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

COVID-19 VR digital health implementation distress moral distress nursing oura ring stress virtual reality wearable web-based platform

Journal

JMIR research protocols
ISSN: 1929-0748
Titre abrégé: JMIR Res Protoc
Pays: Canada
ID NLM: 101599504

Informations de publication

Date de publication:
06 May 2024
Historique:
received: 01 12 2023
accepted: 06 03 2024
revised: 04 03 2024
medline: 6 5 2024
pubmed: 6 5 2024
entrez: 6 5 2024
Statut: epublish

Résumé

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. 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. 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. The study was funded in 2022 and received research ethics board approval in April 2023. The study is ongoing. 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. ClinicalTrials.gov NCT05923398; https://clinicaltrials.gov/study/NCT05923398. DERR1-10.2196/54180.

Sections du résumé

BACKGROUND 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 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 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 RESULTS
The study was funded in 2022 and received research ethics board approval in April 2023. The study is ongoing.
CONCLUSIONS 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 BACKGROUND
ClinicalTrials.gov NCT05923398; https://clinicaltrials.gov/study/NCT05923398.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) UNASSIGNED
DERR1-10.2196/54180.

Identifiants

pubmed: 38709554
pii: v13i1e54180
doi: 10.2196/54180
doi:

Banques de données

ClinicalTrials.gov
['NCT05923398']

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e54180

Informations de copyright

©Josh Martin, Alice Rueda, Gyu Hee Lee, Vanessa K Tassone, Haley Park, Martin Ivanov, Benjamin C Darnell, Lindsay Beavers, Douglas M Campbell, Binh Nguyen, Andrei Torres, Hyejung Jung, Wendy Lou, Anthony Nazarov, Andrea Ashbaugh, Bill Kapralos, Brett Litz, Rakesh Jetly, Adam Dubrowski, Gillian Strudwick, Sridhar Krishnan, Venkat Bhat. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 06.05.2024.

Auteurs

Josh Martin (J)

Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Alice Rueda (A)

Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Gyu Hee Lee (GH)

Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Vanessa K Tassone (VK)

Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Haley Park (H)

Interventional Psychiatry Program, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.

Martin Ivanov (M)

Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Benjamin C Darnell (BC)

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 (L)

Allan Waters Family Simulation Program, Unity Health Toronto, Toronto, ON, Canada.
Department of Physical Therapy, University of Toronto, Toronto, ON, Canada.

Douglas M Campbell (DM)

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 (B)

Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Andrei Torres (A)

maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada.

Hyejung Jung (H)

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Wendy Lou (W)

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Anthony Nazarov (A)

MacDonald Franklin OSI Research Centre, Lawson Health Research Institute, London, ON, Canada.

Andrea Ashbaugh (A)

School of Psychology, University of Ottawa, Ottawa, ON, Canada.

Bill Kapralos (B)

maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada.

Brett Litz (B)

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 (R)

Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.

Adam Dubrowski (A)

maxSIMhealth Group, Ontario Tech University, Oshawa, ON, Canada.

Gillian Strudwick (G)

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 (S)

Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Venkat Bhat (V)

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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Classifications MeSH