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

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.

Keywords: Heart rate variability; chronic kidney disease; circadian phase; diabetes; diurnal variability; electrokardiogram; outside of the hospital environment; remote sensing; telemedicine; wearable devices.

Figure 1.

Diurnal phenotypes on cohort and patient level. Top: Boxplots of BioPatch data streams are stratified by cohort diabetic (left) and normoglycemic (center) patient with CKD compared to healthy controls (right) as well as by day (orange) and night (green) for the following readouts: Activity (g), peak acceleration (g), heart rate (HR, bpm), SDNN (standard deviation of normal-to-normal RR intervals is calculated as rolling heart rate variability (HRV) value in ms), breathing rate (BR, bpm), and posture (degree where values toward 0 indicate vertical posture and negative values indicate prone or supine torso positions). Center: Boxplots of EKG waveform data streams are analyzed by Kubios. HR (bpm) and interbeat intervals (RR, ms) are stratified by cohort as well as day and night. Bottom: Time-of-day dependent modulation, or absence thereof, of interbeat intervals (RR, ms) at participant level for a diabetic (left) and normoglycemic (center) patient with CKD compared to healthy control (right). Gray rectangular indicates the first and second night (22:00–06:00). CKD, chronic kidney disease.

Figure 2.

α2 long-term fluctuations in detrended fluctuation analysis (α2-DFA). Boxplot with mean values for the Kubios heart rate variability (HRV) readout, α2-DFA, are shown for all participants (black dots) as well as for CKD/T2DM patients (green dots), CKD patients (orange dots), and healthy volunteers (blue dots) including day versus night differences. CKD, chronic kidney disease; T2DM, type 2 diabetes mellitus.

Figure 3.

Cosinor metrics of BioPatch data streams The rhythm-adjusted mean, MESOR, (Left) and amplitude (Center) for activity (g), breathing (bpm) and heart (bpm) rates, and BioPatch SDNN (standard deviation of normal-to-normal RR, ms) as heart rate variability (HRV) readout are stratified by cohort, i.e. diabetic (left) and normoglycemic (center) patient with CKD compared to healthy controls (right). Right: The time-of-day when physiological readouts peak, acrophase, is shown for activity (g), breathing (bpm) and heart (bpm) rates, and BioPatch SDNN (ms) for controls (top, blue), diabetic (center, green), and normoglycemic (bottom, orange) patient with CKD. CKD, chronic kidney disease.