Quality of life among older adults with atrial fibrillation was influenced by multiple interrelated factors, with social support, psychological status, and economic circumstances exerting significant combined effects through both direct and indirect pathways.
Key Findings
Methods
Older adults with atrial fibrillation were categorized into high-quality and low-quality of life groups based on AFEQT scores, with more patients in the high-quality group.
A convenience sample of 252 older adults with AF admitted to hospital between August 2023 and August 2024 was included.
Patients were categorized into high-quality (n = 158) and low-quality (n = 94) groups according to their AFEQT scores.
Data were collected using the AFEQT questionnaire, Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Social Support Rating Scale (SSRS).
Results
Eight independent determinants of quality of life in older adults with AF were identified through univariate analysis and multivariate logistic regression.
Independent determinants included age, monthly income, AF type, New York Heart Association functional classification, treatment adherence, SDS score, SSRS score, and presence of comorbid chronic conditions.
All identified determinants reached statistical significance at p < 0.05.
These determinants were identified through a two-step process of univariate analysis followed by multivariate logistic regression.
Results
Structural equation modeling demonstrated that five variables exerted both direct and indirect effects on quality of life, with SSRS score having the largest total effect.
Age, monthly income, AF type, SDS score, and SSRS score each exerted both direct and indirect effects on quality of life.
Direct effect path coefficients were: age = 0.200, monthly income = -0.131, AF type = 0.134, SDS score = 0.160, and SSRS score = -0.207.
Total effect coefficients were: age = 0.316, monthly income = -0.168, AF type = 0.188, SDS score = 0.225, and SSRS score = -0.347.
SSRS score had the largest total effect coefficient in magnitude (-0.347), followed by SDS score (0.225) and age (0.316).
Results
Social support, psychological status, and economic circumstances exerted significant combined effects on quality of life through both direct and indirect pathways.
SSRS score (social support) had the largest total effect on quality of life among all variables examined (total effect coefficient = -0.347).
SDS score (depression) had a total effect coefficient of 0.225 on quality of life.
Monthly income had a total effect coefficient of -0.168 on quality of life.
These variables influenced quality of life through mediating variables in addition to their direct effects.
Results
The SEM model identified mediating pathways through which demographic and clinical variables indirectly affected quality of life.
Indirect effects were observed for age (total effect 0.316 vs. direct effect 0.200, indicating an indirect component of 0.116), monthly income (total -0.168 vs. direct -0.131), AF type (total 0.188 vs. direct 0.134), SDS score (total 0.225 vs. direct 0.160), and SSRS score (total -0.347 vs. direct -0.207).
The indirect effects were assessed through mediating variables identified within the SEM framework.
SEM was used to assess interrelationships among variables beyond what logistic regression could capture.
Qiao C, Xu Y, Jiang R, Zhang M, Huang B, Ding L. (2026). Determinants of quality of life in older adults with atrial fibrillation: a structural equation modeling analysis.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1753021