Time-specific associations of wearable sensor-based cardiovascular and behavioral readouts with disease phenotypes in the outpatient setting of the Chronic Renal Insufficiency Cohort. (June 2022)

• Record Type: Journal Article
• Authors: Lahens, Nicholas F.; Rahman, Mahboob; Cohen, Jordana B.; Cohen, Debbie L.; Chen, Jing; Weir, Matthew R.; Feldman, Harold I.; Grant, Gregory R.; Townsend, Raymond R.; Skarke, Carsten; Study Investigators*, and the CRIC
• Is Part Of: Digital health. Volume 8(2022)
• Publication Date: 2022-06
• Subjects: Heart rate variability -- electrokardiogram -- chronic kidney disease -- diabetes -- wearable devices -- remote sensing -- telemedicine -- circadian phase -- diurnal variability -- outside of the hospital environment; Medical care -- Data processing -- Periodicals; Medical informatics -- Periodicals; 362.10285
• DOI: 10.1177/20552076221107903
• Languages: English
• ISSNs: 2055-2076

Abstract

Patients with chronic kidney disease are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with chronic kidney disease from the Chronic Renal Insufficiency Cohort and controls (n = 49). Time-specific partitioning of heart rate variability readouts confirm higher parasympathetic nervous activity during the night (mean RR at night 14.4 ± 1.9 ms vs. 12.8 ± 2.1 ms during active hours; n = 47, analysis of variance (ANOVA) q = 0.001). The α2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and nondiabetic patients (prominent at night with 0.58 ± 0.2 vs. 0.45 ± 0.12, respectively, adj. p = 0.004). Both diabetic and nondiabetic chronic kidney disease patients showed loss of rhythmic organization compared to controls, with diabetic chronic kidney disease patients exhibiting deconsolidation of peak phases between their activity and standard deviation of interbeat intervals rhythms (mean phase difference chronic kidney disease 8.3 h, chronic kidney disease/type 2 diabetes mellitus 4 h, controls 6.8 h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.