Risk Assessment Profiles for Caregiver Burden in Family Caregivers of Persons Living with Alzheimer's Disease: An Exploratory Study with Machine Learning

dc.contributor.author Beatriz Cepa en
dc.contributor.author Cláudia Vanessa Brito en
dc.contributor.other 8840 en
dc.contributor.other 7516 en
dc.date.accessioned 2025-06-20T17:32:19Z
dc.date.available 2025-06-20T17:32:19Z
dc.date.issued 2025 en
dc.description.abstract Alzheimer's disease (AD) places a profound global challenge, driven by its escalating prevalence and the multifaceted strain it places on individuals, families, and societies. Family caregivers (FCs), who are pivotal in supporting family members with AD, frequently endure substantial emotional, physical, and psychological demands. To better understand the determinants of family caregiving strain, this study employed machine learning (ML) to develop predictive models identifying factors that contribute to caregiver burden over time. Participants were evaluated across sociodemographic clinical, psychophysiological, and psychological domains at baseline (T1; N = 130), six months (T2; N = 114), and twelve months (T3; N = 92). Results revealed three distinct risk profiles, with the first focusing on T2 data, highlighting the importance of distress, forgiveness, age, and heart rate variability. The second profile integrated T1 and T2 data, emphasizing additional factors like family stress. The third profile combined T1 and T2 data with sociodemographic and clinical features, underscoring the importance of both assessment moments on distress at T2 and forgiveness at T1 and T2, as well as family stress at T1. By employing computational methods, this research uncovers nuanced patterns in caregiver burden that conventional statistical approaches might overlook. Key drivers include psychological factors (distress, forgiveness), physiological markers (heart rate variability), contextual stressors (familial dynamics, sociodemographic disparities). The insights revealed enable early identification of FCs at higher risk of burden, paving the way for personalized interventions. Such strategies are urgently needed as AD rates rise globally, underscoring the imperative to safeguard both patients and the caregivers who support them. en
dc.identifier P-018-E52 en
dc.identifier.uri https://repositorio.inesctec.pt/handle/123456789/15526
dc.language eng en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Risk Assessment Profiles for Caregiver Burden in Family Caregivers of Persons Living with Alzheimer's Disease: An Exploratory Study with Machine Learning en
dc.type en
dc.type Publication en
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