Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study

Designing and developing a prescription digital therapeutic for at-home heart rate variability biofeedback to support and enhance patient outcomes in post-traumatic stress disorder treatment

Post-traumatic stress disorder (PTSD) is a psychiatric condition producing considerable distress, dysfunction, and impairment in affected individuals. While various forms of psychotherapy are commonly utilized in PTSD treatment, the known neurological pathologies associated with PTSD are insufficiently addressed by these conventional approaches. Heart rate variability biofeedback (HRV-BFB) is a promising tool for correcting autonomic dysfunction in PTSD, with subsequent changes in clinically significant outcome measures. This paper outlines a systematic approach for the development, distribution, and implementation of a prescription at-home HRV-BFB digital therapeutic. We provide recommendations for evidence-generation strategies and propose appropriate regulatory pathways within existing frameworks. Widespread access to HRV-BFB could potentially reduce the distress, disability, and healthcare burden associated with PTSD. Promoting HRV-BFB as a primary intervention could also serve to reduce the stigma associated with "mental" illness and increase health literacy regarding the neuroimmune impacts of psychosocial factors. These processes might in turn improve treatment-seeking, adherence, and supported self-management of these conditions.

Quantifying Stress and Relaxation: A New Measure of Heart Rate Variability as a Reliable Biomarker

Background/Objectives: For the rapid, objective characterization of the physiological stress response, there is currently no generally recognized standard. The stress measurement methods used in practice (e.g., for psychological measures of stress) are often subjective, or in the case of biological markers (e.g., cortisol, amylase), they usually require a blood test. For this reason, the use of heart rate variability (HRV) to characterize stress has recently come to the fore. HRV is the variability in the length of heartbeat intervals, which indicates the ability of the heart to respond to various physiological and environmental stimuli. However, the conventional HRV metrics are not corrected for heart rate dependence; hence, they fail to fully account for the complex physiology of stress and relaxation. In order to remedy this problem, here we introduce a novel HRV parameter, the normalized variability derived from an RMSSD "Master Curve", and we compare it with the conventional metrics. Methods: In Study 1, the relaxation state was induced either by heart rate variability biofeedback training (N = 21) or by habitual relaxation (N = 21), while in Study 2 (N = 9), the Socially Evaluated Cold Pressor Test and the Socially Evaluated Stroop Test were used to induce stress in the subject. For a statistical evaluation of the data, the Kolmogorov-Smirnov test was used to compare the distributions of mean HR, log(RMSSD), log(SDNN), and normalized variability before, during, and after relaxation and stress. Results: The results of this study indicate that while log(RMSSD) and log(SDNN) did not change significantly, the normalized variability did undergo a significant change both in relaxation states and in stress states induced by the Socially Evaluated Cold Pressor Test. Conclusions: Overall, we suggest this novel type of normalized variability ought to be used as a sensitive stress indicator, and in general, for the characterization of the complex processes of the vegetative nervous system.

Effects of Heart Rate Variability Biofeedback Training on Anxiety Reduction and Brain Activity: a Randomized Active-Controlled Study Using EEG

Heart rate variability biofeedback (HRVBF) is a promising anxiety-reducing intervention that increases vagally-mediated heart rate variability (vmHRV) through slow-paced breathing and feedback of heart rhythm. Several studies have reported the anxiety-reducing effects of HRVBF; however, some studies have reported such training as ineffective. Furthermore, the effects of training and underlying brain activity changes remain unclear. This study examined the anxiety-reducing effects of HRVBF training and related brain activity changes by randomly assigning participants, employing an active control group, and measuring anxiety-related attentional bias using the emotional Stroop task and electroencephalography (EEG). Fifty-five healthy students with anxiety were randomly assigned to the HRVBF or control groups, and 21 in the HRVBF group and 19 in the control group were included in the analysis. Both groups performed 10 training sessions of 20 min each within 3 weeks. They were assessed using resting vmHRV, event-related potential (ERP), time-frequency EEG, attentional bias, and the State-Trait Anxiety Inventory-JYZ (STAI-JYZ) before and after training. The results demonstrated increased resting vmHRV in the HRVBF group compared to the control group after training. However, no differences were observed in ERP, time-frequency EEG, attentional bias, and STAI-JYZ. Participants with higher pre-training resting vmHRV achieved higher heart rhythm coherence in HRVBF training and had reduced attentional bias. This study suggests that individuals with higher resting vmHRV are more likely to be proficient in HRVBF training and benefit from its anxiety-reducing effects. The findings contribute to participant selection to benefit from HRVBF training and modification of the training protocols for non-responders.Clinical trial registrationOrganization: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR), JapanRegistration number: UMIN000047096Registration date: March 6, 2022.

Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study

BACKGROUND

Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress.

OBJECTIVE

This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress.

METHODS

This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed.

RESULTS

Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024.

CONCLUSIONS

This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies.

TRIAL REGISTRATION

OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51298.

The Future of Stress Management: Integration of Smartwatches and HRV Technology

In the modern world, stress has become a pervasive concern that affects individuals' physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data's advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.