Measuring psychosocial stress with heart rate variability-based methods in different health and age groups

Cardiopulmonary and metabolic responses during a 2-day CPET in myalgic encephalomyelitis/chronic fatigue syndrome: translating reduced oxygen consumption to impairment status to treatment considerations

BACKGROUND

Post-exertional malaise (PEM), the hallmark symptom of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), represents a constellation of abnormal responses to physical, cognitive, and/or emotional exertion including profound fatigue, cognitive dysfunction, and exertion intolerance, among numerous other maladies. Two sequential cardiopulmonary exercise tests (2-d CPET) provide objective evidence of abnormal responses to exertion in ME/CFS but validated only in studies with small sample sizes. Further, translation of results to impairment status and approaches to symptom reduction are lacking.

METHODS

Participants with ME/CFS (Canadian Criteria; n = 84) and sedentary controls (CTL; n = 71) completed two CPETs on a cycle ergometer separated by 24 h. Two-way repeated measures ANOVA compared CPET measures at rest, ventilatory/anaerobic threshold (VAT), and peak effort between phenotypes and CPETs. Intraclass correlations described stability of CPET measures across tests, and relevant objective CPET data indicated impairment status. A subset of case-control pairs (n = 55) matched for aerobic capacity, age, and sex, were also analyzed.

RESULTS

Unlike CTL, ME/CFS failed to reproduce CPET-1 measures during CPET-2 with significant declines at peak exertion in work, exercise time, V ˙ e, V ˙ O2, V ˙ CO2, V ˙ T, HR, O2pulse, DBP, and RPP. Likewise, CPET-2 declines were observed at VAT for V ˙ e/ V ˙ CO2, PetCO2, O2pulse, work, V ˙ O2 and SBP. Perception of effort (RPE) exceeded maximum effort criteria for ME/CFS and CTL on both CPETs. Results were similar in matched pairs. Intraclass correlations revealed greater stability in CPET variables across test days in CTL compared to ME/CFS owing to CPET-2 declines in ME/CFS. Lastly, CPET-2 data signaled more severe impairment status for ME/CFS compared to CPET-1.

CONCLUSIONS

Presently, this is the largest 2-d CPET study of ME/CFS to substantiate impaired recovery in ME/CFS following an exertional stressor. Abnormal post-exertional CPET responses persisted compared to CTL matched for aerobic capacity, indicating that fitness level does not predispose to exertion intolerance in ME/CFS. Moreover, contributions to exertion intolerance in ME/CFS by disrupted cardiac, pulmonary, and metabolic factors implicates autonomic nervous system dysregulation of blood flow and oxygen delivery for energy metabolism. The observable declines in post-exertional energy metabolism translate notably to a worsening of impairment status. Treatment considerations to address tangible reductions in physiological function are proffered.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov, retrospectively registered, ID# NCT04026425, date of registration: 2019-07-17.

Cardiorespiratory fitness is linked with heart rate variability during stress in "at-risk" adults

BACKGROUND

Physiological mechanisms explaining why cardiorespiratory fitness (CRF) predicts cardiovascular morbidity and mortality are incompletely understood. We examined if CRF modifies vagally mediated heart rate variability (HRV) during acute physical or psychosocial stress or night-time sleep in adults with cardiovascular risk factors.

METHODS

Seventy-eight adults (age 56 years [IQR 50-60], 74% female, body mass index 28 kg/m2 [IQR 25-31]) with frequent cardiovascular risk factors participated in this cross-sectional study. They went through physical (treadmill cardiopulmonary exercise test [CPET]) and psychosocial (Trier Social Stress Test for Groups [TSST-G]) stress tests and night-time sleep monitoring (polysomnography). Heart rate (HR) and vagally mediated HRV (root mean square of successive differences between normal R-R intervals [RMSSD]) were recorded during the experiments and analyzed by taking account of potential confounders.

RESULTS

CRF (peak O2 uptake) averaged 99% (range 78-126) in relation to reference data. From pre-rest to moderate intensities during CPET and throughout TSST-G, HR did not differ between participants with CRF below median (CRFlower) and CRF equal to or above median (CRFhigher), whereas CRFhigher had higher HRV than CRFlower, and CRF correlated positively with HRV in all participants. Meanwhile, CRF had no independent associations with HR or HRV levels during slow-wave sleep, the presence of metabolic syndrome was not associated with recorded HR or HRV levels, and single factors predicted HRV responsiveness independently only to limited extents.

CONCLUSIONS

CRF is positively associated with prevailing vagally mediated HRV at everyday levels of physical and psychosocial stress in adults with cardiovascular risk factors.

Continuous real-time assessment of acute cognitive stress from cardiac mechanical signals captured by a skin-like patch

The inability to objectively quantify cognitive stress in real-time with wearable devices is a crucial unsolved problem with serious negative consequences for dementia and mental disability patients and those seeking to improve their quality of life. Here, we introduce a skin-like, wireless sternal patch that captures changes in cardiac mechanics due to stress manifesting in the seismocardiogram (SCG) signals. Judicious optimization of the device's micro-structured interconnections and elastomer integration yields a device that sufficiently matches the skin's mechanics, robustly yet gently adheres to the skin without aggressive tapes, and captures planar and longitudinal SCG waves well. The device transmits SCG beats in real-time to a user's device, where derived features relate to the heartbeat's mechanical morphology. The signals are assessed by a series of features in a support vector machine regressor. Controlled studies, compared to gold standard cortisol and following the validated imaging test, show an R-squared correlation of 0.79 between the stress prediction and cortisol change, significantly improving over prior works. Likewise, the system demonstrates excellent robustness to external temperature and physical recovery status while showing excellent accuracy and wearability in full-day use.

Does Osteopathic Heart-Focused Palpation Modify Heart Rate Variability in Stressed Participants with Musculoskeletal Pain? A Randomised Controlled Pilot Study

Heart rate variability (HRV) describes fluctuations in time intervals between heartbeats and reflects autonomic activity. HRV is reduced in stressed patients with musculoskeletal pain and improved after osteopathic manipulative treatment and mind-body interventions. Heart-focused palpation (HFP) combines manual and mind-body approaches to facilitate relaxation. This randomised controlled pilot study investigated the feasibility and sample size for a future randomised controlled trial and the effect of a single treatment with HFP or sham HFP (SHAM) on short-term HRV. A total of Thirty-three adults (47.7 ± 13.5 years old) with stress and musculoskeletal pain completed the trial with acceptable rates of recruitment (8.25 subjects per site/month), retention (100%), adherence (100%), and adverse events (0%). HFP (n = 18), but not SHAM (n = 15), significantly increased the root mean square of successive RR interval differences (p = 0.036), standard deviation of the NN intervals (p = 0.009), and ratio of the low-frequency to high-frequency power band (p = 0.026). HFP and SHAM significantly decreased the heart rate (p < 0.001, p = 0.009) but not the stress index and ratio of the Poincaré plot standard deviation along and perpendicular to the line of identity (p > 0.05). A power analysis calculated 72 participants. Taken together, the study was feasible and HFP improved HRV in stressed subjects with musculoskeletal pain, suggesting a parasympathetic effect.

Physiological sensor data cleaning with autoencoders

Objective.Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients' vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics.Approach.In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability.Main results.Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates.Significance.Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials.

Physiological reactivity to acute mental stress in essential hypertension-a systematic review

Objective

Exaggerated physiological reactions to acute mental stress (AMS) are associated with hypertension (development) and have been proposed to play an important role in mediating the cardiovascular disease risk with hypertension. A variety of studies compared physiological reactivity to AMS between essential hypertensive (HT) and normotensive (NT) individuals. However, a systematic review of studies across stress-reactive physiological systems including intermediate biological risk factors for cardiovascular diseases is lacking.

Methods

We conducted a systematic literature search (PubMed) for original articles and short reports, published in English language in peer-reviewed journals in November and December 2022. We targeted studies comparing the reactivity between essential HT and NT to AMS in terms of cognitive tasks, public speaking tasks, or the combination of both, in at least one of the predefined stress-reactive physiological systems.

Results

We included a total of 58 publications. The majority of studies investigated physiological reactivity to mental stressors of mild or moderate intensity. Whereas HT seem to exhibit increased reactivity in response to mild or moderate AMS only under certain conditions (i.e., in response to mild mental stressors with specific characteristics, in an early hyperkinetic stage of HT, or with respect to certain stress systems), increased physiological reactivity in HT as compared to NT to AMS of strong intensity was observed across all investigated stress-reactive physiological systems.

Conclusion

Overall, this systematic review supports the proposed and expected generalized physiological hyperreactivity to AMS with essential hypertension, in particular to strong mental stress. Moreover, we discuss potential underlying mechanisms and highlight open questions for future research of importance for the comprehensive understanding of the observed hyperreactivity to AMS in essential hypertension.

Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?

Chronic stress has become an epidemic with negative health risks including cardiovascular disease, hypertension, and diabetes. Traditional methods of stress measurement and monitoring typically relies on self-reporting. However, wearable smart technologies offer a novel strategy to continuously and non-invasively collect objective health data in the real-world. A novel electrocardiogram (ECG) feature has recently been introduced to the Apple Watch device. Interestingly, ECG data can be used to derive Heart Rate Variability (HRV) features commonly used in the identification of stress, suggesting that the Apple Watch ECG app could potentially be utilized as a simple, cost-effective, and minimally invasive tool to monitor individual stress levels. Here we collected ECG data using the Apple Watch from 36 health participants during their daily routines. Heart rate variability (HRV) features from the ECG were extracted and analyzed against self-reported stress questionnaires based on the DASS-21 questionnaire and a single-item LIKERT-type scale. Repeated measures ANOVA tests did not find any statistical significance. Spearman correlation found very weak correlations (p < 0.05) between several HRV features and each questionnaire. The results indicate that the Apple Watch ECG cannot be used for quantifying stress with traditional statistical methods, although future directions of research (e.g., use of additional parameters and Machine Learning) could potentially improve stress quantification with the device.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review

INTRODUCTION

Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress.

OBJECTIVE

The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face.

METHODS

This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2].

RESULTS

A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability.

CONCLUSION

Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.

Study on the Psychological States of Olfactory Stimuli Using Electroencephalography and Heart Rate Variability

In the modern information society, people are constantly exposed to stress due to complex work environments and various interpersonal relationships. Aromatherapy is attracting attention as one of the methods for relieving stress using aroma. A method to quantitatively evaluate such an effect is necessary to clarify the effect of aroma on the human psychological state. In this study, we propose a method of using two biological indexes, electroencephalogram (EEG) and heart rate variability (HRV), to evaluate human psychological states during the inhalation of aroma. The purpose is to investigate the relationship between biological indexes and the psychological effect of aromas. First, we conducted an aroma presentation experiment using seven different olfactory stimuli while collecting data from EEG and pulse sensors. Next, we extracted the EEG and HRV indexes from the experimental data and analyzed them with respect to the olfactory stimuli. Our study found that olfactory stimuli have a strong effect on psychological states during aroma stimuli and that the human response to olfactory stimuli is immediate but gradually adapts to a more neutral state. The EEG and HRV indexes showed significant differences between aromas and unpleasant odors especially for male participants in their 20-30s, while the delta wave and RMSSD indexes showed potential for generalizing the method to evaluate psychological states influenced by olfactory stimuli across genders and generations. The results suggest the possibility of using EEG and HRV indexes to evaluate psychological states toward olfactory stimuli such as aroma. In addition, we visualized the psychological states affected by the olfactory stimuli on an emotion map, suggesting an appropriate range of EEG frequency bands for evaluating psychological states applied to the olfactory stimuli. The novelty of this research lies in our proposed method to provide a more detailed picture of the psychological responses to olfactory stimuli using the integration of biological indexes and emotion map, which contributes to the areas such as marketing and product design by providing insights into the emotional responses of consumers to different olfactory products.