Key result
PPG-derived pulse rate variability correlates strongly with ECG-derived measurements with ~3 ms mean bias.
Why the study?
Assessing heart rate variability (HRV) via ECG presents practical challenges in large or out-of-laboratory studies, prompting evaluation of whether photoplethysmography-derived pulse rate variability (PRV) agrees with ECG-derived HRV and how age, BMI, and cardiorespiratory fitness influence HRV.
Does photoplethysmography-derived pulse rate variability accurately approximate electrocardiography-derived heart rate variability (SDNN) in healthy young adults at rest?
Cross-Sectional (n=35)
No
Does photoplethysmography-derived pulse rate variability accurately approximate electrocardiography-derived heart rate variability (SDNN) in healthy young adults at rest?
Answers are grounded in this paper and cite their sources.
Resting PPG-derived pulse rate variability strongly correlates with ECG-derived heart rate variability at the group level in healthy young adults, though wide limits of agreement preclude individual-level clinical interchangeability and require adjustment for age and cardiorespiratory fitness.
Background: Heart rate variability (HRV) provides information about autonomic regulation and has been associated with resilience to stress. However, its assessment using electrocardiography (ECG) can present practical challenges for large studies and recordings outside the laboratory. Pulse rate variability (PRV), derived from photoplethysmography (PPG), and may provide a more accessible alternative. Objective: This study evaluated agreement between ECG-derived HRV and PPG-derived PRV and examined age, body mass index (BMI), and cardiorespiratory fitness as determinants of HRV. Methods: Simultaneous ECG and finger PPG recordings were obtained from 35 healthy adults (20 females and 15 males; mean age 23.1 years; mean BMI 27.5 kg/m²) under standardised resting conditions. Agreement for the standard deviation of normal-to-normal intervals (SDNN) was evaluated using correlation and Bland-Altman analysis. Multiple linear regression assessed age, BMI, and peak oxygen consumption (VO₂peak) as predictors of ECG-derived SDNN. Results: PRV correlated strongly with HRV for SDNN (r = 0.94, p < 0.001). The mean ECG-minus-PPG bias was 3.30 ms, with 95% limits of agreement from -9.48 to 16.08 ms. Age was negatively associated with SDNN (B = -2.14 ms/year, p < 0.001), whereas VO₂peak was positively associated (B = 1.76 ms per ml/kg/min, p = 0.005). BMI was not independently associated after adjustment. The regression model explained 52% of SDNN variance. Conclusion: Resting PRV provides an accessible approximation of HRV for group-level research in healthy young adults. Its use may facilitate wearable-based investigation of autonomic responses in psychological and political settings. Age and fitness should be measured and modelled when interpreting HRV or PRV, while individual-level interchangeability and broader population validity require further study.
No takes yet. Share an insight, caveat, or question.
Bhimani et al. (2026) conducted a cross-sectional in Healthy adults (autonomic regulation / stress regulation) (n=35). Photoplethysmography (PPG)-derived pulse rate variability (PRV) vs. Electrocardiography (ECG)-derived heart rate variability (HRV) was evaluated on Standard deviation of normal-to-normal intervals (SDNN, ms) method agreement and correlation (r = 0.94; mean bias 3.30 ms (95% LoA -9.48 to 16.08 ms), p=<0.001). PPG-derived pulse rate variability strongly correlated with ECG-derived heart rate variability for SDNN at rest (r = 0.94, p < 0.001) with a mean ECG-minus-PPG bias of 3.30 ms.
Synapse has enriched 10 related papers. Consider them for comparative context: