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Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position
Marlene Rietz1,2, Jesper Schmidt-Persson1,3, Martin Gillies Banke Rasmussen1,4
1Center for Research in Childhood Health, Research Unit for Exercise Epidemiology, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense M, Denmark.
Free-living heart rate variability (HRV) analysis revealed significant differences across body positions and sleep states. This open-source method helps monitor autonomic stress by accounting for physical activity.
Area of Science:
- Physiology and Autonomic Nervous System Research
- Biomarker Analysis in Free-Living Conditions
- Wearable Sensor Data Interpretation
Background:
- Heart rate variability (HRV) is a key biomarker for autonomic nervous system activity.
- Ambulatory HRV analysis is complex due to influences of physical behavior, stress, and sleep.
- Open-source methodologies offer accessible tools for complex physiological data analysis.
Purpose of the Study:
- To investigate differences in HRV based on accelerometer-derived body positions and self-reported sleep.
- To compare different summary measures of HRV, including 24-hour (24h) and sleep-specific estimates.
- To validate an open-source methodology for free-living HRV analysis in adults.
Main Methods:
- Collected beat-to-beat heart rate (HR) and accelerometry data from 160 adults in the SCREENS trial.
- Processed and analyzed HR data using the RHRV R package, extracting HRV from accelerometer-defined physical behavior episodes.
- Employed linear mixed models and repeated-measures Bland-Altman analysis to compare HRV estimates across conditions and measures.
Main Results:
- Significant differences in HR and HRV markers were observed across body positions (Sitting, Standing, Lying).
- Ambulatory HRV varied significantly based on sleep status, with poor agreement between 24h and sleep HRV estimates.
- Sensitivity analyses confirmed that excluding initial/final 30s of HR episodes accurately accounted for orthostatic effects.
Conclusions:
- Free-living ambulatory HRV is significantly influenced by body position and sleep status.
- The proposed open-source approach effectively differentiates HRV across physical behaviors and sleep.
- This methodology can serve as a valuable tool for monitoring general autonomic stress, mitigating physical activity confounding.
