Wearable technologies enable continuous, non-invasive monitoring of cardiovascular health, promising for tracking dynamic responses during exercise. Photoplethysmography (PPG) is a simple and cost-effective sensing modality, but its accuracy during movement remains a challenge. This study investigates motion-induced hemodynamic effects on in-ear PPG pulse wave morphology during heart-paced walking. We hypothesize that motion artifacts partly arise from physiological effects induced by body motion, such as blood inertia and wave reflections. The 12 healthy participants (6 female, 28±2 years) walked on a treadmill, guided by auditory signals to synchronize their steps to either systole (R-wave) or diastole (45% RR interval) of the cardiac cycle, while recording in-ear PPG, electrocardiogram and chest acceleration. PPG pulse wave amplitude and morphology varied significantly during heart-paced walking. Diastolic stepping resulted in a consistent morphology downward deflection after 50% RR interval, while systolic stepping yielded a 0.06 V higher peak-to-peak amplitude (Cohen’s d=1.08). Our findings highlight the potential of in-ear PPG to capture physiological changes during dynamic conditions and raises questions about pulse wave morphology in the periphery during walking. Further studies are needed on extracting motion-induced hemodynamics in less controlled conditions.

Background: The impact of aortic stiffness in the development of coronary microvascular dysfunction in chronic coronary syndrome (CCS) remains unknown. Invasive aortic pressure curves, routinely recorded during coronary angiography, provide an opportunity to assess aortic stiffness through pulse wave analysis and relate to measures of coronary microvascular dysfunction.

Purpose: To investigate the relationship between invasive aortic pulse wave features – such as pulse pressure, augmentation index, and reflection time – and (i) thermodilution-derived indices of coronary flow and (ii) major adverse cardiovascular events (MACE) in patients with CCS.

Methods: CCS-patients were prospectively included the day before coronary angiography. Coronary flow was assessed in the LAD using the thermodilution technique during coronary angiography at rest and during adenosine infusion. Index of microcirculatory resistance (IMR), baseline resistance index (BRI), hyperemic flow velocity (HFV), resting flow velocity (RFV), and microvascular resistance reserve (MRR) were recorded. Aortic pressure waveforms recorded during coronary angiography were analysed using a dedicated software. Pulse pressure, augmentation index, and reflection time (time interval from waveform onset to the reflected wave) were determined. Follow-up was conducted through the population registry, telephone calls and review of medical records. Pulse wave metrics were analysed in relation to indices of microvascular function with linear regression. Skewed variables were log-transformed. Cox regression analyses were used for pulse wave metrics in relation to MACE, defined as death, myocardial infarction or hospitalization due to heart failure.

Results: Three-hundred and eighty-six patients, with median age 68 years (IQR 59-74), including 95 (25%) women, were included in the analysis. Median follow-up was 5.4 years (IQR 3.2–6.6), during which 52 patients experienced a MACE. Pulse pressure was associated with RFV and inversely associated with BRI, and MRR but not with IMR or HFV (Figure 1). Augmentation index was associated with HFV (β = 10 [95% CI 1.6–19]) and RFV (β = 11 [95% CI 1.3 – 20.0]). Reflection time was inversely associated with HFV (β = -0.014 [95% CI -0.026 – -0.003]) and RFV (β = -0.018 [95% CI -0.030 – -0.005]). Pulse pressure was associated with MACE (HR 1.03 [95% CI 1.01 – 1.04]; Figure 2) before but not after adjustment for age and gender (HR 1.01 [95% CI 1-1.03). Augmentation index and reflection time were not associated with MACE (Figure 2).

Conclusions: Pulse pressure derived from invasive aortic pulse wave is associated with higher coronary microcirculatory resting flow velocity, lower microcirculatory coronary resting resistance and lower non-endothelial dependent vasodilatory capacity in the LAD. Furthermore, in patients with CCS, invasive central pulse pressure is associated with MACE in crude analysis but not after adjustments for age and gender.  

Introduction: During exercise, the cardiovascular, respiratory, and locomotor systems interplay dynamically, yet the specific mechanisms of cardiovascular and locomotor interaction during simple rhythmic exercise like walking remain unclear. Computational models constitute a powerful tool to investigate the interplay of networked physiological systems, but while gravitational and postural effects on circulation have been explored, the influence of inertial forces from body motion on hemodynamics has not been addressed. Methods: Here, we present a closed-loop cardiovascular model that incorporates inertial effects during walking. The lumped parameter model includes 25 vascular compartments, a four-chamber heart with valves, pericardial and intrathoracic pressures, interventricular septal dynamics, and a baroreflex mechanism. Inertial effects are modeled as additional hydrodynamic pressure sources in each vascular segment, equivalent to the acceleration of blood mass, caused by gravity and motion. Three protocols are used: a head-up tilt test to validate baroreflex and gravity effects; a synthetic walking simulation with controlled heart rate (HR) and step rate (SR); and a human walking experiment (n=2) linking beat-wise simulated aortic pressure to measured brachial pressure using recorded HR and body acceleration. Beat-wise morphology similarity (K-stat) between experimental and simulated hemodynamic waveforms is quantified with a two-sample Kolmogorov-Smirnov test. Results: The model reproduces expected physiological responses to head-up tilt. During synthetic walking, inertial effects result in pressure augmentation, increasing systolic or diastolic pressure depending on the phase between HR and SR. With SR > HR, phase variability produces a low-frequency “beating” in the pressure waveforms and mean arterial pressure, corresponding to the difference between SR and HR. In the human subject experiment, the model accurately replicates beat-wise pressure changes at varying phase shifts between HR and SR. Quantitative comparison shows a substantial increase in similarity of waveform when hydrodynamic pressure is included (K-stat: 0.123 vs. 0.029 for P1; 0.164 vs. 0.059 for P2). Conclusion: Introducing contributions of body acceleration as an additional hydrodynamic pressure source in the vascular compartments seems a valid way to capture walking-induced inertial effects. This work contributes to the broader effort to characterize physiological network adaptations to exercise and offers a foundation for future research studying and optimizing cardiac-locomotor interaction.

 Introduction: One way to improve exercise performance and protect heart health is the extended synchronization of the stepping with the diastolic phase of the cardiac cycle. Cardiac-locomotor coupling (CLC) happens when the step rate (SR) equals the heart rate (HR). The extent of CLC in daily life is unknown. This study aims to analyze spontaneous occurrences of CLC during daily activities.

Methods: A retrospective analysis of daily life recordings from a wrist-worn sensor was undertaken (PMData, N = 16, 5 months duration). The deviation between HR and SR was used to define CLC (deviation ≤ 1%) and weak CLC (1%< deviation ≤ 10%). The occurrence and the probability of CLC during everyday life were computed from the recordings. The CLC occurrences were stratified depending on the duration and intensity of the physical activity. Finally, a Monte Carlo simulation was run to evaluate the probability of random occurrences of CLC vs. the observed recordings.

Results: Participants couple for 5% and weakly couple for 35% of the observational period. The ratio of 1:1 between HR and SR is the dominating occurrence across the study population and this overrepresentation is significant. CLC occurs mostly for long activities. The extent of CLC for various intensities of activity is subject-dependent. The results suggest that CLC is feasible for most people.

Conclusions: CLC occurs spontaneously during unsupervised daily activity in everyone in our cohort, which suggests a mechanistic interaction between the cardiac and the locomotor systems. This interaction should be investigated for medical rehabilitation and sports applications in the future.

 One way to improve hemodynamic efficiency is the synchronization of the stepping with the diastolic phase of the cardiac cycle (diastolic stepping) minimizing peak pressures on the heart level. At head level, we expect maximized blood flow in diastolic stepping and minimized blood flow in systolic stepping. This study aims to verify suggested blood flow patterns via analysis of the pulse wave (PW) amplitudes in the ear during synchronized walking. Four participants (2 men, 2 women, 27 ± 3 years) walked on a treadmill at a comfortable speed, guided by auditory signals (Pulson, USA) to perform diastolic and systolic stepping. PW amplitudes were continuously measured in the ear using an optosensor (TCRT1000,Vishay, USA) and compared to heart rate (HR) from an ECG chest strap (Movesense, Finland). Results showed that the PW amplitude increased by 20 ± 16% across all subjects during diastolic stepping compared to systolic stepping, with lower HRs detected from the optosensor (0.73 ± 1.4 beats/min) than from the ECG. The findings suggest that synchronized walking modulates PW amplitude during diastolic stepping with different extent across subjects.

Human locomotion is typically studied independently ofthe cardiovascular system, although the two areintrinsically linked. An example of their interaction iscardiac-locomotor coupling (CLC), which refers to theentrainment of heart rate (HR) and step rate (SR) duringrhythmic exercise at a 1:1 ratio. Actively synchronizingthe stepping with each diastole of the cardiac cycle, hasbeen shown to provide improved cardiometabolicefficiency, demonstrated by a decrease in HR and inventilation [1]. Active CLC can therefore be one way toimprove hemodynamic efficiency, ensuring greaterendurance performances. Previous studies investigatedthe presence of CLC in laboratory settings whereasobservations during daily activities are still lacking. Wedo not know if and when CLC occurs physiologically.The aim of this study is to investigate the extent and theprobability of CLC during unsupervised daily activitiesin contrast to random chance.

During exercise, the locomotor and the cardiovascular system work in synergy to control the blood flow through the body. In particular, the muscle contraction generates rhythmic raising and lowering of intramuscular pressure, which in synergy supports cardiovascular function. This study aims to analyze spontaneous cardiac-locomotor coupling (CLC) events during daily activities using weareable sensors.

We analyze the dataset PMData, containing recordings from sixteen healthy subjects during five months. The data were acquired with a smartwatch and consist of step rate (SR), heart rate (HR) and daily surveys reporting the training sessions. Coupling is defined as being present when SR and HR are within 1% of each other (strong coupling) and within the 10% of each other (weak coupling).

The results show that every subject presents occurrences of CLC while performing normal daily activities. In particular, strong coupling occurs more likely for longer activities (111 ± 34 min), at moderate intensity (100 steps/min < SR > 130 steps/min).

The presence of CLC during daily activities rises the question whether there is a physiological mechanism controlling this phenomenon, that should be investigated in future.