin changes over time. Finally, fluctuations in Skin Temperature are typically tracked with thermocouples or infrared light (IR) measuring systems in these experiments. While consumer options such a smartwatches for health tracking often have the integrated ability to perform photoplethysmography, they typically perform significant processing on the data which is not transparent to the user and often have a granularity of data that is far too low to be useful for research purposes. It is possible to purchase sensor boards that can be added to Arduino systems, however, these systems generally are very large and obtrusive. Additionally, at the high end of the spectrum there are medical tools used to track these physiological signals, but they are often very expensive and require specific software to be licensed for communication. In this paper, an open-source solution to create a physiological tracker with a wristwatch form factor is presented and validated, using conventional off-the-shelf components. The proposed tool is intended to be applied as a cost-effective solution for research and educational settings.

Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach

Objectives

Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reflecting specific adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks.

Methods

Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions.

Results

Cluster analysis of pupillometry data identified two distinct groups with statistically significant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower self-esteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1. Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were significant between the clusters.

Conclusion

The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulation, cognitive load, and behavioral responses to cognitive load and deceptive feedback. These findings underscore the potential of pupillometry combined with machine learning in identifying individual differences in stress resilience and cognitive performance. Our research on pupillary dynamics and ANS patterns can lead to the development of remote diagnostic tools for real-time cognitive stress monitoring and performance optimization, applicable in clinical, educational, and occupational settings.

The Effects of a Complex Interactive Multimodal Intervention on Personalized Stress Management Among Health Care Workers in China: Nonrandomized Controlled Study

BACKGROUND

Health care workers (HCWs) frequently face multiple stressors at work, particularly those working night shifts. HCWs who have experienced distress may find it difficult to adopt stress management approaches, even if they are aware of the effects of stress and coping processes. Therefore, an individualized intervention may be required to assist distressed HCWs in bridging the "knowledge-practice" gap in stress management and effectively alleviating stress symptoms.

OBJECTIVE

The main objective of this research was to compare the effects of a complex interactive multimodal intervention (CIMI) to self-guided stress management interventions on stress symptoms of distressed HCWs, as measured by physiological (heart rate variability), psychological (perceived stress, mental distress, and subjective happiness), and sleep disorder (fatigue and sleepiness) indicators.

METHODS

We conducted a nonrandomized, controlled study in 2 Chinese general hospitals. The participants in this study were 245 HCWs who fulfilled at least 1 of the 3 dimensions on the Depression, Anxiety, and Stress Scale. All eligible individuals were required to complete a questionnaire and wear a 24-hour Holter device to determine the physiological signs of stress as indexed by heart rate variability at both baseline and after the intervention. The CIMI group received a 12-week online intervention with 4 components-mobile stress management instruction, a web-based WeChat social network, personalized feedback, and a nurse coach, whereas the control group simply received a self-guided intervention.

RESULTS

After a 12-week intervention, the Perceived Stress Scale (PSS) scores reduced significantly in the CIMI group (mean difference [MD] -5.31, 95% CI -6.26 to -4.37; P<.001) compared to the baseline levels. The changes in PSS scores before and after the intervention exhibited a significant difference between the CIMI and control groups (d=-0.64; MD -4.03, 95% CI -5.91 to -2.14; P<.001), and the effect was medium. In terms of physiological measures, both the control group (MD -9.56, 95% CI -16.9 to -2.2; P=.01) and the CIMI group (MD -8.45, 95% CI -12.68 to -4.22; P<.001) demonstrated a significant decrease in the standard deviation of normal-to-normal intervals (SDNN) within the normal clinical range; however, there were no significant differences between the 2 groups (d=0.03; MD 1.11, 95% CI -7.38 to 9.59; P=.80).

CONCLUSIONS

The CIMI was an effective intervention for improving sleep disorders, as well as parts of the psychological stress measures in distressed HCWs. The findings provide objective evidence for developing a mobile stress management intervention that is adaptable and accessible to distressed HCWs, but its long-term effects should be investigated in future research.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05239065; https://clinicaltrials.gov/ct2/show/NCT05239065.

Psychophysiological wellbeing in a class of dental students attending dental school: anxiety, burnout, post work executive performance and a 24 hours physiological investigation during a working day

Aim

To the best of our knowledge, dental school students have never been evaluated for stress, anxiety, burnout, physiological indexes during a 24-h working day, and executive function performance post-work and post-work after returning from vacation; therefore, this research has been conducted.

Methods

Data were acquired at the Dental School of the University of Padua on 16 students in their 4th year, far from the exam session. While performing clinical activity on the dental chair and during a working day, electrodermal activity (EDA), heart rate variability (HRV), and heart rate (HR) were recorded. Participants' stress was measured with the Perceived Stress Scale (PSS-10 scale) and anxiety with the General Anxiety Disorder Questionnaire (GAD-7) and State-Trait Anxiety Inventory (STAI-Y-2), while burnout with the Maslach Burnout Inventory (MBI-HSS). Executive functions were evaluated using the Tower of London test (TOL-R).

Results

Three students (2F/1M) had a GAD-7 score ≥ 10. Five students (4F/1M) showed trait anxiety. Moderate levels of perceived stress were reported in 85% of participants. MBI-HSS showed that 7 participants scored high on emotional exhaustion and 7 on depersonalization. TOL-R performance (M = 15.85, SD = 4.01) was below the normative value p < 0.00001. A second test, after the holidays, showed normal values. EDA was higher during children's treatment (p < 0.05), ANOVA showed high HR during working time (p < 0.001), and HRV was higher in males (p < 0.001).

Conclusion

Based on the sample size evaluated, it is reported that being a dental student has a moderate impact on stress, anxiety, and burnout while a strong impact on executive functions buffered by rest.

Relationship between dust allergen sensitization and cardiac autonomic function in patients with chronic obstructive pulmonary disease

OBJECTIVE

Cardiac autonomic function predicts cardiovascular disease risk. The aim of this study was to investigate the relationship between sensitization to dust allergens and cardiac autonomic dysfunction in patients with chronic obstructive pulmonary disease (COPD), and to provide new ideas for the prevention of cardiovascular complications in these patients.

METHODS

Immunoassays for sensitization to cats/dogs, cockroaches and dust mites were performed in 840 patients with COPD. Indicators of heart rate variability in these patients were used to assess cardiac autonomic function, including standard deviation of normal-to-normal intervals (SDNN), root-mean square of successive differences between normal-to-normal intervals (RMSSD), low-frequency power (LF), high-frequency power (HF), and LF/HF ratios, which were obtained based on ambulatory electrocardiographic monitoring data. The relationship between sensitization to these dust allergens and heart rate variability was explored using multivariate logistic regression.

FINDINGS

The multivariate analyses showed that sensitization to total allergens was associated with reduced levels of SDNN, RMSSD, LF and HF and with increased levels of the LF/HF ratio in the patients with COPD (p < .05).

CONCLUSION

Dust allergen sensitization may be associated with cardiac autonomic dysfunction in patients with COPD. Whether desensitization can prevent cardiovascular complications in these patients should be further explored.

Using Digital Biomarkers for Objective Assessment of Perfusionists' Workload and Acute Stress During Cardiac Surgery

The cardiac operating room (OR) is a high-risk, high-stakes environment inserted into a complex socio-technical healthcare system. During cardiopulmonary bypass (CPB), the most critical phase of cardiac surgery, the perfusionist has a crucial role within the interprofessional OR team, being responsible for optimizing patient perfusion while coordinating other tasks with the surgeon, anesthesiologist, and nurses. The aim of this study was to investigate objective digital biomarkers of perfusionists' workload and stress derived from heart rate variability (HRV) metrics captured via a wearable physiological sensor in a real cardiac OR. We explored the relationships between several HRV parameters and validated self-report measures of surgical task workload (SURG-TLX) and acute stress (STAI-SF), as well as surgical processes and outcome measures. We found that the frequency-domain HRV parameter HF relative power - FFT (%) presented the strongest association with task workload (correlation coefficient: -0.491, p-value: 0.003). We also found that the time-domain HRV parameter RMSSD (ms) presented the strongest correlation with perfusionists' acute stress (correlation coefficient: -0.489, p-value: 0.005). A few workload and stress biomarkers were also associated with bypass time and patient length of stay in the hospital. The findings from this study will inform future research regarding which HRV-based biomarkers are best suited for the development of cognitive support systems capable of monitoring surgical workload and stress in real time.