Key result
Higher resting LnRMSSD links to fewer MetS components, but resting heart rate attenuates this effect.
Why the study?
The study sought to examine associations between multi-day ultra-short resting HR parameters and cardiometabolic health under free-living conditions.
Cross-Sectional (n=67)
No
Effect estimate: IRR 0.79 (95% CI 0.64-0.97)
p-value: p=0.03
Resting heart rate accounts for a substantial portion of the cross-sectional association between heart rate variability and cardiometabolic health, suggesting simple remote heart rate monitoring may be effective for early cardiometabolic risk identification.
Resting heart rate may confound HRV-metabolic syndrome links; leaves open whether HRV adds value beyond simple HR monitoring for risk assessment.
This study examined associations between multi‐day ultra‐short resting heart rate (HR) parameters and cardiometabolic health under free‐living conditions. Sixty‐seven adults (18–59 years, 53% male) completed seven consecutive days of seated 1‐min RR interval recordings after waking using a validated chest strap monitor. Log‐transformed root mean square of successive differences (LnRMSSD M ) and resting HR (HR M ) were averaged across days and standardized. Cardiometabolic health status was defined as the number of metabolic syndrome (MetS) components present. Associations were assessed using Poisson regression adjusted for age and sex, with additional models including HR M and HR‐adjusted LnRMSSD. Higher LnRMSSD M was associated with fewer MetS components (IRR = 0.79, 95% CI: 0.64–0.97), whereas higher HR M was associated with greater metabolic burden (IRR = 1.40, 95% CI: 1.10–1.77). When both variables were included, the association between LnRMSSD M and MetS was attenuated and no longer significant (IRR = 0.93, 95% CI: 0.72–1.22), while HR M remained significant (IRR = 1.33, 95% CI: 1.00–1.77). Resting HR parameters obtained through accessible devices were associated with cardiometabolic health, with resting HR accounting for much of the cross‐sectional information captured by LnRMSSD. Remote cardiac monitoring may support early cardiometabolic risk identification and longitudinal health surveillance.
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González-Fimbres et al. (2026) conducted a cross-sectional in Cardiometabolic health (Metabolic syndrome) (n=67). Higher log-transformed root mean square of successive differences (LnRMSSD) was evaluated on Number of metabolic syndrome (MetS) components present (IRR 0.79, 95% CI 0.64-0.97, p=0.03). Higher resting LnRMSSD was associated with fewer metabolic syndrome components (IRR 0.79), but this association was attenuated and no longer significant when accounting for resting heart rate.
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