Exploration of subgroups and associated factors of the uncertainty in illness among older adults with chronic heart failure: A latent profile analysis

Background: Uncertainty in illness is regarded as a source of stress, and tends to have adverse consequences on quality of life among older adults with chronic heart failure (CHF). Objective: The purpose of this study was to identify distinct subgroups in uncertainty in illness, and to explore associated factors within the population of older adults with CHF. Methods: We conducted a cross-sectional study using convenience sampling to survey 311 hospitalized older adults with CHF. The Mishel Uncertainty in Illness Scale, the 14-item Fatigue Scale, and the Perceived Social Support Scale were administered through self-reported questionnaires. Statistical analyses were conducted using latent profile analysis to precisely categorize participants based on the variable of uncertainty in illness, and multinomial logistic regression was applied to identify factors associated with subgroup heterogeneity. Results: Participants were classified as four subgroups: overall low uncertainty group (27.3%), moderate uncertainty-inconsistency fluctuations group (43.7%), moderate uncertainty-strong inconsistency group (21.2%) and overall high uncertainty group (7.8%). In comparison to the first subgroup (the overall low uncertainty group), the marital status, educational background, monthly household income, number of comorbidities, fatigue level, and social support were associated with heterogeneity of uncertainty in illness among participants in other three subgroups. Conclusion: The findings identified four distinct subgroups characterized by uncertainty in illness among older adults with CHF and revealed the demographic and clinical factors associated with each subgroup. Healthcare professionals should prioritize precise assessments and consider developing tailored interventions to reduce chronic uncertainty in illness among older patients. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.