Effects of missing data on heart rate variability measured from a smartwatch

Abstract

BACKGROUND: Measuring heart rate variability (HRV) through wearable photoplethysmography sensors from smartwatches is gaining popularity for monitoring many health conditions. However, missing data caused by insufficient wear compliance or signal quality can degrade the performance of health metrics or algorithm calculations. Research is needed on how to best account for missing data and to assess the accuracy of metrics derived from photoplethysmography sensors.

OBJECTIVE: This study aimed to evaluate the influence of missing data on HRV metrics collected from smartwatches both at rest and during activity in real-world settings and to evaluate HRV agreement and consistency between wearable photoplethysmography and gold-standard wearable electrocardiogram (ECG) sensors in real-world settings.

METHODS: Healthy participants were outfitted with a smartwatch with a photoplethysmography sensor that collected high-resolution interbeat interval (IBI) data to wear continuously (day and night) for up to 6 months. New datasets were created with various amounts of missing data and then compared with the original (reference) datasets. 5-minute windows of each HRV metric (median IBI, SD of IBI values [STDRR], root-mean-square of the difference in successive IBI values [RMSDRR], low-frequency [LF] power, high-frequency [HF] power, and the ratio of LF to HF power [LF/HF]) were compared between the reference and the missing datasets (10%, 20%, 35%, and 60% missing data). HRV metrics calculated from the photoplethysmography sensor were compared with HRV metrics calculated from a chest-worn ECG sensor.

RESULTS: At rest, median IBI remained stable until at least 60% of data degradation (P=.24), STDRR remained stable until at least 35% of data degradation (P=.02), and RMSDRR remained stable until at least 35% data degradation (P=.001). During the activity, STDRR remained stable until 20% data degradation (P=.02) while median IBI (P=.01) and RMSDRR P<.001) were unstable at 10% data degradation. LF (rest: P<.001; activity: P<.001), HF (rest: P<.001, activity: P<.001), and LF/HF (rest: P<.001, activity: P<.001) were unstable at 10% data degradation during rest and activity. Median IBI values calculated from photoplethysmography sensors had a moderate agreement (intraclass correlation coefficient [ICC]=0.585) and consistency (ICC=0.589) and LF had moderate consistency (ICC=0.545) with ECG sensors. Other HRV metrics demonstrated poor agreement (ICC=0.071-0.472).

CONCLUSIONS: This study describes a methodology for the extraction of HRV metrics from photoplethysmography sensor data that resulted in stable and valid metrics while using the least amount of available data. While smartwatches containing photoplethysmography sensors are valuable for remote monitoring of patients, future work is needed to identify best practices for using these sensors to evaluate HRV in medical settings.