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Martin J, Rueda A, Lee GH, Tassone VK, Park H, Ivanov M, Darnell BC, Beavers L, Campbell DM, Nguyen B, Torres A, Jung H, Lou W, Nazarov A, Ashbaugh A, Kapralos B, Litz B, Jetly R, Dubrowski A, Strudwick G, Krishnan S, Bhat V. Digital Interventions to Understand and Mitigate Stress Response: Protocol for Process and Content Evaluation of a Cohort Study. JMIR Res Protoc 2024; 13:e54180. [PMID: 38709554 PMCID: PMC11106701 DOI: 10.2196/54180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 05/07/2024] Open
Abstract
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.
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Affiliation(s)
- Josh Martin
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Alice Rueda
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Gyu Hee Lee
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Vanessa K Tassone
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Haley Park
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Martin Ivanov
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Benjamin C Darnell
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, ON, Canada
- Department of Physical Therapy, University of Toronto, Toronto, ON, Canada
| | - Douglas M Campbell
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, ON, Canada
- Neonatal Intensive Care Unit, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Binh Nguyen
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Andrei Torres
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Hyejung Jung
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anthony Nazarov
- MacDonald Franklin OSI Research Centre, Lawson Health Research Institute, London, ON, Canada
| | - Andrea Ashbaugh
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Bill Kapralos
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Brett Litz
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, United States
- Department of Psychiatry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Rakesh Jetly
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | - Adam Dubrowski
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Gillian Strudwick
- Centre For Addiction & Mental Health, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Venkat Bhat
- Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Torres A, Nguyen B, Kapralos B, Krishnan S, Campbell DM, Beavers L, Dubrowski A, Bhat V. Development and Implementation of a Stress Monitoring Paradigm Using Virtual Reality Simulation During the COVID-19 Pandemic. Cureus 2024; 16:e53450. [PMID: 38435150 PMCID: PMC10909386 DOI: 10.7759/cureus.53450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Healthcare providers, particularly during the COVID-19 crisis, have been forced to make difficult decisions and have reported acting in ways that are contrary to their moral values, integrity, and professional commitments, given the constraints in their work environments. Those actions and decisions may lead to healthcare providers' moral suffering and distress. This work outlines the development of the Moral Distress Virtual Reality Simulator (Moral Distress VRS) to research stress and moral distress among healthcare workers during the COVID-19 pandemic. The Moral Distress VRS was developed based on the agile methodology framework, with three simultaneous development streams. It followed a two-week sprint cycle, ending with meetings with stakeholders and subject matter experts, whereby the project requirements, scope, and features were revised, and feedback was provided on the prototypes until reaching the final prototype that was deployed for in-person study sessions. The final prototype had two user interfaces (UIs), one for the participant and one for the researcher, with voice narration and customizable character models wearing medical personal protective equipment, and followed a tree-based dialogue scenario, outputting a video recording of the session. The virtual environment replicated an ICU nursing station and a fully equipped patient room. We present the development process that guided this project, how different teams worked together and in parallel, and detail the decisions and outcomes in creating each major component within a limited deadline. Finally, we list the most significant challenges and difficulties faced and recommendations on how to solve them.
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Affiliation(s)
- Andrei Torres
- maxSIMhealth Group, Ontario Tech University, Oshawa, CAN
| | - Binh Nguyen
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, CAN
| | - Bill Kapralos
- maxSIMhealth Group, Ontario Tech University, Oshawa, CAN
| | - Sridhar Krishnan
- Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, CAN
| | - Douglas M Campbell
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, CAN
- Neonatal Intensive Care Unit, St. Michael's Hospital, Toronto, CAN
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, CAN
- Pediatrics, University of Toronto, Toronto, CAN
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, CAN
- Physical Therapy, University of Toronto, Toronto, CAN
| | - Adam Dubrowski
- maxSIMhealth Group, Ontario Tech University, Oshawa, CAN
| | - Venkat Bhat
- Psychiatry and Interventional Psychiatry, St. Michael's Hospital, Toronto, CAN
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Espinola CW, Nguyen B, Torres A, Sim W, Rueda A, Beavers L, Campbell DM, Jung H, Lou W, Kapralos B, Peter E, Dubrowski A, Krishnan S, Bhat V. Digital Interventions for Stress Among Frontline Health Care Workers: Results From a Pilot Feasibility Cohort Trial. JMIR Serious Games 2024; 12:e42813. [PMID: 38194247 PMCID: PMC10783335 DOI: 10.2196/42813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 08/03/2023] [Accepted: 09/30/2023] [Indexed: 01/10/2024] Open
Abstract
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.
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Affiliation(s)
- Caroline W Espinola
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Binh Nguyen
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Andrei Torres
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Walter Sim
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Alice Rueda
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, ON, Canada
- Department of Physical Therapy, University of Toronto, Toronto, ON, Canada
| | - Douglas M Campbell
- Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, ON, Canada
- Neonatal Intensive Care Unit, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
- Department of Pediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Hyejung Jung
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Bill Kapralos
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Elizabeth Peter
- Lawrence S. Bloomberg, Faculty of Nursing, University of Toronto, Toronto, ON, Canada
| | - Adam Dubrowski
- maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
| | - Venkat Bhat
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Interventional Psychiatry Program, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
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Nguyen B, Torres A, Espinola CW, Sim W, Kenny D, Campbell DM, Lou W, Kapralos B, Beavers L, Peter E, Dubrowski A, Krishnan S, Bhat V. Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107645. [PMID: 37352806 PMCID: PMC10258128 DOI: 10.1016/j.cmpb.2023.107645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 05/19/2023] [Accepted: 06/04/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND AND OBJECTIVE Due to the constraints of the COVID-19 pandemic, healthcare workers have reported acting in ways that are contrary to their moral values, and this may result in moral distress. This paper proposes the novel digital phenotype profile (DPP) tool, developed specifically to evaluate stress experiences within participants. The DPP tool was evaluated using the COVID-19 VR Healthcare Simulation of Stress Experience (HSSE) dataset (NCT05001542), which is composed of passive physiological signals and active mental health questionnaires. The DPP tool focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with moral injury outcome scale (Brief MIOS). METHODS Data-driven techniques are encompassed to develop a tool for robust evaluation of distress among participants. To accomplish this, we applied pre-processing techniques which involved normalization, data sanitation, segmentation, and windowing. During feature analysis, we extracted domain-specific features, followed by feature selection techniques to rank the importance of the feature set. Prior to classification, we employed k-means clustering to group the Brief MIOS scores to low, moderate, and high moral distress as the Brief MIOS lacks established severity cut-off scores. Support vector machine and decision tree models were used to create machine learning models to predict moral distress severities. RESULTS Weighted support vector machine with leave-one-subject-out-cross-validation evaluated the separation of the Brief MIOS scores and achieved an average accuracy, precision, sensitivity, and F1 of 98.67%, 98.83%, 99.44%, and 99.13%, respectively. Various machine learning ablation tests were performed to support our results and further enhance the understanding of the predictive model. CONCLUSION Our findings demonstrate the feasibility to develop a DPP tool to predict distress experiences using a combination of mental health questionnaires and passive signals. The DPP tool is the first of its kind developed from the analysis of the HSSE dataset. Additional validation is needed for the DPP tool through replication in larger sample sizes.
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Affiliation(s)
- Binh Nguyen
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Andrei Torres
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Caroline W Espinola
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada
| | - Walter Sim
- Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada
| | - Deborah Kenny
- College of Nursing, University of Colorado Anschutz Medical Campus, Aurora 80045, United States
| | - Douglas M Campbell
- Neonatal Intensive Care Unit, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Pediatrics, University of Toronto, Toronto M5T 1P8, Canada; Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada; Allan Waters Family Simulation Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Bill Kapralos
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Lindsay Beavers
- Allan Waters Family Simulation Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Department of Physical Therapy, Temerty Faculty of Medicine, University of Toronto, Toronto M5T 1P8, Canada
| | - Elizabeth Peter
- Faculty of Nursing, University of Toronto, Toronto M5T 1P8, Canada
| | - Adam Dubrowski
- maxSIMhealth, Ontario Tech University, Oshawa, ON L1H 7K4, Canada
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Venkat Bhat
- Department of Psychiatry, University of Toronto, Toronto, ON M5S 1A1, Canada; Interventional Psychiatry Program, St. Michael's Hospital, Toronto M5B 1W8, Canada.
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Nguyen B, Torres A, Rueda A, Sim W, Campbell DM, Lou W, Kapralos B, Beavers L, Dubrowski A, Bhat V, Krishnan S. Digital Interventions to Reduce Distress Among Frontline Health Care Providers: Analysis of Self-Perceived Stress. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083372 DOI: 10.1109/embc40787.2023.10340958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
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.
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