Abstract
Background: Caregivers of hospice patients, often family or close friends, provide continuous support for individuals with a life expectancy of six months or less. In 2022, 1.72 million Medicare beneficiaries received hospice care, most at home, with an average stay of 98 days. Caregivers manage medications, symptoms, personal care, emotional support, and provider coordination, placing them at high risk for fatigue, anxiety, depression, sleep disturbance, and burnout. Traditional surveys or single-point interviews often fail to capture the dynamic nature of distress, especially during nighttime care or crises.
Purpose: To characterize real-time patterns of physical and psychological distress among home hospice caregivers and identify contextual and caregiver-level predictors to inform future AI-guided interventions promoting caregiver well-being.
Framework: Guided by the Stress Process Model and the COM-B model, the study integrates caregiving demands, health outcomes, and behavioral determinants to design and interpret Ecological Momentary Assessment (EMA) data via mobile technology.
Methods: This three-phase, funded, IRB-approved study includes:
- Phase 1 (in progress): Qualitative interviews with caregivers to explore perceptions of real-time predictors of distress.
- Phase 2: Development of EMA items from validated instruments (Fatigue Assessment Scale, Pittsburgh Sleep Quality Index, Perceived Stress Scale, Brief COPE), refined with caregiver input.
- Phase 3: EMA surveys administered via smartphone over 14 days, capturing mood, anxiety, fatigue, sleep quality, burden, and coping strategies.
Participants: Adults providing daily home hospice care, English-speaking, and with mobile access. Professional caregivers or those unable/unwilling to complete EMA surveys are excluded. Target sample: 60 participants for adequate power in multilevel analyses.
Data Analysis: Directed content analysis for qualitative data. Multilevel modeling will examine within-person and temporal effects of momentary predictors on distress, with backward elimination to refine predictor models.
Significance: Using EMA and mobile technology to capture real-time caregiver experiences, this study represents an emerging technology in nursing research and practice. Findings will inform AI-guided interventions to reduce burden, enhance well-being, and promote equitable, person-centered end-of-life care, advancing digital health innovation in hospice nursing.
Notes
References included in separate attached file.
Sigma Membership
Non-member
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Qualitative Research
Keywords:
Hospice, Palliative Care, Palliative Treament, End-of-Life Care, Stress and Coping, Competence, Caregivers, Psychological Distress, Hospice Care, Terminal Care
Recommended Citation
Xu, Tuzhen and Huang, Ya-Ching, "Ecological Momentary Assessment of Caregiver Distress in Home Hospice Caregivers: A Study Protocol" (2026). International Nursing Research Congress (INRC). 14.
https://www.sigmarepository.org/inrc/2026/posters_2026/14
Conference Name
37th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Toronto, Ontario, Canada
Conference Year
2026
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record. All permission requests should be directed accordingly and not to the Sigma Repository. All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Date of Issue
2026-07-22
Funder
Prairie View A & M University
Second Funder
Texas A & M University
Ecological Momentary Assessment of Caregiver Distress in Home Hospice Caregivers: A Study Protocol
Toronto, Ontario, Canada
Background: Caregivers of hospice patients, often family or close friends, provide continuous support for individuals with a life expectancy of six months or less. In 2022, 1.72 million Medicare beneficiaries received hospice care, most at home, with an average stay of 98 days. Caregivers manage medications, symptoms, personal care, emotional support, and provider coordination, placing them at high risk for fatigue, anxiety, depression, sleep disturbance, and burnout. Traditional surveys or single-point interviews often fail to capture the dynamic nature of distress, especially during nighttime care or crises.
Purpose: To characterize real-time patterns of physical and psychological distress among home hospice caregivers and identify contextual and caregiver-level predictors to inform future AI-guided interventions promoting caregiver well-being.
Framework: Guided by the Stress Process Model and the COM-B model, the study integrates caregiving demands, health outcomes, and behavioral determinants to design and interpret Ecological Momentary Assessment (EMA) data via mobile technology.
Methods: This three-phase, funded, IRB-approved study includes:
- Phase 1 (in progress): Qualitative interviews with caregivers to explore perceptions of real-time predictors of distress.
- Phase 2: Development of EMA items from validated instruments (Fatigue Assessment Scale, Pittsburgh Sleep Quality Index, Perceived Stress Scale, Brief COPE), refined with caregiver input.
- Phase 3: EMA surveys administered via smartphone over 14 days, capturing mood, anxiety, fatigue, sleep quality, burden, and coping strategies.
Participants: Adults providing daily home hospice care, English-speaking, and with mobile access. Professional caregivers or those unable/unwilling to complete EMA surveys are excluded. Target sample: 60 participants for adequate power in multilevel analyses.
Data Analysis: Directed content analysis for qualitative data. Multilevel modeling will examine within-person and temporal effects of momentary predictors on distress, with backward elimination to refine predictor models.
Significance: Using EMA and mobile technology to capture real-time caregiver experiences, this study represents an emerging technology in nursing research and practice. Findings will inform AI-guided interventions to reduce burden, enhance well-being, and promote equitable, person-centered end-of-life care, advancing digital health innovation in hospice nursing.
Description
Focus: Patient Families
Status: Ongoing Work/Project
A funded, IRB-approved study protocol employing mobile Ecological Momentary Assessment to track real-time distress in home hospice caregivers, aiming to identify predictors for future AI-guided support interventions.