Diurnal variation of wearable device-based heart rate variability in the Chronic Renal Insufficiency Cohort study

Little is known about the prognostic value of continuous, out-of-clinic biometric monitoring of cardiovascular function in chronic kidney disease (CKD). In this study, a mean (±SD) of 50.3 ± 9.3 h of EKG recordings from wearable BioPatch devices was collected from 458 participants across seven Chronic Renal Insufficiency Cohort centers. Multivariable linear regression showed that diabetes was associated with 7.4 ms lower Standard Deviation of NN Intervals (SDNN) compared to non-diabetic participants (p = 0.001). Higher proteinuria (uPCR ≥ 0.2) was associated with 5.73 ms lower SDNN compared to lower proteinuria (p = 0.027). This study represents the largest dataset to date evaluating SDNN, a key heart rate variability metric using wearable EKG technology in CKD. Our findings highlight that specific clinical and demographic factors significantly influence HRV in this population. These results provide a critical foundation for future work to determine whether time-specific HRV metrics can serve as predictive biomarkers for cardiovascular risk and clinical outcomes in CKD.

• Publication year

  2025

• Venue

  npj Digital Medicine

• Publication date

  2025-11-13

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1038/s41746-025-02010-5PMID 41233490PMCID 12615609
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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