BackgroundSubstantial evidence suggests an association between obesity and sleep. However, research investigating sleep patterns in relation to novel anthropometric indices is limited. Therefore, we conducted a cross‐sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014 to examine the relationship between the body roundness index (BRI) and unhealthy sleep patterns.ObjectiveThis study aimed to investigate the association between the BRI and unhealthy sleep patterns among US adults.MethodsData were sourced from NHANES (2007–2014), including respondents aged 20 years and older. Participants were categorized into two groups based on the healthiness of their sleep patterns. The data were weighted, and multiple potential covariates were included in the analysis to provide national estimates and account for the comprehensive sampling design. A multivariable weighted logistic regression model was used, employing restricted cubic spline (RCS) curves to examine potential associations, and subgroup analyses were conducted to determine the stability of the results. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic performance of BRI and body mass index (BMI) in identifying unhealthy sleep patterns.ResultsIn the fully adjusted multivariable logistic regression model, the prevalence odds ratio (POR) for the association between BRI and unhealthy sleep patterns was 1.09, with a 95% confidence interval (CI) of 1.07–1.10. The RCS analysis found that the nonlinear association between BRI and unhealthy sleep patterns was not significant. Subgroup and sensitivity analyses indicated a consistently positive association between high BRI and unhealthy sleep patterns across most subgroups. ROC diagnostic tests showed that BRI's effectiveness in diagnosing unhealthy sleep patterns was comparable to that of BMI, and it was not inferior to BMI in assessing certain components of sleep patterns.ConclusionHigh BRI is positively associated with unhealthy sleep patterns significantly, indicating that BRI could be a promising metric for evaluating sleep health.