Digital Interventions to Understand and Mitigate Stress Response: Protocol for Process and Content Evaluation of a Cohort Study.

BACKGROUND: Staffing and resource shortages, especially during the COVID-19 pandemic, have increased stress levels among health care workers. Many health care workers have reported feeling unable to maintain the quality of care expected within their profession, which, at times, may lead to moral distress and moral injury. Currently, interventions for moral distress and moral injury are limited. OBJECTIVE: This study has the following aims: (1) to characterize and reduce stress and moral distress related to decision-making in morally complex situations using a virtual reality (VR) scenario and a didactic intervention; (2) to identify features contributing to mental health outcomes using wearable, physiological, and self-reported questionnaire data; and (3) to create a personal digital phenotype profile that characterizes stress and moral distress at the individual level. METHODS: This will be a single cohort, pre- and posttest study of 100 nursing professionals in Ontario, Canada. Participants will undergo a VR simulation that requires them to make morally complex decisions related to patient care, which will be administered before and after an educational video on techniques to mitigate distress. During the VR session, participants will complete questionnaires measuring their distress and moral distress, and physiological data (electrocardiogram, electrodermal activity, plethysmography, and respiration) will be collected to assess their stress response. In a subsequent 12-week follow-up period, participants will complete regular assessments measuring clinical outcomes, including distress, moral distress, anxiety, depression, and loneliness. A wearable device will also be used to collect continuous data for 2 weeks before, throughout, and for 12 weeks after the VR session. A pre-post comparison will be conducted to analyze the effects of the VR intervention, and machine learning will be used to create a personal digital phenotype profile for each participant using the physiological, wearable, and self-reported data. Finally, thematic analysis of post-VR debriefing sessions and exit interviews will examine reoccurring codes and overarching themes expressed across participants' experiences. RESULTS: The study was funded in 2022 and received research ethics board approval in April 2023. The study is ongoing. CONCLUSIONS: It is expected that the VR scenario will elicit stress and moral distress. Additionally, the didactic intervention is anticipated to improve understanding of and decrease feelings of stress and moral distress. Models of digital phenotypes developed and integrated with wearables could allow for the prediction of risk and the assessment of treatment responses in individuals experiencing moral distress in real-time and naturalistic contexts. This paradigm could also be used in other populations prone to moral distress and injury, such as military and public safety personnel. TRIAL REGISTRATION: ClinicalTrials.gov NCT05923398; https://clinicaltrials.gov/study/NCT05923398. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/54180.

Amygdala biomarkers of treatment response in major depressive disorder: An fMRI systematic review of SSRI antidepressants.

Functional neuroimaging studies have demonstrated abnormal activity and functional connectivity (FC) of the amygdala among individuals with major depressive disorder (MDD), which may be rectified with selective serotonin reuptake inhibitor (SSRI) treatment. This systematic review aimed to identify changes in the amygdala on functional magnetic resonance imaging (fMRI) scans among individuals with MDD who received SSRIs. A search for fMRI studies examining amygdala correlates of SSRI response via fMRI was conducted through OVID (MEDLINE, PsycINFO, and Embase). The end date was April 4th, 2023. In total, 623 records were screened, and 16 studies were included in this review. While the search pertained to SSRIs broadly, the included studies were escitalopram-, citalopram-, fluoxetine-, sertraline-, and paroxetine-specific. Decreases in event-related amygdala activity were found following 6-to-12-week SSRI treatment, particularly in response to negative stimuli. Eight-week courses of SSRI pharmacotherapy were associated with increased event-related amygdala FC (i.e., with the prefrontal [PFC] and anterior cingulate cortices, insula, thalamus, caudate nucleus, and putamen) and decreased resting-state effective connectivity (i.e., amygdala-PFC). Preliminary evidence suggests that SSRIs may alter amygdala activity and FC in MDD. Additional studies are needed to corroborate findings. Future research should employ long-term follow-ups to determine whether effects persist after treatment termination.

Digital Interventions for Stress Among Frontline Health Care Workers: Results From a Pilot Feasibility Cohort Trial.

BACKGROUND: The COVID-19 pandemic has challenged the mental health of health care workers, increasing the rates of stress, moral distress (MD), and moral injury (MI). Virtual reality (VR) is a useful tool for studying MD and MI because it can effectively elicit psychophysiological responses, is customizable, and permits the controlled study of participants in real time. OBJECTIVE: This study aims to investigate the feasibility of using an intervention comprising a VR scenario and an educational video to examine MD among health care workers during the COVID-19 pandemic and to use our mobile app for longitudinal monitoring of stress, MD, and MI after the intervention. METHODS: We recruited 15 participants for a compound intervention consisting of a VR scenario followed by an educational video and a repetition of the VR scenario. The scenario portrayed a morally challenging situation related to a shortage of life-saving equipment. Physiological signals and scores of the Moral Injury Outcome Scale (MIOS) and Perceived Stress Scale (PSS) were collected. Participants underwent a debriefing session to provide their impressions of the intervention, and content analysis was performed on the sessions. Participants were also instructed to use a mobile app for 8 weeks after the intervention to monitor stress, MD, and mental health symptoms. We conducted Wilcoxon signed rank tests on the PSS and MIOS scores to investigate whether the VR scenario could induce stress and MD. We also evaluated user experience and the sense of presence after the intervention through semi-open-ended feedback and the Igroup Presence Questionnaire, respectively. Qualitative feedback was summarized and categorized to offer an experiential perspective. RESULTS: All participants completed the intervention. Mean pre- and postintervention scores were respectively 10.4 (SD 9.9) and 13.5 (SD 9.1) for the MIOS and 17.3 (SD 7.5) and 19.1 (SD 8.1) for the PSS. Statistical analyses revealed no significant pre- to postintervention difference in the MIOS and PSS scores (P=.11 and P=.22, respectively), suggesting that the experiment did not acutely induce significant levels of stress or MD. However, content analysis revealed feelings of guilt, shame, and betrayal, which relate to the experience of MD. On the basis of the Igroup Presence Questionnaire results, the VR scenario achieved an above-average degree of overall presence, spatial presence, and involvement, and slightly below-average realism. Of the 15 participants, 8 (53%) did not answer symptom surveys on the mobile app. CONCLUSIONS: Our study demonstrated VR to be a feasible method to simulate morally challenging situations and elicit genuine responses associated with MD with high acceptability and tolerability. Future research could better define the efficacy of VR in examining stress, MD, and MI both acutely and in the longer term. An improved participant strategy for mobile data capture is needed for future studies. TRIAL REGISTRATION: ClinicalTrails.gov NCT05001542; https://clinicaltrials.gov/study/NCT05001542. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32240.

Digital Interventions to Reduce Distress Among Frontline Health Care Providers: Analysis of Self-Perceived Stress.

Due to the constraints of the COVID-19 pandemic, healthcare workers have reported behaving in ways that are contrary to their values, which may result in distress and injury. This work is the first of its kind to evaluate the presence of stress in the COVID-19 VR Healthcare Simulation for Distress dataset. The dataset collected passive physiological signals and active mental health questionnaires. This paper focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with the Perceived Stress Scale (PSS)-10 questionnaire. The analysis involved data-driven techniques for a robust evaluation of stress among participants. Low-complexity pre-processing and feature extraction techniques were applied and support vector machine and decision tree models were created to predict the PSS-10 scores of users. Imbalanced data classification techniques were used to further enhance our understanding of the results. Decision tree with oversampling through Synthetic Minority Oversampling Technique achieved an accuracy, precision, recall, and F1 of 93.50%, 93.41%, 93.31%, and 93.35%, respectively. Our findings offer novel results and clinically valuable insights for stress detection and potential for translation to edge computing applications to enhance privacy, longitudinal monitoring, and simplify device requirements.

Contrasting the amygdala activity and functional connectivity profile between antidepressant-free participants with major depressive disorder and healthy controls: A systematic review of comparative fMRI studies.

Functional neuroimaging research suggests that the amygdala is implicated in the pathophysiology of major depressive disorder (MDD). This systematic review aimed to identify consistently reported amygdala activity and functional connectivity (FC) abnormalities in antidepressant-free participants with MDD as compared to healthy controls at baseline (i.e., before treatment initiation or experimental manipulation). A search for relevant published studies and registered clinical trials was conducted through OVID (MEDLINE, PsycINFO, and Embase) and ClinicalTrials.gov with an end date of March 7th, 2022. Fifty published studies and two registered clinical trials were included in this review. Participants with MDD frequently exhibited amygdala hyperactivity in response to negative stimuli, abnormal event-related amygdala-anterior cingulate cortex (ACC) FC, and abnormal resting-state amygdala FC with the insula and the prefrontal, temporal, and parietal cortices. Decreased resting-state FC was consistently found between the amygdala and the orbitofrontal cortex, striatum, cerebellum, and middle/inferior frontal gyri. Due to the limited number of studies examining resting-state amygdala activity and FC with specific subregions of interest, including those within the ACC, further investigation is warranted.

Feature Analysis and Hierarchical Classification of Anxiety Severity during early COVID-19.

Distress, confusion, and anger are common responses to COVID-19. Statistics Canada created the Canadian Perspectives Survey Series (CPSS) to understand social issues and effects of COVID-19 on the Canadian labour force (LF). The evaluation of the health and health-related behaviours were done through surveys collected between April and July. Features are composed of 4600 participants and 62 questions, which include the General Anxiety Disorder (GAD)-7 questionnaire. This work proposes the use of CPSS2 survey data characteristics to identify the level of anxiety within the Canadian population during early stages of COVID-19 and is validated with the use of GAD-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top 20 features to represent user anxiety. During classification, decision tree (DT) and support vector machine (SVM) are used to test the separation of anxiety severity. Hierarchical classification was used which separated the anxiety severity labels into different test sets and classified accordingly. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77±0.05%. After analysis, a subset of the reduced feature set can be represented as pseudo passive (PP) data, which are passive sensors that can augment qualitative data. The accurate classification provides proxy on what gives rise to anxiety, as well as the ability to provide early interventions. Future works can implement passive sensors to augment PP data and further understand why people cope this way.

Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game.

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.

Feature analysis of dysphonia speech for monitoring Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that has no known cure and no known prevention. Early detection is crucial in order to slow down the progress. In the past 10 years, interest in PD analysis has visibly increased. Speech impairment affects the majority of people with Parkinson's (PWP). New features and machine learning algorithms were proposed to help diagnose PD and to measure a patient's progress. Using sustained vowel /a/ recordings, we identified a more prominent set of Mel-Frequency Cepstral Coefficient (MFCC) and Intrinsic Mode Functions (IMF), and other parameters that can best represent the characteristics of Parkinson's dysphonia to assist with the diagnosis process. For higher quality audio signals, there is a visible difference in the higher MFCC coefficients, the wider spectrum bandwidth in the first four IMFs of PWP, and higher power intensity in the healthy subjects. We also found that even when the signals are downsampled into toll-quality, the distinguishable MFCC and IMF features were largely maintained. This enabled a whole possibility of providing telemedicine for PWP.