Individual differences in dementia spousal caregiver burden: A biobehavioral approach revision

摘要

Abstract The outbreak of SARS-Cov-2 virus has exasperated the vulnerability of dementia spousal caregivers, as well as those with Alzheimer’s disease or related dementias. SARS-Cov-2 is a highly contagious virus that can cause severe respiratory problems and even death. Older adults and people of all ages with underlying comorbidities are considered to be at “high-risk” for severe illness from COVID-19. During this pandemic, dementia spousal caregivers are tasked with the burden of keeping their spouse safe from getting sick and even dying from COVID- 19, while simultaneously performing their typical caregiving responsibilities. The vast majority of dementia spousal caregivers and their spouses with dementia are over sixty-five years of age, the age bracket that puts people at most risk for COVID-19 disease complications and mortality. Social distancing guidelines make up a large proportion of the current prevention recommendations; thus, loneliness and other negative emotions will likely be frequent and more intense than usual. The proposed competitive revision builds upon the primary aims of the parent grant (R01AG062690) by using attachment theory as an overarching theoretical framework to understand dementia spousal caregiver risk and resilience in light of coronavirus disease 2019 (COVID-19 disease). The proposed research directly addresses several objectives from the PA-18-935, NOT-AG-20-022. Capitalizing on the dementia spousal caregivers who will take part in the parent study, we propose to collect additional data for one week each month for three months. We will collect this data using ecological momentary assessment methods, while passively assessing location, activity, autonomic activity, and sleep via smartphone and smartwatch technology. We aim to understand how emotions, assessed in real-time in the natural environment, affect the extent to which AD spousal caregivers adaptively navigate the challenges associated COVID-19. We will also aim to determine how relatively stable individual difference patterns that originate from people’s close relationship histories (i.e., attachment orientations) inform risk and resilience. As an exploratory high risk/high reward aim, we will evaluate if dynamic risk prediction models and machine learning approaches can incorporate passively collected information (i.e., location, heart rate, heart rate variability, activity, sleep) with information that we learn from our primary aims, to yield a detailed and sophisticated understanding of real-world dynamics that predict three critical COVID-19 specific outcomes: social distancing adherence, social distancing self-efficacy, and caregiver self-efficacy. By understanding patterns of risk and resilience, intervention scientists will be better able to identify at-risk AD spousal caregivers. The proposed research would advance our understanding of how AD spousal caregivers can reduce illness exposure for themselves and those they care for in perhaps the most comprehensive, detailed, real-time, real-world investigation of social distancing in AD spousal caregivers to date.