JMIR Publications

ABSTRACT

Background:

Poor sleep quality is increasingly recognized as a risk factor for cardiovascular disease and stroke. Despite the heightened vulnerability of individuals with cardiometabolic conditions, evidence on multidimensional sleep characteristics in adults at elevated stroke risk remains limited, particularly in low- and middle-income countries.

Objective:

This study aimed to characterize multidimensional sleep profiles among adults at elevated risk of stroke and examine their associations with demographic, behavioral, and cardiometabolic risk factors. The findings provide context-specific evidence from Indonesia to support the conceptual development of a Structured and Measurable Multidimensional Sleep Management Model for LMIC settings.

Methods:

This cross-sectional study included 303 adults with established stroke risk factors. Multidimensional sleep characteristics were assessed, including subjective sleep quality, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Associations between sleep characteristics, demographic factors, lifestyle behaviors, and cardiometabolic conditions were analyzed using descriptive statistics, chi-square tests, and multivariable logistic regression analysis.

Results:

Poor sleep quality was identified in 65.7% of participants, with a marked discrepancy between subjective and objective sleep measures, as most participants perceived their sleep as good despite substantial impairments in sleep efficiency and duration. Smoking, diabetes mellitus, hypercholesterolemia, hypertension, obstructive sleep apnea, and low physical activity were independently associated with cardiometabolic comorbidity, and the final model explained 66.0% of its variability. These findings demonstrate that integrating multidimensional sleep characteristics with cardiometabolic risk profiling enables the identification of heterogeneous sleep dysfunction patterns that may not be detected by subjective sleep perception alone. Accordingly, the findings provide empirical support for the conceptual development of a Structured and Measurable Multidimensional Sleep Management Model to guide individualized stroke prevention strategies.

Conclusions:

These findings demonstrate that multidimensional sleep assessment identifies substantial sleep impairment associated with cardiometabolic risk factors among adults at elevated risk of stroke, despite generally favorable subjective sleep perception. Integrating multidimensional sleep assessment with cardiometabolic risk evaluation may improve early identification of high-risk individuals and support comprehensive stroke prevention strategies, particularly in resource-limited healthcare settings. Future research should prospectively validate the proposed Structured and Measurable Multidimensional Sleep Management Model and evaluate its clinical effectiveness, implementation feasibility, and cost-effectiveness before widespread adoption in routine clinical practice.