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

Abstract: The outbreak of SARS-Cov-2 virus has exasperated the vulnerability of dementia spousal caregivers, as well asthose with Alzheimer's disease or related dementias. SARS-Cov-2 is a highly contagious virus that can causesevere respiratory problems and even death. Older adults and people of all ages with underlying comorbiditiesare considered to be at "high-risk" for severe illness from COVID-19. During this pandemic, dementia spousalcaregivers 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 dementiaspousal caregivers and their spouses with dementia are over sixty-five years of age, the age bracket that putspeople at most risk for COVID-19 disease complications and mortality. Social distancing guidelines make up alarge proportion of the current prevention recommendations; thus, loneliness and other negative emotions willlikely be frequent and more intense than usual. The proposed competitive revision builds upon the primary aimsof the parent grant (R01AG062690) by using attachment theory as an overarching theoretical framework tounderstand dementia spousal caregiver risk and resilience in light of coronavirus disease 2019 (COVID-19disease). 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 collectadditional data for one week each month for three months. We will collect this data using ecological momentaryassessment methods, while passively assessing location, activity, autonomic activity, and sleep via smartphoneand smartwatch technology. We aim to understand how emotions, assessed in real-time in the naturalenvironment, affect the extent to which AD spousal caregivers adaptively navigate the challenges associatedCOVID-19. We will also aim to determine how relatively stable individual difference patterns that originate frompeople's close relationship histories (i.e., attachment orientations) inform risk and resilience. As an exploratoryhigh risk/high reward aim, we will evaluate if dynamic risk prediction models and machine learning approachescan incorporate passively collected information (i.e., location, heart rate, heart rate variability, activity, sleep) withinformation that we learn from our primary aims, to yield a detailed and sophisticated understanding of real-worlddynamics that predict three critical COVID-19 specific outcomes: social distancing adherence, social distancingself-efficacy, and caregiver self-efficacy. By understanding patterns of risk and resilience, intervention scientistswill be better able to identify at-risk AD spousal caregivers. The proposed research would advance ourunderstanding of how AD spousal caregivers can reduce illness exposure for themselves and those they carefor in perhaps the most comprehensive, detailed, real-time, real-world investigation of social distancing in ADspousal caregivers to date.