In the realm of cardiovascular health research, smoking remains a well-documented yet persistently complex risk factor. A groundbreaking study recently published in BioMedical Engineering OnLine delves into the subtle but significant impacts smoking has on blood pressure (BP), leveraging the advanced capabilities of photoplethysmography (PPG) alongside electrocardiogram (ECG) data. This innovative approach not only elucidates the physiological alterations induced by different smoking habits but also offers a new horizon for non-invasive monitoring and predictive analytics using machine learning.
The study systematically explores the effects of three prevalent smoking modalities: traditional normal cigarettes (NC), electronic cigarettes (EC), and shisha (SH). By capturing ECG and PPG signals during meticulously segmented smoking phases—before, during, and after smoking—the researchers identified distinctive fluctuations in cardiovascular parameters that are critical for BP regulation. This temporal segmentation allowed the team to map the dynamic physiological responses to smoking with unprecedented granularity.
Photoplethysmography, a technique that measures blood volume changes in the microvascular bed of tissue, proved central to detecting subtle cardiovascular shifts. The study highlights how smoking impacts various morphological and statistical features derived from PPG waveforms, such as systolic time intervals and peak pulse interval variability. These features are known to be intimately linked with arterial stiffness and vascular tone, both of which are influenced by nicotine and other smoking-related compounds.
Heart rate variability (HRV), an established indicator of autonomic nervous system function, was also markedly affected across all smoking types and phases. Smokers exhibited increased heart rates during smoking sessions, consistent with the sympathomimetic effects of nicotine. Notably, PPG-derived indices like the augmentation index, which reflects arterial wave reflections and overall arterial stiffness, showed significant elevation post-smoking, suggesting acute vascular stress.
One of the study’s most compelling contributions lies in its application of machine learning (ML) models to predict systolic blood pressure (SBP) and diastolic blood pressure (DBP) based solely on ECG and PPG data. The ML algorithms demonstrated exceptional precision, achieving mean errors close to negligible values and remarkably low root mean square errors (RMSE) for both systolic and diastolic estimations. These findings signal a pivotal step toward real-time, non-invasive blood pressure monitoring technologies, particularly relevant for smokers who are at increased cardiovascular risk.
The implications of these measurements stretch beyond mere blood pressure variation. The observed alterations in PPG characteristics reflect deeper mechanistic pathways through which smoking influences vascular health. Increased arterial stiffness, endothelial dysfunction, and disturbed autonomic balance are all pathophysiological processes entwined with the patterns detected in this study. Such insights pave the way for refined diagnostics and targeted interventions.
Comparative analyses revealed that while all smoking forms introduced measurable changes in cardiovascular parameters, the magnitude and nature of these changes varied. Traditional cigarettes exerted the most profound immediate impact on PPG features, whereas electronic cigarettes and shisha, although not benign, induced somewhat distinct patterns. This nuance reinforces the necessity to consider smoking modalities individually when assessing cardiovascular risk and tailoring cessation programs.
Moreover, the temporal dimension observed—the “before,” “during,” and “after” smoking phases—shed light on the acute and short-term vascular effects of smoking. Notably, certain PPG indices remained elevated even after smoking cessation within the monitored interval, highlighting the potential for lingering cardiovascular stress following smoking episodes. These findings underscore the dynamic and possibly cumulative burden smoking places on vascular function.
In the broader context of public health, this study exemplifies how emerging technologies can bridge the gap between real-world behavior (smoking) and clinical biomarkers (blood pressure, heart rate variability). By integrating sensor data with machine learning analytics, healthcare professionals might soon access powerful tools for early detection of smoking-induced cardiovascular anomalies, enabling proactive management and treatment.
The research also demands a reevaluation of commonly held perceptions regarding “safer” smoking alternatives like electronic cigarettes and shisha. While marketed as less harmful, their demonstrated impacts on BP-related PPG signals indicate that they too warrant scrutiny under cardiovascular health metrics. This more comprehensive evaluation strengthens regulatory and educational efforts to mitigate smoking-related health burdens.
Overall, the study’s approach sets a precedent for future investigations that could expand to larger populations and longer monitoring periods, potentially incorporating wearable technology for continuous cardiovascular assessment. By deciphering the intricate links between smoking and vital physiological parameters, the groundwork is laid for personalized medicine approaches that could dramatically alter smoking cessation and cardiovascular disease prevention strategies.
In conclusion, this pioneering work not only reconfirms the damaging cardiovascular influence of smoking across multiple modalities but also leverages photoplethysmography and machine learning to offer innovative pathways for monitoring and understanding these effects. As smoking continues to challenge global health systems, such technological integration promises a nuanced, data-driven approach to mitigating smoking’s vascular consequences.
Subject of Research: Correlation between smoking habits and blood pressure variations analyzed through photoplethysmography and electrocardiogram signals.
Article Title: Investigating the correlation between smoking and blood pressure via photoplethysmography
Article References:
Qananwah, Q., Quran, H., Dagamseh, A. et al. Investigating the correlation between smoking and blood pressure via photoplethysmography.
BioMed Eng OnLine 24, 57 (2025). https://doi.org/10.1186/s12938-025-01373-w
Image Credits: AI Generated
